├── fishtaco ├── __init__.py ├── examples │ ├── output │ │ ├── fishtaco_out_no_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_shapley_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_multi_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_FDR_correction_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_filtering_by_function_list_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_predict_function_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_prior_based_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_de_novo_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ ├── fishtaco_out_no_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab │ │ ├── fishtaco_out_no_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ │ └── fishtaco_out_prior_based_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab │ ├── SAMPLE_vs_CLASS.tab │ └── PATHWAY_vs_SAMPLE_MUSiCC.tab ├── learn_non_neg_elastic_net_with_prior.py ├── compute_pathway_abundance.py └── compute_differential_abundance.py ├── AUTHORS.rst ├── MANIFEST.in ├── README.rst ├── .gitignore~ ├── setup.py~ ├── .gitignore ├── setup.py ├── HISTORY.rst ├── LICENSE ├── scripts └── run_fishtaco.py └── MANIFEST /fishtaco/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 10.292623711091466 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 10.292623711091466 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 12.609520212918492 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 11.9847141505256 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 10.292623711091466 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 11.9847141505256 3 | -------------------------------------------------------------------------------- /AUTHORS.rst: -------------------------------------------------------------------------------- 1 | 2 | ======= 3 | Authors 4 | ======= 5 | 6 | FishTaco is written and maintained by Ohad Manor and the Borenstein group in University of Washington. 7 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 10.292623711091466 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 10.292623711091466 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 12.609520212918492 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 11.9847141505256 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 10.292623711091466 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO wilcoxon 2 | K00001 12.609520212918492 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO Global_Test_RSQR Global_Test_Pearson Global_Test_Spearman 2 | K00001 -0.7461037298818234 0.739370169173309 0.765998743297379 3 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include *.py 2 | include *.rst 3 | include LICENSE 4 | 5 | graft fishtaco 6 | graft scripts 7 | graft tests 8 | 9 | global-exclude *.pyc 10 | global-exclude *.pyo 11 | global-exclude .git 12 | global-exclude *~ 13 | 14 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO Global_Test_RSQR Global_Test_Pearson Global_Test_Spearman 2 | K00001 0.6889294810572076 0.8300459189356245 0.8550818222684725 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO Global_Test_RSQR Global_Test_Pearson Global_Test_Spearman 2 | K00001 0.6889294810572076 0.8300459189356245 0.8550818222684725 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO Global_Test_RSQR Global_Test_Pearson Global_Test_Spearman 2 | K00001 -0.7461037298818234 0.739370169173309 0.765998743297379 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO Global_Test_RSQR Global_Test_Pearson Global_Test_Spearman 2 | K00001 0.6889294810572076 0.8300459189356245 0.8550818222684725 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO Global_Test_RSQR Global_Test_Pearson Global_Test_Spearman 2 | K00001 -0.7461037298818234 0.739370169173309 0.765998743297379 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO R^2 PearsonCorr PearsonPval SpearmanCorr SpearmanPval MeanAbsDiff StdAbsDiff 2 | K00001 -0.7461037298818234 0.739370169173309 4.1749399312295925e-38 0.765998743297379 2.310132796969823e-42 0.32754912642848644 0.19142984012191394 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO R^2 PearsonCorr PearsonPval SpearmanCorr SpearmanPval MeanAbsDiff StdAbsDiff 2 | K00001 0.6889294810572076 0.8300459189356245 2.0369936369396658e-55 0.8550818222684725 4.142820804420943e-62 0.11635132941297402 0.11001978663655498 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO R^2 PearsonCorr PearsonPval SpearmanCorr SpearmanPval MeanAbsDiff StdAbsDiff 2 | K00001 0.6889294810572076 0.8300459189356245 2.0369936369396658e-55 0.8550818222684725 4.142820804420943e-62 0.11635132941297402 0.11001978663655498 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO R^2 PearsonCorr PearsonPval SpearmanCorr SpearmanPval MeanAbsDiff StdAbsDiff 2 | K00001 -0.7461037298818234 0.739370169173309 4.1749399312295925e-38 0.765998743297379 2.310132796969823e-42 0.32754912642848644 0.19142984012191394 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO R^2 PearsonCorr PearsonPval SpearmanCorr SpearmanPval MeanAbsDiff StdAbsDiff 2 | K00001 0.6889294810572076 0.8300459189356245 2.0369936369396658e-55 0.8550818222684725 4.142820804420943e-62 0.11635132941297402 0.11001978663655498 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO R^2 PearsonCorr PearsonPval SpearmanCorr SpearmanPval MeanAbsDiff StdAbsDiff 2 | K00001 -0.7461037298818234 0.739370169173309 4.1749399312295925e-38 0.765998743297379 2.310132796969823e-42 0.32754912642848644 0.19142984012191394 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | K00001 2 | s__Actinomyces_odontolyticus 0.0 3 | s__Campylobacter_concisus 0.0 4 | s__Haemophilus_parainfluenzae 0.0 5 | s__Prevotella_melaninogenica 0.0 6 | s__Rothia_mucilaginosa 0.0 7 | s__Streptococcus_mitis 1.0 8 | s__Streptococcus_sanguinis 1.0 9 | s__Veillonella_atypica 0.0 10 | s__Veillonella_dispar 0.0 11 | s__Veillonella_unclassified 0.0 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.0 3 | s__Campylobacter_concisus 0.0 4 | s__Haemophilus_parainfluenzae 0.0 5 | s__Prevotella_melaninogenica 0.0 6 | s__Rothia_mucilaginosa 0.0 7 | s__Streptococcus_mitis 1.0 8 | s__Streptococcus_sanguinis 1.0 9 | s__Veillonella_atypica 0.0 10 | s__Veillonella_dispar 0.0 11 | s__Veillonella_unclassified 0.0 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.0 3 | s__Campylobacter_concisus 0.0 4 | s__Haemophilus_parainfluenzae 0.0 5 | s__Prevotella_melaninogenica 0.0 6 | s__Rothia_mucilaginosa 0.0 7 | s__Streptococcus_mitis 1.0 8 | s__Streptococcus_sanguinis 1.0 9 | s__Veillonella_atypica 0.0 10 | s__Veillonella_dispar 0.0 11 | s__Veillonella_unclassified 0.0 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_shapley_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_multi_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.161199964097 3 | s__Campylobacter_concisus 0.545213774677 4 | s__Haemophilus_parainfluenzae 1.4055245842 5 | s__Prevotella_melaninogenica 1.00027648389 6 | s__Rothia_mucilaginosa 0.62075282762 7 | s__Streptococcus_mitis 7.19723375303 8 | s__Streptococcus_sanguinis 0.505773614629 9 | s__Veillonella_atypica 0.468654353399 10 | s__Veillonella_dispar 0.299368820742 11 | s__Veillonella_unclassified 0.602769530569 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.0 3 | s__Campylobacter_concisus 0.0 4 | s__Haemophilus_parainfluenzae 0.2773704309108572 5 | s__Prevotella_melaninogenica 0.15397123599939563 6 | s__Rothia_mucilaginosa 1.307043078317127 7 | s__Streptococcus_mitis 1.0395354707166893 8 | s__Streptococcus_sanguinis 0.0 9 | s__Veillonella_atypica 0.4417301095913961 10 | s__Veillonella_dispar 0.6930293615929132 11 | s__Veillonella_unclassified 0.6063745182598926 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.0 3 | s__Campylobacter_concisus 0.0 4 | s__Haemophilus_parainfluenzae 0.2773704309108572 5 | s__Prevotella_melaninogenica 0.15397123599939563 6 | s__Rothia_mucilaginosa 1.307043078317127 7 | s__Streptococcus_mitis 1.0395354707166893 8 | s__Streptococcus_sanguinis 0.0 9 | s__Veillonella_atypica 0.4417301095913961 10 | s__Veillonella_dispar 0.6930293615929132 11 | s__Veillonella_unclassified 0.6063745182598926 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.0 3 | s__Campylobacter_concisus 0.0 4 | s__Haemophilus_parainfluenzae 0.2773704309108572 5 | s__Prevotella_melaninogenica 0.15397123599939563 6 | s__Rothia_mucilaginosa 1.307043078317127 7 | s__Streptococcus_mitis 1.0395354707166893 8 | s__Streptococcus_sanguinis 0.0 9 | s__Veillonella_atypica 0.4417301095913961 10 | s__Veillonella_dispar 0.6930293615929132 11 | s__Veillonella_unclassified 0.6063745182598926 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.3092011780653229 3 | s__Campylobacter_concisus 0.7176598031954807 4 | s__Haemophilus_parainfluenzae 1.8103811245562051 5 | s__Prevotella_melaninogenica 1.6621842417857904 6 | s__Rothia_mucilaginosa 0.9625348624528806 7 | s__Streptococcus_mitis 12.565227911271284 8 | s__Streptococcus_sanguinis 1.7726259397183706 9 | s__Veillonella_atypica 0.7164813433624978 10 | s__Veillonella_dispar 0.6069512841686406 11 | s__Veillonella_unclassified 1.0287954341941912 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.30906776751819265 3 | s__Campylobacter_concisus 0.7215287090622554 4 | s__Haemophilus_parainfluenzae 1.8110481772918554 5 | s__Prevotella_melaninogenica 1.6620730663298486 6 | s__Rothia_mucilaginosa 0.9616676938965346 7 | s__Streptococcus_mitis 12.573944067017116 8 | s__Streptococcus_sanguinis 1.771025013152809 9 | s__Veillonella_atypica 0.7304227455375991 10 | s__Veillonella_dispar 0.598123952966862 11 | s__Veillonella_unclassified 1.0239259492239403 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.6163344926501282 3 | s__Campylobacter_concisus 1.4441247025015522 4 | s__Haemophilus_parainfluenzae 2.7335154054233124 5 | s__Prevotella_melaninogenica 2.773849860838995 6 | s__Rothia_mucilaginosa -2.359832462911748 7 | s__Streptococcus_mitis 11.72974435986869 8 | s__Streptococcus_sanguinis -0.5027576468599894 9 | s__Veillonella_atypica 0.4500382456523898 10 | s__Veillonella_dispar -0.11250956141309741 11 | s__Veillonella_unclassified 0.11210932977170698 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.037199540142033666 3 | s__Campylobacter_concisus 0.08089303417475191 4 | s__Haemophilus_parainfluenzae 0.33801390416231475 5 | s__Prevotella_melaninogenica 0.14025403965212246 6 | s__Rothia_mucilaginosa 0.17370912078243275 7 | s__Streptococcus_mitis 0.8691872442058193 8 | s__Streptococcus_sanguinis 0.2410756805887803 9 | s__Veillonella_atypica 0.10273519540555873 10 | s__Veillonella_dispar 0.071220670068521 11 | s__Veillonella_unclassified 0.08683404944570114 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.3092011780653229 3 | s__Campylobacter_concisus 0.7176598031954807 4 | s__Haemophilus_parainfluenzae 1.8103811245562051 5 | s__Prevotella_melaninogenica 1.6621842417857904 6 | s__Rothia_mucilaginosa 0.9625348624528806 7 | s__Streptococcus_mitis 12.565227911271284 8 | s__Streptococcus_sanguinis 1.7726259397183706 9 | s__Veillonella_atypica 0.7164813433624978 10 | s__Veillonella_dispar 0.6069512841686406 11 | s__Veillonella_unclassified 1.0287954341941912 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.6103532531204596 3 | s__Campylobacter_concisus 1.4463926818027648 4 | s__Haemophilus_parainfluenzae 2.733804461608761 5 | s__Prevotella_melaninogenica 2.781609907663734 6 | s__Rothia_mucilaginosa -2.3791547571544323 7 | s__Streptococcus_mitis 11.617835145917672 8 | s__Streptococcus_sanguinis -0.5047365699757536 9 | s__Veillonella_atypica 0.43358427817300405 10 | s__Veillonella_dispar -0.10561668314470607 11 | s__Veillonella_unclassified 0.11451071962004977 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.05981255648021556 3 | s__Campylobacter_concisus 0.14494123911022067 4 | s__Haemophilus_parainfluenzae 0.5653602963260003 5 | s__Prevotella_melaninogenica 0.21494042765215643 6 | s__Rothia_mucilaginosa 0.3756718110762707 7 | s__Streptococcus_mitis 0.9635639841375959 8 | s__Streptococcus_sanguinis 0.09822722173235483 9 | s__Veillonella_atypica 0.10290452090127211 10 | s__Veillonella_dispar 0.10709838140507931 11 | s__Veillonella_unclassified 0.10676464696662274 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.6274520382443075 3 | s__Campylobacter_concisus 1.4522849809676797 4 | s__Haemophilus_parainfluenzae 2.7387406518525768 5 | s__Prevotella_melaninogenica 2.750747601094291 6 | s__Rothia_mucilaginosa -2.313427827601643 7 | s__Streptococcus_mitis 11.715491666416943 8 | s__Streptococcus_sanguinis -0.5204567794459233 9 | s__Veillonella_atypica 0.4562418360939421 10 | s__Veillonella_dispar -0.08444887633338816 11 | s__Veillonella_unclassified 0.114955421443817 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.6325883443088187 3 | s__Campylobacter_concisus 1.441945663565093 4 | s__Haemophilus_parainfluenzae 2.7026753339450584 5 | s__Prevotella_melaninogenica 2.769380607510136 6 | s__Rothia_mucilaginosa -2.332461065658878 7 | s__Streptococcus_mitis 11.652299537259628 8 | s__Streptococcus_sanguinis -0.5136306064510971 9 | s__Veillonella_atypica 0.44581357832660157 10 | s__Veillonella_dispar -0.08894036475343671 11 | s__Veillonella_unclassified 0.11895773785772162 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_FDR_correction_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.3232982258787425 3 | s__Campylobacter_concisus 0.6399259244009772 4 | s__Haemophilus_parainfluenzae 1.6293874822829608 5 | s__Prevotella_melaninogenica 1.6667424354794043 6 | s__Rothia_mucilaginosa 0.9836804341730101 7 | s__Streptococcus_mitis 12.534810306525605 8 | s__Streptococcus_sanguinis 1.5244378518739055 9 | s__Veillonella_atypica 0.741317940219895 10 | s__Veillonella_dispar 0.6070179894422056 11 | s__Veillonella_unclassified 1.0005791034761633 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.6163344926501282 3 | s__Campylobacter_concisus 1.4441247025015522 4 | s__Haemophilus_parainfluenzae 2.7335154054233124 5 | s__Prevotella_melaninogenica 2.773849860838995 6 | s__Rothia_mucilaginosa -2.359832462911748 7 | s__Streptococcus_mitis 11.72974435986869 8 | s__Streptococcus_sanguinis -0.5027576468599894 9 | s__Veillonella_atypica 0.4500382456523898 10 | s__Veillonella_dispar -0.11250956141309741 11 | s__Veillonella_unclassified 0.11210932977170698 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.15724656488407618 3 | s__Campylobacter_concisus 0.35410123887168277 4 | s__Haemophilus_parainfluenzae 0.7823935653418573 5 | s__Prevotella_melaninogenica 0.9817830397249366 6 | s__Rothia_mucilaginosa 0.40934802877769255 7 | s__Streptococcus_mitis 8.206082753975839 8 | s__Streptococcus_sanguinis 0.6841292856834352 9 | s__Veillonella_atypica 0.48254594896977104 10 | s__Veillonella_dispar 0.3193996732237169 11 | s__Veillonella_unclassified 0.6167124892003303 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.1615008789981154 3 | s__Campylobacter_concisus 0.36969544949178523 4 | s__Haemophilus_parainfluenzae 0.7246416218286257 5 | s__Prevotella_melaninogenica 0.9769357898458746 6 | s__Rothia_mucilaginosa 0.3886693939725185 7 | s__Streptococcus_mitis 7.963423792140214 8 | s__Streptococcus_sanguinis 0.6924748565761327 9 | s__Veillonella_atypica 0.48457675396497446 10 | s__Veillonella_dispar 0.31143951057828434 11 | s__Veillonella_unclassified 0.6216190326558949 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.06521647007434793 3 | s__Campylobacter_concisus 0.13488524555893092 4 | s__Haemophilus_parainfluenzae 0.5382994875815984 5 | s__Prevotella_melaninogenica 0.20379799138717586 6 | s__Rothia_mucilaginosa 0.38597624715793355 7 | s__Streptococcus_mitis 0.9811455481145345 8 | s__Streptococcus_sanguinis 0.09546353830365768 9 | s__Veillonella_atypica 0.10334448205589235 10 | s__Veillonella_dispar 0.11093237410637258 11 | s__Veillonella_unclassified 0.08827555230081272 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.6274520382443075 3 | s__Campylobacter_concisus 1.4522849809676797 4 | s__Haemophilus_parainfluenzae 2.7387406518525768 5 | s__Prevotella_melaninogenica 2.750747601094291 6 | s__Rothia_mucilaginosa -2.313427827601643 7 | s__Streptococcus_mitis 11.715491666416943 8 | s__Streptococcus_sanguinis -0.5204567794459233 9 | s__Veillonella_atypica 0.4562418360939421 10 | s__Veillonella_dispar -0.08444887633338816 11 | s__Veillonella_unclassified 0.114955421443817 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 1.1478198773254777 3 | s__Campylobacter_concisus 1.1106131580702898 4 | s__Haemophilus_parainfluenzae 2.60061626539049 5 | s__Prevotella_melaninogenica 1.9223273969925305 6 | s__Rothia_mucilaginosa -1.724360968445506 7 | s__Streptococcus_mitis 7.325054324122422 8 | s__Streptococcus_sanguinis -0.2620331379577502 9 | s__Veillonella_atypica 0.4313459456600427 10 | s__Veillonella_dispar -0.1938158781918641 11 | s__Veillonella_unclassified -0.004195020537537032 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 1.1600047072966988 3 | s__Campylobacter_concisus 1.1070110732977756 4 | s__Haemophilus_parainfluenzae 2.5662259910191603 5 | s__Prevotella_melaninogenica 1.9370470273592244 6 | s__Rothia_mucilaginosa -1.7135843609162147 7 | s__Streptococcus_mitis 7.126791427692884 8 | s__Streptococcus_sanguinis -0.2774939380307225 9 | s__Veillonella_atypica 0.43402897999677137 10 | s__Veillonella_dispar -0.20545224258043854 11 | s__Veillonella_unclassified 0.000741169706278777 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.011272662509296318 3 | s__Campylobacter_concisus 0.026727389415507768 4 | s__Haemophilus_parainfluenzae 0.10462245511885705 5 | s__Prevotella_melaninogenica 0.05314418546632722 6 | s__Rothia_mucilaginosa 0.07231963171642025 7 | s__Streptococcus_mitis 0.5336371732487072 8 | s__Streptococcus_sanguinis 0.08126378264596375 9 | s__Veillonella_atypica 0.05317454585463113 10 | s__Veillonella_dispar 0.02496153938624603 11 | s__Veillonella_unclassified 0.05818644628040117 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.15724656488407618 3 | s__Campylobacter_concisus 0.35410123887168277 4 | s__Haemophilus_parainfluenzae 0.7823935653418573 5 | s__Prevotella_melaninogenica 0.9817830397249366 6 | s__Rothia_mucilaginosa 0.40934802877769255 7 | s__Streptococcus_mitis 8.206082753975839 8 | s__Streptococcus_sanguinis 0.6841292856834352 9 | s__Veillonella_atypica 0.48254594896977104 10 | s__Veillonella_dispar 0.3193996732237169 11 | s__Veillonella_unclassified 0.6167124892003303 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.14990898479191767 3 | s__Campylobacter_concisus 0.4341623905439014 4 | s__Haemophilus_parainfluenzae 1.1169427473619091 5 | s__Prevotella_melaninogenica 1.1002812523647658 6 | s__Rothia_mucilaginosa 0.6383249978354154 7 | s__Streptococcus_mitis 7.0066922884874945 8 | s__Streptococcus_sanguinis 0.591631306339861 9 | s__Veillonella_atypica 0.5449969084208091 10 | s__Veillonella_dispar 0.41078589800787313 11 | s__Veillonella_unclassified 0.5548692889084407 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.14993863158016885 3 | s__Campylobacter_concisus 0.43847599823444305 4 | s__Haemophilus_parainfluenzae 1.107233424209659 5 | s__Prevotella_melaninogenica 1.1185733207157222 6 | s__Rothia_mucilaginosa 0.6409635619897676 7 | s__Streptococcus_mitis 7.115377414216193 8 | s__Streptococcus_sanguinis 0.5650677840668347 9 | s__Veillonella_atypica 0.5505408578237734 10 | s__Veillonella_dispar 0.43343604423174814 11 | s__Veillonella_unclassified 0.5666983574206477 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.05618294391096145 3 | s__Campylobacter_concisus 0.026870794662179346 4 | s__Haemophilus_parainfluenzae 0.28643206917448666 5 | s__Prevotella_melaninogenica 0.09566648780516639 6 | s__Rothia_mucilaginosa 0.2342278348581567 7 | s__Streptococcus_mitis 0.5355328203268485 8 | s__Streptococcus_sanguinis 0.04308111892800628 9 | s__Veillonella_atypica 0.02977094841956203 10 | s__Veillonella_dispar 0.049088895588664715 11 | s__Veillonella_unclassified 0.039534595179770246 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 1.1478198773254777 3 | s__Campylobacter_concisus 1.1106131580702898 4 | s__Haemophilus_parainfluenzae 2.60061626539049 5 | s__Prevotella_melaninogenica 1.9223273969925305 6 | s__Rothia_mucilaginosa -1.724360968445506 7 | s__Streptococcus_mitis 7.325054324122422 8 | s__Streptococcus_sanguinis -0.2620331379577502 9 | s__Veillonella_atypica 0.4313459456600427 10 | s__Veillonella_dispar -0.1938158781918641 11 | s__Veillonella_unclassified -0.004195020537537032 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.14990898479191767 3 | s__Campylobacter_concisus 0.4341623905439014 4 | s__Haemophilus_parainfluenzae 1.1169427473619091 5 | s__Prevotella_melaninogenica 1.1002812523647658 6 | s__Rothia_mucilaginosa 0.6383249978354154 7 | s__Streptococcus_mitis 7.0066922884874945 8 | s__Streptococcus_sanguinis 0.591631306339861 9 | s__Veillonella_atypica 0.5449969084208091 10 | s__Veillonella_dispar 0.41078589800787313 11 | s__Veillonella_unclassified 0.5548692889084407 12 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | FishTaco: Functional Shifts Taxonomic Contributors 2 | ================================================== 3 | 4 | The official FishTaco source code repository. For details on FishTaco, see http://borenstein-lab.github.io/fishtaco/. 5 | 6 | FishTaco is a metagenomic computational framework, aiming to identify the taxa that are driving the functional shifts 7 | we observe in microbiomes of different individuals or disease states. 8 | 9 | For FishTaco announcements and questions, including notification of new releases, you can visit the `FiShTaCo users forum `_. 10 | 11 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 2 | s__Actinomyces_odontolyticus 0.005283786308208603 3 | s__Campylobacter_concisus 0.03307880836881002 4 | s__Haemophilus_parainfluenzae 0.15341656495518552 5 | s__Prevotella_melaninogenica 0.050737760613336395 6 | s__Rothia_mucilaginosa 0.041255947937363806 7 | s__Streptococcus_mitis 0.43957919533084255 8 | s__Streptococcus_sanguinis 0.05847583048052539 9 | s__Veillonella_atypica 0.024713643418040058 10 | s__Veillonella_dispar 0.03600902977232156 11 | s__Veillonella_unclassified 0.04279721057407755 12 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_filtering_by_function_list_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | Taxa K00001 K00007 K00020 2 | s__Actinomyces_odontolyticus 0.305954854752 0.0 0.0 3 | s__Campylobacter_concisus 0.723085165445 0.0 0.0 4 | s__Haemophilus_parainfluenzae 1.6240510604 0.0 0.0 5 | s__Prevotella_melaninogenica 1.6765258756 0.0 0.0 6 | s__Rothia_mucilaginosa 1.03304233661 0.0 0.0 7 | s__Streptococcus_mitis 12.0967790101 0.0 0.0 8 | s__Streptococcus_sanguinis 1.4350527853 0.0 0.0 9 | s__Veillonella_atypica 0.750656678519 0.0 0.0 10 | s__Veillonella_dispar 0.605683883971 0.0 0.0 11 | s__Veillonella_unclassified 1.02637180925 0.0 0.0 12 | -------------------------------------------------------------------------------- /.gitignore~: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | *.egg-info/ 22 | .installed.cfg 23 | *.egg 24 | 25 | # PyInstaller 26 | # Usually these files are written by a python script from a template 27 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 28 | *.manifest 29 | *.spec 30 | 31 | # Installer logs 32 | pip-log.txt 33 | pip-delete-this-directory.txt 34 | 35 | # Unit test / coverage reports 36 | htmlcov/ 37 | .tox/ 38 | .coverage 39 | .cache 40 | nosetests.xml 41 | coverage.xml 42 | 43 | # Translations 44 | *.mo 45 | *.pot 46 | 47 | # Django stuff: 48 | *.log 49 | 50 | # Sphinx documentation 51 | docs/_build/ 52 | 53 | # PyBuilder 54 | target/ 55 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Ordering 1 2 3 4 5 6 7 8 9 10 2 | 1 8 0 3 5 4 7 1 2 9 6 3 | 2 9 8 5 1 2 6 7 0 3 4 4 | 3 4 9 3 6 0 8 7 5 2 1 5 | 4 7 5 3 9 4 8 1 0 2 6 6 | 5 2 7 8 9 4 5 0 1 3 6 7 | 6 6 5 7 8 1 0 4 9 2 3 8 | 7 7 3 0 1 6 8 5 9 2 4 9 | 8 6 1 9 4 2 8 7 3 5 0 10 | 9 7 8 4 1 5 6 9 0 3 2 11 | 10 7 6 0 2 5 1 9 4 8 3 12 | 11 8 1 7 5 6 3 0 4 9 2 13 | 12 2 4 3 6 8 9 1 0 5 7 14 | 13 7 1 3 8 4 9 6 2 0 5 15 | 14 4 9 6 0 7 1 5 3 8 2 16 | 15 1 4 7 5 8 2 9 0 6 3 17 | 16 5 1 3 2 0 9 4 7 8 6 18 | 17 4 1 9 8 6 7 0 3 5 2 19 | 18 0 5 9 3 8 7 1 6 2 4 20 | 19 0 6 3 9 8 5 1 4 2 7 21 | 20 2 3 4 6 1 8 0 9 7 5 22 | 21 1 8 2 3 7 6 0 5 4 9 23 | 22 1 4 2 8 5 0 3 7 6 9 24 | 23 9 7 6 2 8 4 3 5 0 1 25 | 24 5 8 6 1 0 7 3 2 9 4 26 | 25 9 8 0 2 4 3 7 5 6 1 27 | 26 9 2 5 7 1 0 3 4 8 6 28 | 27 5 9 1 0 3 6 2 7 4 8 29 | 28 5 1 8 6 9 2 0 3 4 7 30 | 29 6 3 1 8 0 9 7 5 4 2 31 | 30 1 4 9 6 8 5 3 0 7 2 32 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Ordering 1 2 3 4 5 6 7 8 9 10 2 | 1 8 6 3 2 7 1 0 4 9 5 3 | 2 5 6 7 1 9 2 0 4 8 3 4 | 3 1 2 8 5 9 4 7 3 6 0 5 | 4 1 4 8 3 6 2 7 0 5 9 6 | 5 4 6 1 8 2 7 5 3 9 0 7 | 6 4 9 5 0 1 3 6 8 2 7 8 | 7 7 5 8 3 6 0 2 9 1 4 9 | 8 1 3 6 9 4 0 2 5 7 8 10 | 9 6 9 4 0 1 8 3 5 2 7 11 | 10 9 8 1 0 7 2 6 3 5 4 12 | 11 0 1 3 6 5 9 8 2 7 4 13 | 12 4 1 0 2 8 6 9 7 5 3 14 | 13 9 0 6 3 1 2 4 8 7 5 15 | 14 2 7 4 6 8 9 5 1 0 3 16 | 15 0 3 8 1 5 2 6 4 9 7 17 | 16 3 6 9 2 0 4 5 1 7 8 18 | 17 3 4 1 7 8 0 5 9 2 6 19 | 18 0 3 9 4 1 5 6 7 8 2 20 | 19 2 6 4 1 7 9 5 3 0 8 21 | 20 9 4 1 2 6 8 3 0 5 7 22 | 21 1 7 3 4 8 9 2 6 0 5 23 | 22 0 8 1 2 5 6 7 3 4 9 24 | 23 6 5 8 2 7 4 3 9 0 1 25 | 24 2 3 5 6 4 8 0 9 1 7 26 | 25 4 6 2 9 3 1 7 0 8 5 27 | 26 8 9 6 7 0 2 5 3 4 1 28 | 27 6 3 4 9 5 1 7 2 0 8 29 | 28 2 9 1 4 6 3 0 7 5 8 30 | 29 4 1 9 3 0 7 8 6 5 2 31 | 30 5 8 7 4 0 1 3 9 2 6 32 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | Ordering 1 2 3 4 5 6 7 8 9 10 2 | 1 0 5 1 8 4 2 3 6 9 7 3 | 2 1 9 3 6 5 7 8 4 2 0 4 | 3 7 2 3 0 9 5 6 1 4 8 5 | 4 9 7 0 6 4 3 8 2 1 5 6 | 5 4 2 3 7 5 1 8 0 9 6 7 | 6 1 3 8 5 4 2 9 0 7 6 8 | 7 7 0 4 5 3 6 2 1 9 8 9 | 8 1 3 6 4 0 7 2 8 5 9 10 | 9 8 5 4 3 1 7 9 6 0 2 11 | 10 0 3 1 5 8 9 7 6 2 4 12 | 11 3 7 9 4 5 8 0 1 6 2 13 | 12 4 7 3 9 2 8 6 5 0 1 14 | 13 0 5 3 2 4 8 7 9 1 6 15 | 14 8 0 9 3 4 6 2 7 5 1 16 | 15 4 3 5 6 2 1 0 8 9 7 17 | 16 2 4 9 6 8 0 5 7 1 3 18 | 17 6 3 0 8 5 1 7 2 9 4 19 | 18 9 2 5 6 8 3 1 7 4 0 20 | 19 2 3 6 8 9 7 1 4 5 0 21 | 20 2 4 0 6 9 3 8 5 1 7 22 | 21 4 8 1 5 3 0 6 9 7 2 23 | 22 3 9 2 0 7 4 6 1 5 8 24 | 23 0 6 4 9 1 5 3 2 8 7 25 | 24 5 2 1 3 0 6 8 9 7 4 26 | 25 5 9 1 2 0 6 4 3 8 7 27 | 26 2 4 6 0 9 8 7 1 3 5 28 | 27 4 3 9 1 0 2 7 5 6 8 29 | 28 7 8 5 3 4 2 0 6 1 9 30 | 29 6 8 1 4 3 7 9 0 5 2 31 | 30 1 4 2 8 3 6 9 7 5 0 32 | -------------------------------------------------------------------------------- /setup.py~: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | 4 | try: 5 | from setuptools import setup 6 | except ImportError: 7 | from distutils.core import setup 8 | 9 | 10 | def read(*paths): 11 | """Build a file path from *paths* and return the contents.""" 12 | with open(os.path.join(*paths), 'r') as f: 13 | return f.read() 14 | 15 | setup(name='FiShTaCo', 16 | version='1.0', 17 | classifiers=['License :: OSI Approved :: BSD License'], 18 | license=['BSD'], 19 | description='FiShTaCo: a metagenomic computational framework, aiming to identify the taxa that are driving functional shifts in microbiomes.', 20 | long_description=read('README.rst'), 21 | author='Ohad Manor', 22 | author_email='omanor@gmail.com', 23 | url='http://omanor.github.io/fishtaco/', 24 | packages=['fishtaco'], 25 | package_data={'fishtaco': ['data/*.tab', 'data/*.lst', 'examples/*.tab']}, 26 | install_requires=['NumPy >= 1.6.1', 'SciPy >= 0.9', 'scikit-learn >= 0.15.2', 'pandas >= 0.14'], 27 | scripts=['scripts/run_fishtaco.py', 'tests/test_fishtaco.py'], 28 | ) 29 | 30 | 31 | 32 | 33 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | *.egg-info/ 22 | .installed.cfg 23 | *.egg 24 | 25 | # PyInstaller 26 | # Usually these files are written by a python script from a template 27 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 28 | *.manifest 29 | *.spec 30 | 31 | # Installer logs 32 | pip-log.txt 33 | pip-delete-this-directory.txt 34 | 35 | # Unit test / coverage reports 36 | htmlcov/ 37 | .tox/ 38 | .coverage 39 | .cache 40 | nosetests.xml 41 | coverage.xml 42 | 43 | # Translations 44 | *.mo 45 | *.pot 46 | 47 | # Django stuff: 48 | *.log 49 | 50 | # Sphinx documentation 51 | docs/_build/ 52 | 53 | # PyBuilder 54 | target/ 55 | 56 | # PyCharm 57 | .idea/ 58 | .idea 59 | .idea/dictionaries .idea/encodings.xml .idea/fishtaco.iml .idea/inspectionProfiles .idea/misc.xml .idea/modules.xml .idea/scopes .idea/vcs.xml .idea/workspace.xml 60 | dist 61 | __pycache__ 62 | MANIFEST.in~ 63 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': None, 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | Function meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | K00001 0.8452748613766243 0.4562966093890158 10.292623711091466 7.607469592332784e-25 24.118759774876267 1.0 1.0 1.0 1.0 K00001 4 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': None, 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | #DA functions:1 #Taxa:10 #Samples:213 3 | 0:K00001 took 3.687908887863159 seconds to run. 4 | ---------------------------------------------- 5 | Program completed successfully with no errors. 6 | ---------------------------------------------- 7 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | 4 | try: 5 | from setuptools import setup 6 | except ImportError: 7 | from distutils.core import setup 8 | 9 | 10 | def read(*paths): 11 | """Build a file path from *paths* and return the contents.""" 12 | with open(os.path.join(*paths), 'r') as f: 13 | return f.read() 14 | 15 | setup(name='FishTaco', 16 | version='1.1.6', 17 | classifiers=['License :: Free for non-commercial use'], 18 | description='FishTaco: a metagenomic computational framework, aiming to ' 19 | 'identify the taxa that are driving functional shifts in microbiomes.', 20 | long_description=('\n\n' + ('README.rst') + '\n\n' + read('HISTORY.rst') + 21 | '\n\n' + read('AUTHORS.rst') + '\n\n' + read('LICENSE') + '\n\n'), 22 | author='Ohad Manor', 23 | author_email='omanor@gmail.com', 24 | url='http://omanor.github.io/fishtaco/', 25 | packages=['fishtaco'], 26 | package_data={'fishtaco': ['data/*.tab', 'data/*.lst', 27 | 'examples/*.tab', 'examples/output/*.tab']}, 28 | install_requires=['NumPy >= 1.17.0', 'SciPy >= 1.3.0', 'scikit-learn >= 0.21.3', 29 | 'statsmodels >= 0.10.1', 'pandas >= 0.25.0', 'MUSiCC >= 1.0.3'], 30 | scripts=['scripts/run_fishtaco.py', 'tests/test_fishtaco.py'], 31 | ) 32 | 33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'functional_profile_already_corrected_with_musicc': True, 'multiple_hypothesis_correction': 'FDR-0.05', 'apply_inference': True, 'residual_mode': 'remove_residual', 'perform_inference_on_ko_level': False, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'normalization_mode': 'scale_permuted', 'map_function_file': None, 'na_rep': 'NA', 'da_result_file': None, 'control_label': '0', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_permutations': '100', 'taxa_assessment_method': 'single_taxa', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'score_to_compute': 'wilcoxon', 'taxa_to_function_file': None, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'write_log': True, 'single_function_filter': None, 'max_da_functions_cases_controls': None, 'permutation_mode': 'blocks', 'max_score_cutoff': '100', 'map_function_level': 'none', 'number_of_shapley_orderings_per_taxa': '5', 'case_label': '1'} 2 | Function meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | K00001 0.8452748613766243 0.4562966093890158 10.292623711091466 7.607469592332784e-25 24.118759774876267 1.0 1.0 1.0 1.0 K00001 4 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | Function meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | K00001 0.8452748613766243 0.4562966093890158 10.292623711091466 7.607469592332784e-25 24.118759774876267 1.0 1.0 1.0 1.0 K00001 4 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'functional_profile_already_corrected_with_musicc': True, 'multiple_hypothesis_correction': 'FDR-0.05', 'apply_inference': True, 'residual_mode': 'remove_residual', 'perform_inference_on_ko_level': False, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'normalization_mode': 'scale_permuted', 'map_function_file': None, 'na_rep': 'NA', 'da_result_file': None, 'control_label': '0', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_permutations': '100', 'taxa_assessment_method': 'single_taxa', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'score_to_compute': 'wilcoxon', 'taxa_to_function_file': None, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'write_log': True, 'single_function_filter': None, 'max_da_functions_cases_controls': None, 'permutation_mode': 'blocks', 'max_score_cutoff': '100', 'map_function_level': 'none', 'number_of_shapley_orderings_per_taxa': '5', 'case_label': '1'} 2 | #DA functions:1 #Taxa:10 #Samples:213 3 | 0:K00001 took 4.877540826797485 seconds to run. 4 | ---------------------------------------------- 5 | Program completed successfully with no errors. 6 | ---------------------------------------------- 7 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | #DA functions:1 #Taxa:10 #Samples:213 3 | 0:K00001 took 3.671281337738037 seconds to run. 4 | ---------------------------------------------- 5 | Program completed successfully with no errors. 6 | ---------------------------------------------- 7 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | Function meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | K00001 0.8452748613766243 0.4562966093890158 10.292623711091466 7.607469592332784e-25 24.118759774876267 1.0 1.0 1.0 1.0 K00001 4 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | #DA functions:1 #Taxa:10 #Samples:213 3 | 0:K00001 took 3.686523914337158 seconds to run. 4 | ---------------------------------------------- 5 | Program completed successfully with no errors. 6 | ---------------------------------------------- 7 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'max_da_functions_cases_controls': None, 'taxa_assessment_method': 'single_taxa', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_permutations': '100', 'control_label': '0', 'map_function_file': None, 'da_result_file': None, 'apply_inference': False, 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'na_rep': 'NA', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'perform_inference_on_ko_level': False, 'write_log': True, 'map_function_level': 'none', 'case_label': '1', 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'permutation_mode': 'blocks', 'single_function_filter': None, 'max_score_cutoff': '100', 'number_of_shapley_orderings_per_taxa': '5', 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'normalization_mode': 'scale_permuted', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab'} 2 | Function meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | K00001 0.8452748613766243 0.4562966093890158 10.292623711091466 7.607469592332784e-25 24.118759774876267 1.0 1.0 1.0 1.0 K00001 4 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'max_da_functions_cases_controls': None, 'taxa_assessment_method': 'single_taxa', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_permutations': '100', 'control_label': '0', 'map_function_file': None, 'da_result_file': None, 'apply_inference': False, 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'na_rep': 'NA', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'perform_inference_on_ko_level': False, 'write_log': True, 'map_function_level': 'none', 'case_label': '1', 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'permutation_mode': 'blocks', 'single_function_filter': None, 'max_score_cutoff': '100', 'number_of_shapley_orderings_per_taxa': '5', 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'normalization_mode': 'scale_permuted', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab'} 2 | #DA functions:1 #Taxa:10 #Samples:213 3 | 0:K00001 took 5.269505500793457 seconds to run. 4 | ---------------------------------------------- 5 | Program completed successfully with no errors. 6 | ---------------------------------------------- 7 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'taxa_assessment_method': 'single_taxa', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'apply_inference': True, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'da_result_file': None, 'write_log': True, 'map_function_level': 'none', 'number_of_permutations': '100', 'case_label': '1', 'map_function_file': None, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'max_score_cutoff': '100', 'permutation_mode': 'blocks', 'max_da_functions_cases_controls': None, 'na_rep': 'NA', 'single_function_filter': None, 'perform_inference_on_ko_level': False, 'control_label': '0', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_shapley_orderings_per_taxa': '5'} 2 | Function meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | K00001 0.8452748613766243 0.4562966093890158 10.292623711091466 7.607469592332784e-25 24.118759774876267 1.0 1.0 1.0 1.0 K00001 4 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'taxa_assessment_method': 'single_taxa', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'apply_inference': True, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'da_result_file': None, 'write_log': True, 'map_function_level': 'none', 'number_of_permutations': '100', 'case_label': '1', 'map_function_file': None, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'max_score_cutoff': '100', 'permutation_mode': 'blocks', 'max_da_functions_cases_controls': None, 'na_rep': 'NA', 'single_function_filter': None, 'perform_inference_on_ko_level': False, 'control_label': '0', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_shapley_orderings_per_taxa': '5'} 2 | #DA functions:1 #Taxa:10 #Samples:213 3 | 0:K00001 took 5.044782400131226 seconds to run. 4 | ---------------------------------------------- 5 | Program completed successfully with no errors. 6 | ---------------------------------------------- 7 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # RUN PARAMETERS: 2 | # --------------- 3 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'functional_profile_already_corrected_with_musicc': True, 'multiple_hypothesis_correction': 'FDR-0.05', 'apply_inference': True, 'residual_mode': 'remove_residual', 'perform_inference_on_ko_level': False, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'normalization_mode': 'scale_permuted', 'map_function_file': None, 'na_rep': 'NA', 'da_result_file': None, 'control_label': '0', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_permutations': '100', 'taxa_assessment_method': 'single_taxa', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'score_to_compute': 'wilcoxon', 'taxa_to_function_file': None, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'write_log': True, 'single_function_filter': None, 'max_da_functions_cases_controls': None, 'permutation_mode': 'blocks', 'max_score_cutoff': '100', 'map_function_level': 'none', 'number_of_shapley_orderings_per_taxa': '5', 'case_label': '1'} 4 | # 5 | # LEGEND: 6 | # ------- 7 | # a:case-associated and driving case-enrichment; 8 | # b:case-associated and attenuating case-enrichment; 9 | # c:control-associated and driving case-enrichment; 10 | # d:control-associated and attenuating case-enrichment; 11 | # 12 | # TAXON-LEVEL CONTRIBUTION VALUES: 13 | # -------------------------------- 14 | # 15 | Taxa K00001 16 | s__Actinomyces_odontolyticus c:0.6163344926501282 17 | s__Campylobacter_concisus c:1.4441247025015522 18 | s__Haemophilus_parainfluenzae c:2.7335154054233124 19 | s__Prevotella_melaninogenica c:2.773849860838995 20 | s__Rothia_mucilaginosa d:-2.359832462911748 21 | s__Streptococcus_mitis a:11.72974435986869 22 | s__Streptococcus_sanguinis b:-0.5027576468599894 23 | s__Veillonella_atypica c:0.4500382456523898 24 | s__Veillonella_dispar d:-0.11250956141309741 25 | s__Veillonella_unclassified c:0.11210932977170698 26 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # RUN PARAMETERS: 2 | # --------------- 3 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'max_da_functions_cases_controls': None, 'taxa_assessment_method': 'single_taxa', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_permutations': '100', 'control_label': '0', 'map_function_file': None, 'da_result_file': None, 'apply_inference': False, 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'na_rep': 'NA', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'perform_inference_on_ko_level': False, 'write_log': True, 'map_function_level': 'none', 'case_label': '1', 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'permutation_mode': 'blocks', 'single_function_filter': None, 'max_score_cutoff': '100', 'number_of_shapley_orderings_per_taxa': '5', 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'normalization_mode': 'scale_permuted', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab'} 4 | # 5 | # LEGEND: 6 | # ------- 7 | # a:case-associated and driving case-enrichment; 8 | # b:case-associated and attenuating case-enrichment; 9 | # c:control-associated and driving case-enrichment; 10 | # d:control-associated and attenuating case-enrichment; 11 | # 12 | # TAXON-LEVEL CONTRIBUTION VALUES: 13 | # -------------------------------- 14 | # 15 | Taxa K00001 16 | s__Actinomyces_odontolyticus c:0.3092011780653229 17 | s__Campylobacter_concisus c:0.7176598031954807 18 | s__Haemophilus_parainfluenzae c:1.8103811245562051 19 | s__Prevotella_melaninogenica c:1.6621842417857904 20 | s__Rothia_mucilaginosa c:0.9625348624528806 21 | s__Streptococcus_mitis a:12.565227911271284 22 | s__Streptococcus_sanguinis a:1.7726259397183706 23 | s__Veillonella_atypica c:0.7164813433624978 24 | s__Veillonella_dispar c:0.6069512841686406 25 | s__Veillonella_unclassified c:1.0287954341941912 26 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # RUN PARAMETERS: 2 | # --------------- 3 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'taxa_assessment_method': 'single_taxa', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'apply_inference': True, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'da_result_file': None, 'write_log': True, 'map_function_level': 'none', 'number_of_permutations': '100', 'case_label': '1', 'map_function_file': None, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'max_score_cutoff': '100', 'permutation_mode': 'blocks', 'max_da_functions_cases_controls': None, 'na_rep': 'NA', 'single_function_filter': None, 'perform_inference_on_ko_level': False, 'control_label': '0', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_shapley_orderings_per_taxa': '5'} 4 | # 5 | # LEGEND: 6 | # ------- 7 | # a:case-associated and driving case-enrichment; 8 | # b:case-associated and attenuating case-enrichment; 9 | # c:control-associated and driving case-enrichment; 10 | # d:control-associated and attenuating case-enrichment; 11 | # 12 | # TAXON-LEVEL CONTRIBUTION VALUES: 13 | # -------------------------------- 14 | # 15 | Taxa K00001 16 | s__Actinomyces_odontolyticus c:0.6274520382443075 17 | s__Campylobacter_concisus c:1.4522849809676797 18 | s__Haemophilus_parainfluenzae c:2.7387406518525768 19 | s__Prevotella_melaninogenica c:2.750747601094291 20 | s__Rothia_mucilaginosa d:-2.313427827601643 21 | s__Streptococcus_mitis a:11.715491666416943 22 | s__Streptococcus_sanguinis b:-0.5204567794459233 23 | s__Veillonella_atypica c:0.4562418360939421 24 | s__Veillonella_dispar d:-0.08444887633338816 25 | s__Veillonella_unclassified c:0.114955421443817 26 | -------------------------------------------------------------------------------- /HISTORY.rst: -------------------------------------------------------------------------------- 1 | ======= 2 | HISTORY 3 | ======= 4 | 5 | ==================== 6 | 1.1.6 (25 May, 2021) 7 | ==================== 8 | * fixed type error with num_cv by specifying argument input type 9 | 10 | ==================== 11 | 1.1.5 (24 May, 2021) 12 | ==================== 13 | * fixed type in read_csv file separator 14 | 15 | ======================== 16 | 1.1.4 (5 November, 2020) 17 | ======================== 18 | * fixed bug that could cause OTU IDs to be read in as integers (caused problems when setting the dataframe index). Due to an underlying pandas bug, could be reverted if pandas is updated 19 | 20 | ======================= 21 | 1.1.3 (23 August, 2019) 22 | ======================= 23 | * fixed bug in specifying FDR correction 24 | * changed DA function statistics output file to include Bonferroni- and FDR-corrected significance values, rather than just whether they passed the supplied alpha threshold 25 | * added tests for FDR correction filtering and changing the alpha cutoff for differentially abundant functions 26 | 27 | ======================= 28 | 1.1.2 (22 August, 2019) 29 | ======================= 30 | * fixed deprecated scikit-learn imports 31 | * fixed swapped control and case sample numbers reported while running (this bug was only a display error, it had no effect on the results) 32 | * added option to specify the number of folds for cross validation during genomic content inference 33 | * added informative error message for when there are too few control and/or case samples for genomic content inference (given the number of folds) 34 | * added option to specify the significance cutoff for multiple hypothesis corrections 35 | 36 | ======================== 37 | 1.1.1 (20 October, 2016) 38 | ======================== 39 | * improved unit testing to include each individual function, as well as comparing the calculated FishTaco output to a pre-computed example output 40 | * removed debugging statements from code 41 | * added documentation to all functions 42 | * resolved several runtime warnings for edge cases 43 | 44 | ========================= 45 | 1.1.0 (18 February, 2016) 46 | ========================= 47 | * added the option to run FishTaco with no functional profile input, predicting it from the taxonomic profiles and genomic content inputs 48 | * added a unit test for the option to run with no functional profiles input 49 | * added the option to run FishTaco on only a subset of functions by supplying a list file as input 50 | 51 | ===================== 52 | 1.0.5 (15 July, 2015) 53 | ===================== 54 | * Initial release 55 | 56 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': None, 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | Taxa meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | s__Actinomyces_odontolyticus 0.004788111982456582 0.03165727519296322 -11.637846727987196 2.6461190502437123e-31 -30.577390620579045 1.0 1.0 1.0 1.0 s__Actinomyces_odontolyticus 4 | s__Campylobacter_concisus 0.007429160844587094 0.06964164495501507 -11.28653228721112 1.529527506449628e-29 -28.815442708419823 1.0 1.0 1.0 1.0 s__Campylobacter_concisus 5 | s__Haemophilus_parainfluenzae 0.18302478559632007 0.35017383702219373 -5.761112126903864 8.356150292016951e-09 -8.0779937574894 1.0 1.0 1.0 1.0 s__Haemophilus_parainfluenzae 6 | s__Prevotella_melaninogenica 0.013477531694454245 0.16173121734427978 -11.622282164155344 3.1753890087019113e-31 -30.498203062818064 1.0 1.0 1.0 1.0 s__Prevotella_melaninogenica 7 | s__Rothia_mucilaginosa 0.05087954107728926 0.13706606805385468 -8.073561610493218 6.827670022642209e-16 -15.165727476252172 1.0 1.0 1.0 1.0 s__Rothia_mucilaginosa 8 | s__Streptococcus_mitis 0.6931068257045447 0.01672800864812219 12.609520212918492 1.8712872006902566e-36 35.72785955290465 1.0 1.0 1.0 1.0 s__Streptococcus_mitis 9 | s__Streptococcus_sanguinis 0.025518241266430316 0.0038204413798713674 9.563312720113284 1.1404650624488597e-21 20.942918014568026 1.0 1.0 1.0 1.0 s__Streptococcus_sanguinis 10 | s__Veillonella_atypica 0.007658508781594692 0.07139870275712581 -9.974661907097929 1.9677044722970736e-23 -22.706040127290976 1.0 1.0 1.0 1.0 s__Veillonella_atypica 11 | s__Veillonella_dispar 0.00456204374569069 0.05765179102897155 -11.306543869280643 1.2179297905994872e-29 -28.914377746542282 1.0 1.0 1.0 1.0 s__Veillonella_dispar 12 | s__Veillonella_unclassified 0.009555249306632402 0.10013101361760238 -10.757337116928172 5.472879263730797e-27 -26.261784132649357 1.0 1.0 1.0 1.0 s__Veillonella_unclassified 13 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'functional_profile_already_corrected_with_musicc': True, 'multiple_hypothesis_correction': 'FDR-0.05', 'apply_inference': True, 'residual_mode': 'remove_residual', 'perform_inference_on_ko_level': False, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_de_novo_inf', 'normalization_mode': 'scale_permuted', 'map_function_file': None, 'na_rep': 'NA', 'da_result_file': None, 'control_label': '0', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_permutations': '100', 'taxa_assessment_method': 'single_taxa', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'score_to_compute': 'wilcoxon', 'taxa_to_function_file': None, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'write_log': True, 'single_function_filter': None, 'max_da_functions_cases_controls': None, 'permutation_mode': 'blocks', 'max_score_cutoff': '100', 'map_function_level': 'none', 'number_of_shapley_orderings_per_taxa': '5', 'case_label': '1'} 2 | Taxa meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | s__Actinomyces_odontolyticus 0.004788111982456582 0.03165727519296322 -11.637846727987196 2.6461190502437123e-31 -30.577390620579045 1.0 1.0 1.0 1.0 s__Actinomyces_odontolyticus 4 | s__Campylobacter_concisus 0.007429160844587094 0.06964164495501507 -11.28653228721112 1.529527506449628e-29 -28.815442708419823 1.0 1.0 1.0 1.0 s__Campylobacter_concisus 5 | s__Haemophilus_parainfluenzae 0.18302478559632007 0.35017383702219373 -5.761112126903864 8.356150292016951e-09 -8.0779937574894 1.0 1.0 1.0 1.0 s__Haemophilus_parainfluenzae 6 | s__Prevotella_melaninogenica 0.013477531694454245 0.16173121734427978 -11.622282164155344 3.1753890087019113e-31 -30.498203062818064 1.0 1.0 1.0 1.0 s__Prevotella_melaninogenica 7 | s__Rothia_mucilaginosa 0.05087954107728926 0.13706606805385468 -8.073561610493218 6.827670022642209e-16 -15.165727476252172 1.0 1.0 1.0 1.0 s__Rothia_mucilaginosa 8 | s__Streptococcus_mitis 0.6931068257045447 0.01672800864812219 12.609520212918492 1.8712872006902566e-36 35.72785955290465 1.0 1.0 1.0 1.0 s__Streptococcus_mitis 9 | s__Streptococcus_sanguinis 0.025518241266430316 0.0038204413798713674 9.563312720113284 1.1404650624488597e-21 20.942918014568026 1.0 1.0 1.0 1.0 s__Streptococcus_sanguinis 10 | s__Veillonella_atypica 0.007658508781594692 0.07139870275712581 -9.974661907097929 1.9677044722970736e-23 -22.706040127290976 1.0 1.0 1.0 1.0 s__Veillonella_atypica 11 | s__Veillonella_dispar 0.00456204374569069 0.05765179102897155 -11.306543869280643 1.2179297905994872e-29 -28.914377746542282 1.0 1.0 1.0 1.0 s__Veillonella_dispar 12 | s__Veillonella_unclassified 0.009555249306632402 0.10013101361760238 -10.757337116928172 5.472879263730797e-27 -26.261784132649357 1.0 1.0 1.0 1.0 s__Veillonella_unclassified 13 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | Taxa meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | s__Actinomyces_odontolyticus 0.004788111982456582 0.03165727519296322 -11.637846727987196 2.6461190502437123e-31 -30.577390620579045 1.0 1.0 1.0 1.0 s__Actinomyces_odontolyticus 4 | s__Campylobacter_concisus 0.007429160844587094 0.06964164495501507 -11.28653228721112 1.529527506449628e-29 -28.815442708419823 1.0 1.0 1.0 1.0 s__Campylobacter_concisus 5 | s__Haemophilus_parainfluenzae 0.18302478559632007 0.35017383702219373 -5.761112126903864 8.356150292016951e-09 -8.0779937574894 1.0 1.0 1.0 1.0 s__Haemophilus_parainfluenzae 6 | s__Prevotella_melaninogenica 0.013477531694454245 0.16173121734427978 -11.622282164155344 3.1753890087019113e-31 -30.498203062818064 1.0 1.0 1.0 1.0 s__Prevotella_melaninogenica 7 | s__Rothia_mucilaginosa 0.05087954107728926 0.13706606805385468 -8.073561610493218 6.827670022642209e-16 -15.165727476252172 1.0 1.0 1.0 1.0 s__Rothia_mucilaginosa 8 | s__Streptococcus_mitis 0.6931068257045447 0.01672800864812219 12.609520212918492 1.8712872006902566e-36 35.72785955290465 1.0 1.0 1.0 1.0 s__Streptococcus_mitis 9 | s__Streptococcus_sanguinis 0.025518241266430316 0.0038204413798713674 9.563312720113284 1.1404650624488597e-21 20.942918014568026 1.0 1.0 1.0 1.0 s__Streptococcus_sanguinis 10 | s__Veillonella_atypica 0.007658508781594692 0.07139870275712581 -9.974661907097929 1.9677044722970736e-23 -22.706040127290976 1.0 1.0 1.0 1.0 s__Veillonella_atypica 11 | s__Veillonella_dispar 0.00456204374569069 0.05765179102897155 -11.306543869280643 1.2179297905994872e-29 -28.914377746542282 1.0 1.0 1.0 1.0 s__Veillonella_dispar 12 | s__Veillonella_unclassified 0.009555249306632402 0.10013101361760238 -10.757337116928172 5.472879263730797e-27 -26.261784132649357 1.0 1.0 1.0 1.0 s__Veillonella_unclassified 13 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | # test_fishtaco.py {'case_label': '1', 'use_t2f_as_prior': False, 'taxa_to_function_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'permutation_mode': 'blocks', 'write_log': True, 'score_to_compute': 'wilcoxon', 'control_label': '0', 'residual_mode': 'remove_residual', 'number_of_permutations': '5', 'max_score_cutoff': '100', 'class_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'number_of_shapley_orderings_per_taxa': '3', 'max_da_functions_cases_controls': '1', 'function_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'na_rep': 'NA', 'da_result_file': None, 'taxa_abun_file': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'output_pref': '/net/gs/vol1/home/ohadm/METAFIT/PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'function_da_threshold': 'Bonf', 'taxa_assessment_method': 'permuted_shapley_orderings', 'single_function_filter': 'K00001'} 2 | Taxa meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | s__Actinomyces_odontolyticus 0.004788111982456582 0.03165727519296322 -11.637846727987196 2.6461190502437123e-31 -30.577390620579045 1.0 1.0 1.0 1.0 s__Actinomyces_odontolyticus 4 | s__Campylobacter_concisus 0.007429160844587094 0.06964164495501507 -11.28653228721112 1.529527506449628e-29 -28.815442708419823 1.0 1.0 1.0 1.0 s__Campylobacter_concisus 5 | s__Haemophilus_parainfluenzae 0.18302478559632007 0.35017383702219373 -5.761112126903864 8.356150292016951e-09 -8.0779937574894 1.0 1.0 1.0 1.0 s__Haemophilus_parainfluenzae 6 | s__Prevotella_melaninogenica 0.013477531694454245 0.16173121734427978 -11.622282164155344 3.1753890087019113e-31 -30.498203062818064 1.0 1.0 1.0 1.0 s__Prevotella_melaninogenica 7 | s__Rothia_mucilaginosa 0.05087954107728926 0.13706606805385468 -8.073561610493218 6.827670022642209e-16 -15.165727476252172 1.0 1.0 1.0 1.0 s__Rothia_mucilaginosa 8 | s__Streptococcus_mitis 0.6931068257045447 0.01672800864812219 12.609520212918492 1.8712872006902566e-36 35.72785955290465 1.0 1.0 1.0 1.0 s__Streptococcus_mitis 9 | s__Streptococcus_sanguinis 0.025518241266430316 0.0038204413798713674 9.563312720113284 1.1404650624488597e-21 20.942918014568026 1.0 1.0 1.0 1.0 s__Streptococcus_sanguinis 10 | s__Veillonella_atypica 0.007658508781594692 0.07139870275712581 -9.974661907097929 1.9677044722970736e-23 -22.706040127290976 1.0 1.0 1.0 1.0 s__Veillonella_atypica 11 | s__Veillonella_dispar 0.00456204374569069 0.05765179102897155 -11.306543869280643 1.2179297905994872e-29 -28.914377746542282 1.0 1.0 1.0 1.0 s__Veillonella_dispar 12 | s__Veillonella_unclassified 0.009555249306632402 0.10013101361760238 -10.757337116928172 5.472879263730797e-27 -26.261784132649357 1.0 1.0 1.0 1.0 s__Veillonella_unclassified 13 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'max_da_functions_cases_controls': None, 'taxa_assessment_method': 'single_taxa', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_permutations': '100', 'control_label': '0', 'map_function_file': None, 'da_result_file': None, 'apply_inference': False, 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'na_rep': 'NA', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'perform_inference_on_ko_level': False, 'write_log': True, 'map_function_level': 'none', 'case_label': '1', 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'permutation_mode': 'blocks', 'single_function_filter': None, 'max_score_cutoff': '100', 'number_of_shapley_orderings_per_taxa': '5', 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_no_inf', 'normalization_mode': 'scale_permuted', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab'} 2 | Taxa meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | s__Actinomyces_odontolyticus 0.004788111982456582 0.03165727519296322 -11.637846727987196 2.6461190502437123e-31 -30.577390620579045 1.0 1.0 1.0 1.0 s__Actinomyces_odontolyticus 4 | s__Campylobacter_concisus 0.007429160844587094 0.06964164495501507 -11.28653228721112 1.529527506449628e-29 -28.815442708419823 1.0 1.0 1.0 1.0 s__Campylobacter_concisus 5 | s__Haemophilus_parainfluenzae 0.18302478559632007 0.35017383702219373 -5.761112126903864 8.356150292016951e-09 -8.0779937574894 1.0 1.0 1.0 1.0 s__Haemophilus_parainfluenzae 6 | s__Prevotella_melaninogenica 0.013477531694454245 0.16173121734427978 -11.622282164155344 3.1753890087019113e-31 -30.498203062818064 1.0 1.0 1.0 1.0 s__Prevotella_melaninogenica 7 | s__Rothia_mucilaginosa 0.05087954107728926 0.13706606805385468 -8.073561610493218 6.827670022642209e-16 -15.165727476252172 1.0 1.0 1.0 1.0 s__Rothia_mucilaginosa 8 | s__Streptococcus_mitis 0.6931068257045447 0.01672800864812219 12.609520212918492 1.8712872006902566e-36 35.72785955290465 1.0 1.0 1.0 1.0 s__Streptococcus_mitis 9 | s__Streptococcus_sanguinis 0.025518241266430316 0.0038204413798713674 9.563312720113284 1.1404650624488597e-21 20.942918014568026 1.0 1.0 1.0 1.0 s__Streptococcus_sanguinis 10 | s__Veillonella_atypica 0.007658508781594692 0.07139870275712581 -9.974661907097929 1.9677044722970736e-23 -22.706040127290976 1.0 1.0 1.0 1.0 s__Veillonella_atypica 11 | s__Veillonella_dispar 0.00456204374569069 0.05765179102897155 -11.306543869280643 1.2179297905994872e-29 -28.914377746542282 1.0 1.0 1.0 1.0 s__Veillonella_dispar 12 | s__Veillonella_unclassified 0.009555249306632402 0.10013101361760238 -10.757337116928172 5.472879263730797e-27 -26.261784132649357 1.0 1.0 1.0 1.0 s__Veillonella_unclassified 13 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | # /net/gs/vol3/software/modules-sw-python/3.3.3/fishtaco/1.0.1/Linux/RHEL6/x86_64/bin/run_fishtaco.py {'class_file': 'PyCode/FiShTaCo/fishtaco/examples/SAMPLE_vs_CLASS.tab', 'taxa_assessment_method': 'single_taxa', 'score_to_compute': 'wilcoxon', 'residual_mode': 'remove_residual', 'taxa_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab', 'taxa_to_function_file': 'PyCode/FiShTaCo/fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab', 'functional_profile_already_corrected_with_musicc': True, 'apply_inference': True, 'function_abun_file': 'PyCode/FiShTaCo/fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab', 'normalization_mode': 'scale_permuted', 'da_result_file': None, 'write_log': True, 'map_function_level': 'none', 'number_of_permutations': '100', 'case_label': '1', 'map_function_file': None, 'output_pref': 'PyCode/FiShTaCo/fishtaco/examples/output/fishtaco_out_prior_based_inf', 'max_score_cutoff': '100', 'permutation_mode': 'blocks', 'max_da_functions_cases_controls': None, 'na_rep': 'NA', 'single_function_filter': None, 'perform_inference_on_ko_level': False, 'control_label': '0', 'multiple_hypothesis_correction': 'FDR-0.05', 'number_of_shapley_orderings_per_taxa': '5'} 2 | Taxa meanCases meanControls StatValue pval singLogP Bonf FDR-0.01 FDR-0.05 FDR-0.1 Metadata 3 | s__Actinomyces_odontolyticus 0.004788111982456582 0.03165727519296322 -11.637846727987196 2.6461190502437123e-31 -30.577390620579045 1.0 1.0 1.0 1.0 s__Actinomyces_odontolyticus 4 | s__Campylobacter_concisus 0.007429160844587094 0.06964164495501507 -11.28653228721112 1.529527506449628e-29 -28.815442708419823 1.0 1.0 1.0 1.0 s__Campylobacter_concisus 5 | s__Haemophilus_parainfluenzae 0.18302478559632007 0.35017383702219373 -5.761112126903864 8.356150292016951e-09 -8.0779937574894 1.0 1.0 1.0 1.0 s__Haemophilus_parainfluenzae 6 | s__Prevotella_melaninogenica 0.013477531694454245 0.16173121734427978 -11.622282164155344 3.1753890087019113e-31 -30.498203062818064 1.0 1.0 1.0 1.0 s__Prevotella_melaninogenica 7 | s__Rothia_mucilaginosa 0.05087954107728926 0.13706606805385468 -8.073561610493218 6.827670022642209e-16 -15.165727476252172 1.0 1.0 1.0 1.0 s__Rothia_mucilaginosa 8 | s__Streptococcus_mitis 0.6931068257045447 0.01672800864812219 12.609520212918492 1.8712872006902566e-36 35.72785955290465 1.0 1.0 1.0 1.0 s__Streptococcus_mitis 9 | s__Streptococcus_sanguinis 0.025518241266430316 0.0038204413798713674 9.563312720113284 1.1404650624488597e-21 20.942918014568026 1.0 1.0 1.0 1.0 s__Streptococcus_sanguinis 10 | s__Veillonella_atypica 0.007658508781594692 0.07139870275712581 -9.974661907097929 1.9677044722970736e-23 -22.706040127290976 1.0 1.0 1.0 1.0 s__Veillonella_atypica 11 | s__Veillonella_dispar 0.00456204374569069 0.05765179102897155 -11.306543869280643 1.2179297905994872e-29 -28.914377746542282 1.0 1.0 1.0 1.0 s__Veillonella_dispar 12 | s__Veillonella_unclassified 0.009555249306632402 0.10013101361760238 -10.757337116928172 5.472879263730797e-27 -26.261784132649357 1.0 1.0 1.0 1.0 s__Veillonella_unclassified 13 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | FishTaco Software License Agreement 2 | 3 | FishTaco (C) 2014-2016, University of Washington. All rights reserved. 4 | 5 | Subject to the terms below, the University of Washington ("UW"), Professor Elhanan Borenstein, and Ohad Manor ("Developer(s)") give permission for you and other members of your laboratory for as long as they remain members ("Academic User(s)"), such permission granted solely to Academic Users in a nonprofit institution of higher education or a nonprofit research institution ("University"), to use FishTaco solely as further detailed below. FishTaco is a computational framework for linking microbiome taxonomic and functional comparative analyses, for decomposing functional shifts observed in a comparative analysis into individual taxon-level contributions, and for identifying taxa that drive functional shifts in the microbiome. FishTaco is protected by a copyright. The National Institutes of Health supported work on FishTaco. The UW and the Developers allow Academic Users to perform, copy, and modify FishTaco, solely for internal, non-profit academic research purposes, and as long as Academic Users comply with the terms of this FishTaco Software License Agreement: 6 | 7 | 1. FishTaco is not used for any commercial purposes, or as part of a system which has commercial purposes. The FishTaco software remains at your University and is not published, distributed, or otherwise transferred or made available to other than Academic Users. 8 | 9 | 2. You may not distribute FishTaco or any modification to FishTaco to any third party. 10 | 11 | If you wish to obtain FishTaco software for any commercial purposes, you will need to contact the University of Washington to see if rights are available and to negotiate a commercial license and pay a fee among other requirements. This includes, but is not limited to, using FishTaco to provide services to outside parties for a fee. In that case please contact: 12 | 13 | UW CoMotion 14 | University of Washington 15 | 4311 11th Ave. NE, 16 | Suite 500 Seattle, WA 98105-4608 17 | Phone: (206) 543-3970 18 | Email: license@uw.edu 19 | 20 | 3. You retain in FishTaco and any modifications to FishTaco, the copyright, trademark, patent or other notices pertaining to FishTaco as provided by UW. 21 | 22 | 4. You acknowledge that the Developers, the UW and its licensees may develop modifications to FishTaco that may be substantially similar to your modifications of FishTaco, and that the Developers, UW and its licensees shall not be constrained in any way by you in UW's or its licensees' use or management of such modifications. You acknowledge the right of the Developers and UW to prepare and publish modifications to FishTaco that may be substantially similar or functionally equivalent to your modifications and improvements, and if you obtain patent protection for any modification or improvement to FishTaco you agree not to allege or enjoin infringement of your patent by the Developers, the UW or by any of UW's licensees obtaining modifications or improvements to FishTaco from the University of Washington or the Developers. 23 | 24 | 5. If utilization of the FishTaco software results in outcomes which will be published, you will specify the version of FishTaco you used and cite the UW Developers. 25 | 26 | 6. Any risk associated with using the FishTaco software at your organization is with you and your organization. FishTaco is experimental in nature and is made available as a research courtesy "AS IS," expressly without any obligation by UW to provide accompanying services or support. 27 | 28 | 7. UW AND THE DEVELOPERS EXPRESSLY DISCLAIM ANY AND ALL WARRANTIES REGARDING THE SOFTWARE, WHETHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES PERTAINING TO MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. 29 | 30 | 8. This Software License Agreement and all rights granted under it terminate on December 31st, 2020. Upon termination, you agree to remove so as to make unrecoverable the original FishTaco software, all copies and all modifications thereof. 31 | -------------------------------------------------------------------------------- /fishtaco/examples/SAMPLE_vs_CLASS.tab: -------------------------------------------------------------------------------- 1 | Sample Site 2 | SRS011090 1 3 | SRS011144 1 4 | SRS011247 1 5 | SRS011310 1 6 | SRS012281 1 7 | SRS013239 1 8 | SRS013506 1 9 | SRS013711 1 10 | SRS013825 1 11 | SRS013881 1 12 | SRS013945 1 13 | SRS014126 1 14 | SRS014472 1 15 | SRS014575 1 16 | SRS014686 1 17 | SRS014890 1 18 | SRS015040 1 19 | SRS015059 1 20 | SRS015154 1 21 | SRS015274 1 22 | SRS015374 1 23 | SRS015436 1 24 | SRS015646 1 25 | SRS015745 1 26 | SRS015799 1 27 | SRS015895 1 28 | SRS015921 1 29 | SRS016039 1 30 | SRS016088 1 31 | SRS016196 1 32 | SRS016297 1 33 | SRS016349 1 34 | SRS016503 1 35 | SRS016533 1 36 | SRS016600 1 37 | SRS017013 1 38 | SRS017080 1 39 | SRS017127 1 40 | SRS017215 1 41 | SRS017441 1 42 | SRS017537 1 43 | SRS017687 1 44 | SRS017810 1 45 | SRS018149 1 46 | SRS018329 1 47 | SRS018359 1 48 | SRS018661 1 49 | SRS018971 1 50 | SRS019024 1 51 | SRS019073 1 52 | SRS019124 1 53 | SRS019221 1 54 | SRS019329 1 55 | SRS019391 1 56 | SRS019587 1 57 | SRS019872 1 58 | SRS019976 1 59 | SRS020336 1 60 | SRS020858 1 61 | SRS021473 1 62 | SRS022079 1 63 | SRS022145 1 64 | SRS022532 1 65 | SRS022625 1 66 | SRS022721 1 67 | SRS023354 1 68 | SRS023534 1 69 | SRS023591 1 70 | SRS023837 1 71 | SRS023987 1 72 | SRS024017 1 73 | SRS024140 1 74 | SRS024347 1 75 | SRS024377 1 76 | SRS024470 1 77 | SRS024557 1 78 | SRS042457 1 79 | SRS043646 1 80 | SRS043676 1 81 | SRS045049 1 82 | SRS045254 1 83 | SRS045262 1 84 | SRS045978 1 85 | SRS046623 1 86 | SRS046686 1 87 | SRS048719 1 88 | SRS049283 1 89 | SRS050007 1 90 | SRS050029 1 91 | SRS050628 1 92 | SRS051116 1 93 | SRS052668 1 94 | SRS052874 1 95 | SRS054569 1 96 | SRS054776 1 97 | SRS055118 1 98 | SRS056892 1 99 | SRS057022 1 100 | SRS058105 1 101 | SRS063272 1 102 | SRS063287 1 103 | SRS063478 1 104 | SRS064809 1 105 | SRS065133 1 106 | SRS065431 1 107 | SRS075406 1 108 | SRS077738 1 109 | SRS011140 0 110 | SRS011243 0 111 | SRS011306 0 112 | SRS012279 0 113 | SRS013164 0 114 | SRS013234 0 115 | SRS013502 0 116 | SRS013705 0 117 | SRS013818 0 118 | SRS013879 0 119 | SRS014124 0 120 | SRS014271 0 121 | SRS014470 0 122 | SRS014573 0 123 | SRS014684 0 124 | SRS014888 0 125 | SRS015038 0 126 | SRS015057 0 127 | SRS015209 0 128 | SRS015272 0 129 | SRS015395 0 130 | SRS015434 0 131 | SRS015537 0 132 | SRS015644 0 133 | SRS015762 0 134 | SRS015797 0 135 | SRS015893 0 136 | SRS015941 0 137 | SRS016002 0 138 | SRS016037 0 139 | SRS016086 0 140 | SRS016225 0 141 | SRS016319 0 142 | SRS016342 0 143 | SRS016501 0 144 | SRS016529 0 145 | SRS016569 0 146 | SRS017120 0 147 | SRS017209 0 148 | SRS017439 0 149 | SRS017533 0 150 | SRS017713 0 151 | SRS017808 0 152 | SRS018145 0 153 | SRS018300 0 154 | SRS018357 0 155 | SRS018439 0 156 | SRS018591 0 157 | SRS018739 0 158 | SRS018791 0 159 | SRS018969 0 160 | SRS019022 0 161 | SRS019045 0 162 | SRS019071 0 163 | SRS019122 0 164 | SRS019219 0 165 | SRS019327 0 166 | SRS019389 0 167 | SRS019607 0 168 | SRS019894 0 169 | SRS019974 0 170 | SRS020220 0 171 | SRS020334 0 172 | SRS020856 0 173 | SRS021496 0 174 | SRS021954 0 175 | SRS022077 0 176 | SRS022143 0 177 | SRS022530 0 178 | SRS022621 0 179 | SRS022719 0 180 | SRS023352 0 181 | SRS023557 0 182 | SRS023617 0 183 | SRS023835 0 184 | SRS023926 0 185 | SRS023958 0 186 | SRS024015 0 187 | SRS024081 0 188 | SRS024138 0 189 | SRS024277 0 190 | SRS024318 0 191 | SRS024375 0 192 | SRS024441 0 193 | SRS024580 0 194 | SRS024637 0 195 | SRS042131 0 196 | SRS042643 0 197 | SRS042910 0 198 | SRS043663 0 199 | SRS044373 0 200 | SRS044486 0 201 | SRS044662 0 202 | SRS045127 0 203 | SRS045715 0 204 | SRS047210 0 205 | SRS047219 0 206 | SRS047824 0 207 | SRS048411 0 208 | SRS048791 0 209 | SRS049147 0 210 | SRS049389 0 211 | SRS050244 0 212 | SRS050669 0 213 | SRS051791 0 214 | SRS052227 0 215 | SRS053603 0 216 | SRS053854 0 217 | SRS054687 0 218 | SRS055426 0 219 | SRS056323 0 220 | SRS056622 0 221 | SRS057205 0 222 | SRS057355 0 223 | SRS057539 0 224 | SRS057692 0 225 | SRS057791 0 226 | SRS058336 0 227 | SRS062540 0 228 | SRS062544 0 229 | SRS062761 0 230 | SRS063193 0 231 | SRS063288 0 232 | SRS063932 0 233 | SRS064423 0 234 | SRS064774 0 235 | SRS065278 0 236 | SRS077736 0 237 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_predict_function_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.957281545671 0.0683431978022 0.749280393392 0.0272206659879 0.824980917187 0.0188342195256 0.856649468324 0.0106722923938 0.877079983613 0.0487374407121 0.0250351001845 0.740622225676 0.0433659254423 0.909497092858 0.00735449186593 0.439156183729 0.0269399991108 0.542394400614 0.041940488539 0.653282086979 0.156310011233 0.0162246627418 0.974379825418 0.0137367254649 0.95894144295 0.0282969914499 0.384254674003 0.0223823318571 0.654457384946 0.0111623901088 0.84987213934 0.806133986871 0.624585515895 0.39958410782 0.00485399238945 0.00830201536583 0.718104796701 0.69024600582 0.0223507378894 0.0166224530152 0.725146371681 0.00933153768839 0.0178404178655 0.967579725667 0.404276505282 0.00739266561476 0.660495975766 0.70014435947 0.612713351684 0.0196026618223 0.0157771783683 0.0113571090965 0.598895351304 0.00473125563941 0.732156293641 0.919203386165 0.00768163828842 0.698119813323 0.032805461064 0.717622765525 0.0274773295271 0.431091198679 0.0253411968722 0.410088513541 0.0186269526271 0.928717777576 0.676394428155 0.315637808845 0.978659554384 0.0133463468827 0.943656049385 0.0154487962741 0.926927004961 0.066030922541 0.947086751443 0.853577654988 0.0297863708643 0.0159057503612 0.729598998939 0.00896197796052 0.855622548931 0.906711104907 0.0161353027364 0.585267333598 0.0119070491904 0.00452223163454 0.547647594871 0.00397527315223 0.0240809112203 0.313885351957 0.0279578446466 0.0275613062951 0.676578436479 0.00816905862843 0.721749184591 0.884135168426 0.0135775648808 0.528801862979 0.0199811665584 0.819458456494 0.20972804708 0.0102618793012 0.594785707899 0.00816371234967 0.0218084813811 0.665488762626 0.02239578563 0.0661158412669 0.848065861339 0.0152049661553 0.89321530111 0.861530124918 0.01388695752 0.0189570482837 0.00990764625582 0.495888778196 0.893088492882 0.0212097970634 0.936323306867 0.0172213211442 0.790136779186 0.0188828769476 0.762940105198 0.955994192927 0.442632250853 0.0106852886312 0.857455161543 0.0132700874725 0.0273254073723 0.856013558092 0.01364027364 0.0205320866584 0.645092890841 0.0275738599027 0.692466160012 0.00864587250766 0.00988472237854 0.835965670408 0.0358139862544 0.860406357827 0.0238753317789 0.977556402794 0.0104952153746 0.572382445494 0.771440344639 0.0186559096117 0.0329663399807 0.02182536852 0.655140568204 0.834571537362 0.011898127248 0.598913350751 0.0080815508562 0.0198448913261 0.0197207116794 0.723029699686 0.0325077921051 0.726275114616 0.752401893296 0.00988911449661 0.555911401831 0.788185442483 0.921745482704 0.00897071299807 0.0100517885007 0.794375449296 0.0200958127732 0.0197916845565 0.627863832629 0.0082779298073 0.545682331255 0.130932699247 0.785057764372 0.0114492952593 0.946647511355 0.00601430795641 0.0270373078889 0.866054611424 0.799705960532 0.0203953898948 0.00556787151443 0.849534752335 0.0161807216492 0.720518590429 0.863756029612 0.030182282697 0.012080552017 0.0111325465914 0.474595618855 0.684434611471 0.0171946383879 0.00886070012216 0.0106540761651 0.0207105163891 0.00987727523537 0.769972427536 0.0074871664712 0.0829092329534 0.0188114331646 0.00903629538801 0.851100948817 0.2511141343 0.0276889750066 0.199567932438 0.00799146472995 0.0100524477824 0.743026519524 0.342596456215 0.00730418542146 0.811333033624 0.898144225982 0.0275049563988 0.855281777538 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 1.0022425727574404 0.5866609125919453 0.7943993494914254 0.3838908796772683 0.9144838519602992 0.35179330118972546 0.9257922177295494 0.389702031022116 0.9218608787696281 0.3509986454494675 0.75403507121623 0.8607349249155576 0.4952387031141494 0.9763026717702222 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/fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 1.0022425727574404 0.5866609125919453 0.7943993494914254 0.3838908796772683 0.9144838519602992 0.35179330118972546 0.9257922177295494 0.389702031022116 0.9218608787696281 0.3509986454494675 0.75403507121623 0.8607349249155576 0.4952387031141494 0.9763026717702222 0.25886417734848904 0.5670276290924465 0.46958055748248884 0.8605050229269635 0.4884887732461623 0.7889326957934195 0.534374338839188 0.5342358403979002 1.0241623495205012 0.3293865356528895 1.0100879694382618 0.4099337110846184 0.6191736483418979 0.4413663432366666 0.8342361530250441 0.6386174339742033 0.9302084792833842 0.9170473399048432 0.9524331169779499 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0.270326237849376 0.6617337912978816 0.28539580057440145 0.6531543320643209 0.7049111995928279 0.809444597591094 0.7973591703030315 0.7807263326008147 0.30599666300415473 0.8240697573488368 0.9408840946978202 0.3490977628997792 0.6327531829274721 0.4338548419118987 0.9156514232288965 0.5477584894100189 0.4227030058457384 0.7766269625549506 0.4005038739667515 0.4478733112533475 0.7874047164317689 0.3605944486544333 0.5972268553120572 0.9466635926023711 0.33183563015814754 0.9589990826709808 0.9321138974593143 0.29442564820020495 0.46731595676187787 0.3978741913994651 0.6804452159098735 0.950244479112157 0.4286388081364925 0.9910109904864997 0.35843526033664985 0.8756411599936813 0.395053886341909 0.855458909342916 1.0152611445508546 0.6391921117695422 0.5184050482174063 0.9111113803963861 0.32573887273726965 0.5234122259706225 0.9301866356839714 0.37750979622734004 0.3573765100841482 0.7734374585167607 0.5532889966801281 0.7952556958188438 0.3296443050577037 0.31330962207239116 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0.8122360614901052 0.36499481549732077 0.3950564158417603 0.3917955391414588 0.33657391249406465 0.4143909306822872 0.8676232284576214 0.45259536280576207 0.6720675599736818 0.3458825937414579 0.573424577035565 0.9463059825331559 1.1119300824241172 0.5046080703971758 0.5333194394113286 0.34430534382688294 0.3595279170286515 0.8502982940096185 0.6943640292059645 0.35518694740458584 0.8667153219312032 0.8958936235476647 0.6691538823367595 1.0122857546816568 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 1.0022425727574404 0.5866609125919453 0.7943993494914254 0.3838908796772683 0.9144838519602992 0.35179330118972546 0.9257922177295494 0.389702031022116 0.9218608787696281 0.3509986454494675 0.75403507121623 0.8607349249155576 0.4952387031141494 0.9763026717702222 0.25886417734848904 0.5670276290924465 0.46958055748248884 0.8605050229269635 0.4884887732461623 0.7889326957934195 0.534374338839188 0.5342358403979002 1.0241623495205012 0.3293865356528895 1.0100879694382618 0.4099337110846184 0.6191736483418979 0.4413663432366666 0.8342361530250441 0.6386174339742033 0.9302084792833842 0.9170473399048432 0.9524331169779499 0.7256030749967086 0.3630213189656325 0.4388159005982769 0.8485001471692395 0.8005341699505725 0.8665805023067972 0.37559313513407483 0.8316264300686583 0.691965824417527 0.6471098430521868 1.0164198783913732 0.6479861711648774 0.3675854471212912 0.7971840670939612 0.8777270894211517 0.9081634183366502 0.4105276878609346 0.40863272585880145 0.4734271043684658 0.6576501475016329 0.3961002925419793 0.8626450507402386 0.973600391987765 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1.0122857546816568 3 | -------------------------------------------------------------------------------- /fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab: -------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 SRS057355 SRS057539 SRS057692 SRS057791 SRS058105 SRS058336 SRS062540 SRS062761 SRS063193 SRS063272 SRS063287 SRS063288 SRS063478 SRS064423 SRS064774 SRS064809 SRS065133 SRS065278 SRS065431 SRS075406 SRS077736 SRS077738 2 | K00001 0.961070998014416 0.07098082445414398 0.7788527705055488 0.028275267369770977 0.8274587176039184 0.019465800141939334 0.8598174154031968 0.011392489902620274 0.8933898536220192 0.0494940845623656 0.0280119712517313 0.7417449022099941 0.04654062521933078 0.9118499251929508 0.00808087281558385 0.44864832819421613 0.028768262936796195 0.5481928555744217 0.04319164577880451 0.6562531442148754 0.16709271613358562 0.016897463951740438 0.9752093911496034 0.014476259429387447 0.9601185445656839 0.02949067423222522 0.39367365933062565 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-------------------------------------------------------------------------------- 1 | KO SRS011090 SRS011140 SRS011144 SRS011243 SRS011247 SRS011306 SRS011310 SRS012279 SRS012281 SRS013164 SRS013234 SRS013239 SRS013502 SRS013506 SRS013705 SRS013711 SRS013818 SRS013825 SRS013879 SRS013881 SRS013945 SRS014124 SRS014126 SRS014470 SRS014472 SRS014573 SRS014575 SRS014684 SRS014686 SRS014888 SRS014890 SRS015040 SRS015059 SRS015154 SRS015209 SRS015272 SRS015274 SRS015374 SRS015395 SRS015434 SRS015436 SRS015537 SRS015644 SRS015646 SRS015745 SRS015797 SRS015799 SRS015895 SRS015921 SRS015941 SRS016002 SRS016037 SRS016039 SRS016086 SRS016088 SRS016196 SRS016225 SRS016297 SRS016342 SRS016349 SRS016501 SRS016503 SRS016529 SRS016533 SRS016569 SRS016600 SRS017013 SRS017080 SRS017127 SRS017209 SRS017215 SRS017439 SRS017441 SRS017533 SRS017537 SRS017687 SRS017713 SRS017808 SRS017810 SRS018145 SRS018149 SRS018329 SRS018357 SRS018359 SRS018439 SRS018591 SRS018661 SRS018791 SRS018969 SRS018971 SRS019022 SRS019045 SRS019073 SRS019122 SRS019124 SRS019221 SRS019327 SRS019329 SRS019389 SRS019391 SRS019587 SRS019607 SRS019872 SRS019894 SRS019974 SRS019976 SRS020220 SRS020334 SRS020336 SRS020856 SRS020858 SRS021473 SRS021496 SRS021954 SRS022077 SRS022079 SRS022145 SRS022530 SRS022532 SRS022621 SRS022625 SRS022719 SRS022721 SRS023354 SRS023534 SRS023557 SRS023591 SRS023617 SRS023835 SRS023837 SRS023926 SRS023958 SRS023987 SRS024015 SRS024017 SRS024081 SRS024138 SRS024140 SRS024277 SRS024347 SRS024375 SRS024377 SRS024441 SRS024470 SRS024557 SRS024580 SRS024637 SRS042131 SRS042457 SRS043646 SRS043663 SRS043676 SRS044373 SRS044486 SRS044662 SRS045049 SRS045127 SRS045254 SRS045262 SRS045715 SRS045978 SRS046623 SRS046686 SRS047210 SRS047824 SRS048719 SRS048791 SRS049147 SRS049283 SRS049389 SRS050007 SRS050029 SRS050628 SRS050669 SRS051116 SRS051791 SRS052227 SRS052668 SRS052874 SRS053603 SRS053854 SRS054569 SRS054687 SRS054776 SRS055118 SRS055426 SRS056323 SRS056622 SRS056892 SRS057022 SRS057205 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0.2073963991589456 0.008590437620383062 0.011012272660662853 0.7512355656425865 0.3448042260245121 0.007903645005707187 0.8241552166460957 0.928452438720213 0.029646806456527966 0.8665413725674511 3 | -------------------------------------------------------------------------------- /fishtaco/learn_non_neg_elastic_net_with_prior.py: -------------------------------------------------------------------------------- 1 | """ 2 | A module to learns a cross-validated Non Negative Elastic Net model with a 3 | Prior from given features, and evaluate a given model on test data 4 | 5 | functions 6 | ---------- 7 | learn(cov_train, res_train, params={}): 8 | Learns a cross-validation Non Negative Elastic Net model 9 | 10 | test(enet_model, cov_test, res_test, params={}): 11 | Tests an Elastic Net model on test data 12 | 13 | notes 14 | ---------- 15 | - does NOT normalize/centers the covariate matrix, so it needs to be 16 | normalized/centered before using the functions of this module 17 | - does NOT center the response, so it needs to be centered before sending to 18 | function 19 | 20 | """ 21 | 22 | from __future__ import absolute_import, division, print_function 23 | import warnings 24 | import numpy as np 25 | from sklearn.model_selection import KFold 26 | from sklearn.linear_model import enet_path, ElasticNet 27 | 28 | __author__ = 'Ohad Manor' 29 | __email__ = 'omanor@gmail.com' 30 | __status__ = "Development" 31 | 32 | def learn(cov_train, res_train, params={}): 33 | """ 34 | Learns a cross-validation Non Negative Elastic Net model with a Prior 35 | from given features. 36 | 37 | Parameters 38 | ---------- 39 | cov_train: 40 | the covariate matrix of size NxP, with N samples and P covariates 41 | 42 | res_train: 43 | the response vector of size N, with N samples 44 | 45 | params: dictionary 46 | a dictionary containing optional params 47 | params.['covariates_prior']: a vector of priors for the different 48 | features in the range [0,1] 49 | params.['class_subfeatures']: a vector of binary case/control labels 50 | for the samples, dividing them to two classes. If this parameter is 51 | given when learning, 2*Pcovariates will be created. The P covariates 52 | are zero for controls and copied from cov_train for the cases, 53 | and the second P covariates are zero for cases and copied from 54 | cov_train for the controls. This matrix will replace the original, 55 | so the returned model will have 2*P weights 56 | params.['num_cv']: the number of folds in the internal cross-validation 57 | (default: 5) 58 | params.['l1_ratio']: the ratio of L1 penalty in the regularization in 59 | the range [0,1] (default: 0.5) 60 | 61 | Returns 62 | ------- 63 | enet: 64 | the elastic-net model 65 | 66 | best_validation_rsqr: real 67 | the value of the best validation r^2 for which the final model was fit 68 | 69 | """ 70 | # nicer output 71 | np.set_printoptions(precision=2, suppress=False, linewidth=200) 72 | 73 | # update train covariates by the given params 74 | if 'covariates_prior' in params.keys() and params['covariates_prior'] \ 75 | is not None: 76 | cov_train = cov_train * params['covariates_prior'] 77 | 78 | if 'class_subfeatures' in params.keys() and params['class_subfeatures'] \ 79 | is not None: 80 | # cases subfeatures 81 | cases = params['class_subfeatures'] > 0 82 | case_subfeatures = np.zeros_like(cov_train) 83 | case_subfeatures[cases, :] = cov_train[cases, :] 84 | # control subfeatures 85 | controls = params['class_subfeatures'] == 0 86 | control_subfeatures = np.zeros_like(cov_train) 87 | control_subfeatures[controls, :] = cov_train[controls, :] 88 | # update cov_train 89 | cov_train = np.hstack((case_subfeatures, control_subfeatures)) 90 | 91 | # more params 92 | if 'num_cv' in params.keys(): 93 | num_cv = params['num_cv'] 94 | else: 95 | num_cv = 5 96 | 97 | if 'l1_ratio' in params.keys(): 98 | l1_ratio = params['l1_ratio'] 99 | else: 100 | l1_ratio = 0.5 101 | 102 | k_fold = KFold(n_splits=num_cv, shuffle=True).split(cov_train, res_train) 103 | 104 | best_validation_rsqr = np.zeros(num_cv) 105 | best_validation_alpha = np.zeros(num_cv) 106 | 107 | for inner_k, (inner_train, inner_validation) in enumerate(k_fold): 108 | cov_inner_train = cov_train[inner_train, :] 109 | cov_inner_validation = cov_train[inner_validation, :] 110 | response_inner_train = res_train[inner_train] 111 | response_inner_validation = res_train[inner_validation] 112 | 113 | alphas_positive_enet, coefs_positive_enet, \ 114 | _ = enet_path(cov_inner_train, response_inner_train, 115 | l1_ratio=l1_ratio, fit_intercept=False, 116 | normalize=False, positive=True, 117 | return_models=False) 118 | 119 | num_alphas = len(alphas_positive_enet) 120 | 121 | prediction_validation = np.dot(coefs_positive_enet.transpose(), 122 | cov_inner_validation.transpose()) 123 | 124 | rep_res_val = np.repeat(response_inner_validation, 125 | num_alphas).reshape(len( 126 | response_inner_validation), num_alphas).transpose() 127 | rep_mean_val = np.repeat(np.mean(response_inner_validation), 128 | len(response_inner_validation)*num_alphas).\ 129 | reshape(len(response_inner_validation), num_alphas).transpose() 130 | 131 | sos_residual = np.sum((prediction_validation-rep_res_val) ** 2, axis=1) 132 | sos_original = np.sum((rep_res_val - rep_mean_val) ** 2, axis=1) 133 | 134 | rep_validation_rsqr = np.array(1 - (sos_residual / sos_original)) 135 | 136 | sorted_ind = np.argsort(rep_validation_rsqr)[::-1] 137 | best_validation_rsqr[inner_k] = rep_validation_rsqr[sorted_ind[0]] 138 | best_validation_alpha[inner_k] = alphas_positive_enet[sorted_ind[0]] 139 | 140 | mean_best_alpha = np.mean(best_validation_alpha) 141 | 142 | # now learn one unified model on the given data using the mean_best_alpha 143 | enet = ElasticNet(l1_ratio=l1_ratio, alpha=mean_best_alpha, 144 | fit_intercept=False, normalize=False, 145 | positive=True) 146 | 147 | enet.fit(cov_train, res_train) 148 | 149 | return enet, best_validation_rsqr 150 | 151 | 152 | def test(enet_model, cov_test, res_test, params={}): 153 | """ 154 | Evaluates a given Elastic Net model on test data 155 | 156 | Parameters 157 | ---------- 158 | enet_model: 159 | the given elastic-net model to evaludate 160 | 161 | cov_test: 162 | the covariate matrix of size NxP, with N samples and P covariates 163 | 164 | res_test: 165 | the response vector of size N, with N samples 166 | 167 | params: dictionary 168 | a dictionary containing optional params 169 | params.['covariates_prior']: a vector of priors for the different 170 | features in the range [0,1] 171 | params.['class_subfeatures']: a vector of binary case/control labels 172 | for the samples, dividing them to two classes. If this parameter is 173 | given when learning, 2*Pcovariates will be created. The P covariates 174 | are zero for controls and copied from cov_train for the cases, 175 | and the second P covariates are zero for cases and copied from 176 | cov_train for the controls. This matrix will replace the original, 177 | so the returned model will have 2*P weights 178 | params.['num_cv']: the number of folds in the internal cross-validation 179 | (default: 5) 180 | params.['l1_ratio']: the ratio of L1 penalty in the regularization in 181 | the range [0,1] (default: 0.5) 182 | 183 | Returns 184 | ------- 185 | prediction: vector 186 | the predicted values 187 | 188 | test_rsqr: real 189 | the value of the test r^2 190 | 191 | """ 192 | 193 | # update test covariates by the given params 194 | if 'covariates_prior' in params.keys() and params['covariates_prior'] is\ 195 | not None: 196 | cov_test = cov_test * params['covariates_prior'] 197 | 198 | if 'class_subfeatures' in params.keys() and params['class_subfeatures'] \ 199 | is not None: 200 | # cases subfeatures 201 | cases = params['class_subfeatures'] > 0 202 | case_subfeatures = np.zeros_like(cov_test) 203 | case_subfeatures[cases, :] = cov_test[cases, :] 204 | # control subfeatures 205 | controls = params['class_subfeatures'] == 0 206 | control_subfeatures = np.zeros_like(cov_test) 207 | control_subfeatures[controls, :] = cov_test[controls, :] 208 | # update cov_train 209 | cov_test = np.hstack((case_subfeatures, control_subfeatures)) 210 | 211 | prediction = enet_model.predict(cov_test) 212 | 213 | sos_residual = np.sum((prediction - res_test) ** 2) 214 | sos_original = np.sum((res_test - np.mean(res_test)) ** 2) 215 | 216 | test_rsqr = 1 - (sos_residual / sos_original) 217 | 218 | return prediction, test_rsqr 219 | -------------------------------------------------------------------------------- /fishtaco/compute_pathway_abundance.py: -------------------------------------------------------------------------------- 1 | """ 2 | This function aggregates the abundance of KOs into pathways or modules 3 | abundances and writes the output to the given file 4 | 5 | Parameters 6 | ---------- 7 | 8 | args: dictionary 9 | a dictionary containing the function parameters 10 | args.['ko_abun_file']: Input file of ko abundance per sample 11 | args.['ko_to_pathway_file']: Input file of mappingfrom ko to pathway 12 | args.['output_file']: Output file for resulting pathway abundance 13 | args.['output_counts_file']: Output file for number of KOs mapped to 14 | each pathway 15 | args.['mapping_method']: Method to map KOs to Pathway 16 | args.['compute_method']: Method to compute pathway abundance from 17 | mapped KOs 18 | args.['transpose_ko_abundance']: Transpose the ko abundance matrix given 19 | args.['transpose_output']: Transpose the output pathway abundance matrix 20 | args.['verbose']: Increase verbosity of module 21 | 22 | """ 23 | 24 | # to comply with both Py2 and Py3 25 | from __future__ import absolute_import, division, print_function 26 | 27 | import argparse 28 | import numpy as np 29 | import pandas as pd 30 | import os 31 | import sys 32 | import warnings 33 | 34 | __author__ = 'Ohad Manor' 35 | __email__ = 'omanor@gmail.com' 36 | __status__ = "Development" 37 | 38 | ############################################################################### 39 | # MAIN FUNCTION 40 | ############################################################################### 41 | def main(args): 42 | 43 | if 'verbose' in args.keys() and args['verbose']: 44 | print("Given parameters: ", args) 45 | 46 | ########################################################################### 47 | # INPUT 48 | ########################################################################### 49 | print("Reading files...") 50 | 51 | if 'ko_abun_file' in args.keys() and args['ko_abun_file'] is not None: 52 | if not os.path.isfile(args['ko_abun_file']): 53 | sys.exit('Error: Input file "' + 54 | args['ko_abun_file'] + '" does not exist') 55 | ko_abun_data = pd.read_table(args['ko_abun_file'], index_col=0, 56 | dtype={0: str}) 57 | 58 | elif 'ko_abun_pd' in args.keys(): 59 | ko_abun_data = args['ko_abun_pd'] 60 | 61 | else: 62 | sys.exit('Error: No input ko abundance file given to script') 63 | 64 | if args['transpose_ko_abundance']: 65 | ko_abun_data = ko_abun_data.T 66 | 67 | if 'output_file' in args.keys() and args['ko_to_pathway_file'] is not None: 68 | if not os.path.isfile(args['ko_to_pathway_file']): 69 | sys.exit('Error: Input file "' + 70 | args['ko_to_pathway_file'] + '" does not exist') 71 | ko_to_pathway_data = pd.read_table(args['ko_to_pathway_file'], 72 | index_col=0, dtype={0: str}) 73 | 74 | else: 75 | if args['ko_to_pathway_pd'] is not None: 76 | ko_to_pathway_data = args['ko_to_pathway_pd'] 77 | else: 78 | sys.exit('Error: No input ko to pathway file given to script') 79 | 80 | print("Done.") 81 | 82 | ########################################################################### 83 | # FILTER OUT KOs THAT ARE NOT PART OF ANY PATHWAY 84 | ########################################################################### 85 | ko = np.sort(np.intersect1d(ko_abun_data.index.values, 86 | ko_to_pathway_data.index.values)) 87 | ko_abun_data = ko_abun_data.loc[ko] 88 | ko_to_pathway_data = ko_to_pathway_data.loc[ko] 89 | 90 | ########################################################################### 91 | # FOR EACH PATHWAY, COUNT THE NUMBER OF NON-ZERO KOS THAT MAP TO IT IN 92 | # EACH SAMPLE 93 | ########################################################################### 94 | ko_binary_data = (ko_abun_data.values > 0).astype(float) 95 | pathway_counts = np.dot(ko_binary_data.T, ko_to_pathway_data).T 96 | 97 | ########################################################################### 98 | # CREATE THE KO TO PATHWAY MAPPING 99 | ########################################################################### 100 | if args['mapping_method'] != 'naive': 101 | print("No other method implemented yet...") 102 | exit() 103 | 104 | ########################################################################### 105 | # COMPUTE THE PATHWAY ABUNDANCES 106 | ########################################################################### 107 | if args['compute_method'] == 'sum': 108 | pathway_abundance = np.dot(ko_abun_data.T, ko_to_pathway_data).T 109 | 110 | ########################################################################### 111 | # WRITE OUTPUT 112 | ########################################################################### 113 | 114 | print("Writing output...") 115 | 116 | if args['transpose_output']: 117 | path_pd = pd.DataFrame(data=pathway_abundance.T, 118 | index=ko_abun_data.columns, 119 | columns=ko_to_pathway_data.columns) 120 | 121 | else: 122 | path_pd = pd.DataFrame(data=pathway_abundance, 123 | index=ko_to_pathway_data.columns, 124 | columns=ko_abun_data.columns) 125 | 126 | path_pd.index.name = 'Pathway' 127 | 128 | if 'output_file' in args.keys(): 129 | with warnings.catch_warnings(): 130 | warnings.filterwarnings("ignore", category=DeprecationWarning) 131 | path_pd.to_csv(args['output_file'], sep='\t') 132 | 133 | elif 'output_pd' in args.keys(): 134 | args['output_pd'] = path_pd 135 | 136 | else: 137 | sys.exit('Error: No output destination given') 138 | 139 | if 'output_counts_file' in args.keys(): 140 | counts_pd = pd.DataFrame(data=pathway_counts, 141 | index=ko_to_pathway_data.columns, 142 | columns=ko_abun_data.columns) 143 | counts_pd.index.name = 'Pathway' 144 | with warnings.catch_warnings(): 145 | warnings.filterwarnings("ignore", category=DeprecationWarning) 146 | counts_pd.to_csv(args['output_counts_file'], sep='\t') 147 | 148 | print("Done.") 149 | 150 | ############################################################################### 151 | 152 | if __name__ == "__main__": 153 | # get options from user 154 | parser = argparse.ArgumentParser(description='compute the abundance of ' 155 | 'pathways from KOs') 156 | 157 | parser.add_argument('-ko', '--ko_abundance', dest='ko_abun_file', 158 | help='Input file of ko abundance per sample', 159 | default=None) 160 | 161 | parser.add_argument('-ko2path', '--ko_to_pathway', 162 | dest='ko_to_pathway_file', 163 | help='Input file of mappingfrom ko to pathway', 164 | default=None) 165 | 166 | parser.add_argument('-o', '--output', dest='output_file', 167 | help='Output file for resulting pathway abundance (' 168 | 'default: out.tab)', default='out.tab') 169 | 170 | parser.add_argument('-oc', '--output_counts', dest='output_counts_file', 171 | help='Output file for number of KOs mapped to each ' 172 | 'pathway (default: counts.tab)', 173 | default='counts.tab') 174 | 175 | parser.add_argument('-map', '--mapping_method', dest='mapping_method', 176 | help='Method to map KOs to Pathway (default: naive)', 177 | default='naive', choices=['naive']) 178 | 179 | parser.add_argument('-compute', '--compute_method', dest='compute_method', 180 | help='Method to compute pathway abundance from ' 181 | 'mapped KOs (default: sum)', 182 | default='sum', choices=['sum']) 183 | 184 | parser.add_argument('-transpose_ko', '--transpose_ko_abundance', 185 | dest='transpose_ko_abundance', 186 | help='Transpose the ko abundance matrix given (' 187 | 'default: False)', action='store_true') 188 | 189 | parser.add_argument('-transpose_output', '--transpose_output', 190 | dest='transpose_output', 191 | help='Transpose the output pathway abundance matrix ' 192 | '(default: False)', action='store_true') 193 | 194 | parser.add_argument('-v', '--verbose', dest='verbose', 195 | help='Increase verbosity of module (default: false)', 196 | action='store_true') 197 | 198 | given_args = parser.parse_args() 199 | main(vars(given_args)) 200 | 201 | 202 | 203 | 204 | 205 | 206 | -------------------------------------------------------------------------------- /scripts/run_fishtaco.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | """ 4 | This is the running script for FishTaco 5 | """ 6 | # to comply with both Py2 and Py3 7 | from __future__ import absolute_import, division, print_function 8 | 9 | import argparse 10 | 11 | # when the test module will be ready: 12 | from fishtaco.compute_contribution_to_DA import main 13 | 14 | if __name__ == "__main__": 15 | # get options from user 16 | parser = \ 17 | argparse.ArgumentParser(description='Quantify the ' 18 | 'individual contributions of ' 19 | 'taxa to shifts observed in ' 20 | 'functional composition across ' 21 | 'different sample sets') 22 | 23 | # Required arguments: 24 | 25 | parser.add_argument('-ta', '--taxonomic_abundance_profiles', 26 | dest='taxa_abun_file', 27 | help='Input file of taxonomic abundance profiles', 28 | default=None) 29 | 30 | parser.add_argument('-fu', '--functional_abundance_profiles', 31 | dest='function_abun_file', 32 | help='Input file of functional abundance profiles', 33 | default=None) 34 | 35 | parser.add_argument('-l', '--labels', dest='class_file', 36 | help='Input file of label assignment for the two ' 37 | 'sample sets being compared', default=None) 38 | 39 | # Optional arguments: 40 | 41 | parser.add_argument('-gc', '--genomic_content_of_taxa', 42 | dest='taxa_to_function_file', 43 | help='Input file of genomic content of each taxa', 44 | default=None) 45 | 46 | parser.add_argument('-inf', '--perform_inference_of_genomic_content', 47 | dest='apply_inference', 48 | help='Defines if genome content is inferred (either ' 49 | 'de-novo or prior-based if genomic content is ' 50 | 'also given)', action='store_true') 51 | 52 | parser.add_argument('-label_to_find_enrichment_in', dest='case_label', 53 | help='Define sample set label to find enrichment in ' 54 | '(default: 1)', default='1') 55 | 56 | parser.add_argument('-label_to_find_enrichment_against', 57 | dest='control_label', 58 | help='Define sample set label to find enrichment ' 59 | 'against (default: 0)', default='0') 60 | 61 | parser.add_argument('-op', '--output_prefix', dest='output_pref', 62 | help='Output prefix for result files (default: ' 63 | 'fishtaco_out)', default='fishtaco_out') 64 | 65 | parser.add_argument('-map_function_level', dest='map_function_level', 66 | help='Map functions to pathways or modules ' 67 | '(default: pathway)', 68 | default='pathway', choices=['pathway', 'module', 69 | 'none', 'custom']) 70 | 71 | # Advanced arguments: 72 | 73 | parser.add_argument('-map_function_file', dest='map_function_file', 74 | help='pathways or modules mapping file (default: use ' 75 | 'internal KEGG file)', 76 | default=None) 77 | 78 | parser.add_argument('-perform_inference_on_ko_level', 79 | dest='perform_inference_on_ko_level', 80 | help='Indicates to perform the inference on the KO ' 81 | 'level (default: use the mapped functional ' 82 | 'level, e.g., pathway)', action='store_true') 83 | 84 | parser.add_argument('-mult_hyp', '--multiple_hypothesis_correction', 85 | dest='multiple_hypothesis_correction', 86 | help='Multiple hypothesis correction for functional ' 87 | 'enrichment (default: FDR)', 88 | default='FDR', 89 | choices=['Bonf', 'FDR', 'none']) 90 | 91 | parser.add_argument('-max_func', '--maximum_functions_to_analyze', 92 | dest='max_da_functions_cases_controls', 93 | help='Maximum number of enriched functions to ' 94 | 'consider (default: All)', default=None) 95 | 96 | parser.add_argument('-assessment', '--taxa_assessment_method', 97 | dest='taxa_assessment_method', 98 | help='The method used when assessing taxa to compute ' 99 | 'individual contributions (default: multi_taxa)', 100 | default='multi_taxa', 101 | choices=['single_taxa', 'multi_taxa']) 102 | 103 | parser.add_argument('-score', '--score_to_compute', 104 | dest='score_to_compute', 105 | help='The enrichment score to compute for each ' 106 | 'function (default: wilcoxon)', 107 | default='wilcoxon', 108 | choices=['t_test', 'mean_diff', 'median_diff', 109 | 'wilcoxon', 'log_mean_ratio']) 110 | 111 | parser.add_argument('-max_score', '--max_score_cutoff', 112 | dest='max_score_cutoff', 113 | help='The maximum score cutoff (for example, ' 114 | 'when dividing by zero) (default: 100)', 115 | default='100') 116 | 117 | parser.add_argument('-na_rep', dest='na_rep', 118 | help='How to represent NAs in the output (default: ' 119 | 'NA)', default='NA') 120 | 121 | parser.add_argument('-number_of_permutations', 122 | dest='number_of_permutations', 123 | help='number of permutations (default: 100)', 124 | default='100') 125 | 126 | parser.add_argument('-number_of_shapley_orderings_per_taxa', 127 | dest='number_of_shapley_orderings_per_taxa', 128 | help='number of shapley orderings per taxa ' 129 | '(default: 5)', default='5') 130 | 131 | # DEPRECATED: 132 | #parser.add_argument('-use_gc_as_prior', 133 | # '--use_genomic_content_of_taxa_as_prior', dest='use_t2f_as_prior', 134 | # help='Learn the taxa copy number of each function, 135 | # using the given genomic content data as prior (default: False)', 136 | # action='store_true') 137 | #parser.add_argument('-residual_mode', dest='residual_mode', 138 | # choices=['as_taxa', 'remove_residual', 'as_baseline'], 139 | # help='How to treat the residual of the functional 140 | # abundance profile (default: remove_residual)', default='remove_residual') 141 | #parser.add_argument('-normalization_mode', dest='normalization_mode', 142 | # choices=['none', 'scale_non_permuted', 'scale_permuted'], 143 | # help='How to normalize the sample after permuting 144 | # taxa (default: scale_permuted)', default='scale_permuted') 145 | #parser.add_argument('-permutation_mode', dest='permutation_mode', 146 | # choices=['independent', 'blocks'], 147 | # help='How to permute the taxa across samples 148 | # (default: blocks)', default='blocks') 149 | 150 | parser.add_argument('-en', '--enrichment_results', dest='da_result_file', 151 | help='Pre-computed functional enrichment results ' 152 | 'from the compute_differential_abundance.py ' 153 | 'script (default: None)', default=None) 154 | 155 | parser.add_argument('-single_function_filter', 156 | dest='single_function_filter', 157 | help='Limit analysis only to this single function (' 158 | 'default: None)', default=None) 159 | 160 | parser.add_argument('-multi_function_filter_list', 161 | dest='multi_function_filter_list', 162 | help='Limit analysis only to these comma-separated ' 163 | 'functions (default: None)', default=None) 164 | 165 | parser.add_argument('-functional_profile_already_corrected_with_musicc', 166 | dest='functional_profile_already_corrected_with_musicc', 167 | help='Indicates that the functional profile has been ' 168 | 'already corrected with MUSiCC prior to running ' 169 | 'FishTaco (default: False)', action='store_true') 170 | 171 | parser.add_argument('-log', '--log', dest='write_log', 172 | help='Write to log file (default: False)', 173 | action='store_true') 174 | 175 | parser.add_argument('-cv', '--num_cv', dest='num_cv', 176 | help='Number of folds to use during genomic content inference cross validation (default: %(default)s)', 177 | default=5, 178 | type=int) 179 | 180 | parser.add_argument('-a', '--alpha', dest='alpha', 181 | help='Corrected p-value cutoff for defining differentially abundant functions for which to decompose shift contributions (not used when the multiple hypothesis correction method is none) (default: %(default)s)', 182 | default=0.05, 183 | type=float) 184 | 185 | given_args = parser.parse_args() 186 | main(vars(given_args)) 187 | -------------------------------------------------------------------------------- /fishtaco/compute_differential_abundance.py: -------------------------------------------------------------------------------- 1 | """ 2 | This function computes the differential abundance score of functions or taxa 3 | 4 | Parameters 5 | ---------- 6 | 7 | args: dictionary 8 | a dictionary containing the function parameters 9 | args.['input_file']: Input abundance file to compute differential 10 | abundance 11 | args.['class_file']: Class assignment file for the two different 12 | compared classes 13 | args.['class_header']: Flag indicating whether class file contains a 14 | column header 15 | args.['row_metadata']: Metadata to add to each row 16 | args.['output_file']: Output destination for differential abundance 17 | scores 18 | args.['method']: Method to compute differential abundance 19 | args.['control_label']: Define control label 20 | args.['case_label']: Define case label 21 | args.['verbose']: Increase verbosity of module 22 | args.['alpha']: Significance cutoff for multiple hypothesis correction 23 | 24 | """ 25 | # to comply with both Py2 and Py3 26 | from __future__ import absolute_import, division, print_function 27 | 28 | # general imports 29 | import argparse 30 | import numpy as np 31 | import pandas as pd 32 | from scipy import stats 33 | from statsmodels.stats.multitest import multipletests 34 | import sys 35 | import os.path 36 | 37 | __author__ = 'Ohad Manor' 38 | __email__ = 'omanor@gmail.com' 39 | __status__ = "Development" 40 | 41 | def main(args): 42 | 43 | # if verbose, print given options 44 | if 'verbose' in args.keys() and args['verbose']: 45 | print("Given parameters: ", args) 46 | 47 | # set some initial settings for the script 48 | np.set_printoptions(precision=5, suppress=False, linewidth=200) 49 | 50 | if 'verbose' in args.keys() and args['verbose']: 51 | print("Loading files... ", end="") 52 | 53 | # read the input abundance from a file 54 | if 'input_file' in args.keys(): 55 | if not os.path.isfile(args['input_file']): 56 | sys.exit('Error: Input file "' + 57 | args['input_file'] + '" does not exist') 58 | abundance_data = pd.read_table(args['input_file'], index_col=0) 59 | # read the input abundance from a panda data frame 60 | elif 'input_pd' in args.keys(): 61 | abundance_data = args['input_pd'] 62 | else: 63 | sys.exit('Error: No input abundance given to script') 64 | 65 | # change rows with NaNs as IDs to a string ID 66 | non_nan_id = np.array(abundance_data.index) 67 | for i in range(abundance_data.index.shape[0]): 68 | if not isinstance(non_nan_id[i], str) and not isinstance(non_nan_id[i], 69 | np.int_): 70 | print("Fixing row ID for: " + str(non_nan_id[i])) 71 | non_nan_id[i] = "NaN_" + str(i) 72 | 73 | abundance_data.index = non_nan_id 74 | 75 | # read the metadata if given 76 | if 'row_metadata' in args.keys() and args['row_metadata'] is not None: 77 | if not os.path.isfile(args['row_metadata']): 78 | sys.exit('Error: Metadata file does not exist') 79 | metadata = pd.read_table(args['row_metadata'], index_col=0, 80 | header=None) 81 | metadata.columns = ['Metadata'] 82 | metadata.index.name = 'KO' 83 | else: 84 | metadata = pd.DataFrame(data=abundance_data.index.values, 85 | index=abundance_data.index, 86 | columns=['Metadata']) 87 | 88 | # read the class file 89 | if 'class_file' in args.keys(): 90 | if not os.path.isfile(args['class_file']): 91 | sys.exit('Error: Class file does not exist') 92 | if args['class_header']: 93 | class_data = pd.read_table(args['class_file'], index_col=0, 94 | dtype=str) 95 | else: 96 | class_data = pd.read_table(args['class_file'], index_col=0, 97 | header=None, dtype=str) 98 | class_data.columns = ['Class'] 99 | class_data.index.name = 'Sample' 100 | 101 | elif 'class_pd' in args.keys(): 102 | class_data = args['class_pd'] 103 | 104 | else: 105 | sys.exit('Error: No class file given (-c)') 106 | 107 | # intersect the class file with the abundance file and create new 108 | # data frames that hold only the shared samples 109 | # sort both sample lists 110 | cols_abundances = [col for col in abundance_data.columns if col in 111 | class_data.index] 112 | cols_abundances.sort() 113 | rows_class = [row for row in class_data.index if row in 114 | abundance_data.columns] 115 | rows_class.sort() 116 | abundance_data = abundance_data[cols_abundances] 117 | class_data = class_data.loc[rows_class] 118 | 119 | number_of_samples = abundance_data.shape[1] 120 | number_of_kos = abundance_data.shape[0] 121 | 122 | pvals = np.zeros(number_of_kos) 123 | signLogP = np.zeros(number_of_kos) 124 | mean_cases = np.zeros(number_of_kos) 125 | mean_controls = np.zeros(number_of_kos) 126 | stat_value = np.zeros(number_of_kos) 127 | 128 | # define controls and cases 129 | controls = (class_data.values.reshape(number_of_samples) == 130 | args['control_label']) 131 | cases = (class_data.values.reshape(number_of_samples) == 132 | args['case_label']) 133 | 134 | if 'verbose' in args.keys() and args['verbose']: 135 | print("Done.") 136 | print("Number of samples: " + str(number_of_samples)) 137 | print("Number of controls: " + str(sum(controls))) 138 | print("Number of cases: " + str(sum(cases))) 139 | print("Number of functions: " + str(number_of_kos)) 140 | print("Computing differential abundance... ", end="") 141 | 142 | for i in range(number_of_kos): 143 | 144 | mean_cases[i] = np.mean(abundance_data.values[i, cases]) 145 | mean_controls[i] = np.mean(abundance_data.values[i, controls]) 146 | 147 | if args['method'] == "Wilcoxon" or args['method'] == "wilcoxon": 148 | (z, p) = stats.ranksums(abundance_data.values[i, cases], 149 | abundance_data.values[i, controls]) 150 | stat_value[i] = z 151 | pvals[i] = p 152 | signLogP[i] = -1 * np.log10(p) * np.sign(z) 153 | 154 | if args['method'] == "Ttest" or args['method'] == "ttest": 155 | (t, p) = stats.ttest_ind(abundance_data.values[i, cases], 156 | abundance_data.values[i, controls]) 157 | stat_value[i] = t 158 | pvals[i] = p 159 | signLogP[i] = -1 * np.log10(p) * np.sign(t) 160 | 161 | # write output to file 162 | if 'verbose' in args.keys() and args['verbose']: 163 | print("Done.") 164 | print("Writing output... ", end="") 165 | 166 | #print(pvals) 167 | _, bonferroni, _, _ = multipletests(pvals, alpha=args['alpha'], method="bonferroni") 168 | _, fdr, _, _ = multipletests(pvals, alpha=args['alpha'], method="fdr_bh") 169 | 170 | # create output data frame 171 | output_df = pd.DataFrame(data=np.vstack((mean_cases, mean_controls, 172 | stat_value, pvals, signLogP, 173 | bonferroni, fdr)).transpose(), 174 | index=abundance_data.index, 175 | columns=np.array(("meanCases", "meanControls", 176 | "StatValue", "pval", "singLogP", 177 | "Bonf", "FDR"))) 178 | 179 | # join with metadata 180 | output_df = output_df.join(metadata) 181 | output_df.index.name = 'Function' 182 | 183 | # write to output file 184 | if 'output_file' in args.keys(): 185 | output_df.to_csv(args['output_file'], sep='\t', na_rep='None') 186 | elif 'output_pd' in args.keys(): 187 | args['output_pd'] = output_df 188 | else: 189 | sys.exit('Error: No output destination given') 190 | 191 | if 'verbose' in args.keys() and args['verbose']: 192 | print("Done.") 193 | 194 | ############################################################################### 195 | 196 | if __name__ == "__main__": 197 | 198 | # get options from user 199 | parser = argparse.ArgumentParser(description='Compute Differential ' 200 | 'Abundance') 201 | 202 | parser.add_argument('input_file', help='Input abundance file to compute ' 203 | 'differential abundance') 204 | 205 | parser.add_argument('-c', '--class', dest='class_file', 206 | help='Class assignment file for the two different ' 207 | 'compared classes', default=None) 208 | 209 | parser.add_argument('-ch', '--class_header', dest='class_header', 210 | help='Flag indicating whether class file contains a ' 211 | 'column header (default: false)', 212 | action='store_true') 213 | 214 | parser.add_argument('-rmeta', '--row_metadata', dest='row_metadata', 215 | help='Metadata to add to each row (Default: add row ' 216 | 'ID as metadata)', default=None) 217 | 218 | parser.add_argument('-o', '--out', dest='output_file', 219 | help='Output destination for differential abundance ' 220 | 'scores (default: DA.tab)', default='DA.tab') 221 | 222 | parser.add_argument('-m', '--method', dest='method', 223 | help='Method to compute differential abundance (' 224 | 'default: Wilcoxon)', default='Wilcoxon') 225 | 226 | parser.add_argument('-control_label', dest='control_label', 227 | help='Define control label (default: 0)', default='0') 228 | 229 | parser.add_argument('-case_label', dest='case_label', 230 | help='Define case label (default: 1)', default='1') 231 | 232 | parser.add_argument('-v', '--verbose', dest='verbose', 233 | help='Increase verbosity of module (default: false)', 234 | action='store_true') 235 | 236 | parser.add_argument('-a', '--alpha', dest='alpha', 237 | help='Corrected p-value cutoff for defining differentially abundant functions for which to decompose shift contributions (not used when the multiple hypothesis correction method is none) (default: %(default)s)', 238 | default=0.05, 239 | type=float) 240 | 241 | given_args = parser.parse_args() 242 | 243 | main(vars(given_args)) 244 | 245 | -------------------------------------------------------------------------------- /MANIFEST: -------------------------------------------------------------------------------- 1 | AUTHORS.rst 2 | HISTORY.rst 3 | LICENSE 4 | README.rst 5 | setup.py 6 | fishtaco/__init__.py 7 | fishtaco/compute_contribution_to_DA.py 8 | fishtaco/compute_differential_abundance.py 9 | fishtaco/compute_pathway_abundance.py 10 | fishtaco/compute_shapley_value_from_subsets.py 11 | fishtaco/learn_non_neg_elastic_net_with_prior.py 12 | fishtaco/BAK/compute_contribution_to_DA.back_01_21_2015 13 | fishtaco/BAK/compute_contribution_to_DA.back_07_14_2014 14 | fishtaco/data/KOvsMODULE_BACTERIAL_KEGG_2013_07_15 15 | fishtaco/data/KOvsMODULE_BACTERIAL_KEGG_2013_07_15.tab 16 | fishtaco/data/KOvsPATHWAY_BACTERIAL_KEGG_2013_07_15 17 | fishtaco/data/KOvsPATHWAY_BACTERIAL_KEGG_2013_07_15.tab 18 | fishtaco/examples/KO_vs_SAMPLE_MUSiCC.tab 19 | fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001.tab 20 | fishtaco/examples/METAPHLAN_taxa_vs_KO_only_K00001_K00054.tab 21 | fishtaco/examples/METAPHLAN_taxa_vs_SAMPLE_for_K00001.tab 22 | fishtaco/examples/PATHWAY_vs_SAMPLE_MUSiCC.tab 23 | fishtaco/examples/SAMPLE_vs_CLASS.tab 24 | fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001.tab 25 | fishtaco/examples/WGS_KO_vs_SAMPLE_MUSiCC_only_K00001_K00054.tab 26 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 27 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 28 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 29 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 30 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 31 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 32 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 33 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 34 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 35 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 36 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 37 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 38 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 39 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 40 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 41 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 42 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 43 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 44 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 45 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 46 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 47 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 48 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 49 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 50 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 51 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 52 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 53 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 54 | fishtaco/examples/output/fishtaco_out_de_novo_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 55 | fishtaco/examples/output/fishtaco_out_de_novo_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 56 | fishtaco/examples/output/fishtaco_out_filtering_by_function_list_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 57 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 58 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 59 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 60 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 61 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 62 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 63 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 64 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 65 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 66 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 67 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 68 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 69 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 70 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 71 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 72 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 73 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 74 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 75 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 76 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 77 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 78 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 79 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 80 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 81 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 82 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 83 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 84 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 85 | fishtaco/examples/output/fishtaco_out_no_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 86 | fishtaco/examples/output/fishtaco_out_no_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 87 | fishtaco/examples/output/fishtaco_out_predict_function_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 88 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 89 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_function_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 90 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 91 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_DA_taxa_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 92 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 93 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_mean_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 94 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 95 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_median_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 96 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 97 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_original_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 98 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 99 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_DA_value_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 100 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 101 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 102 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 103 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_predicted_function_agreement_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 104 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 105 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_residual_function_abundance_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 106 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 107 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_run_log_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 108 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_shapley_orderings_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 109 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 110 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_std_stat_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 111 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 112 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 113 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 114 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learned_copy_num_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 115 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_permuted_shapley_orderings.tab 116 | fishtaco/examples/output/fishtaco_out_prior_based_inf_STAT_taxa_learning_rsqr_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 117 | fishtaco/examples/output/fishtaco_out_prior_based_inf_main_output_SCORE_wilcoxon_ASSESSMENT_single_taxa.tab 118 | fishtaco/examples/output/fishtaco_out_shapley_STAT_taxa_contributions_SCORE_wilcoxon_ASSESSMENT_multi_taxa.tab 119 | scripts/run_fishtaco.py 120 | tests/test_fishtaco.py 121 | -------------------------------------------------------------------------------- /fishtaco/examples/PATHWAY_vs_SAMPLE_MUSiCC.tab: 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