├── .gitignore ├── DRUMLR_Tutorial.pdf ├── DRUMLR_code ├── .Rbuildignore ├── DESCRIPTION ├── DRUMLR.Rproj ├── NAMESPACE ├── R │ ├── BuildDRUMLs.R │ ├── BuildEMDR.R │ ├── BuildModels.R │ ├── DRUMLPredict.R │ ├── DRUMLPredict_example.R │ ├── DrugMarkerEnrichment.R │ ├── FilterSensitivity.R │ ├── MinMaxNormalise.R │ ├── OptimiseMLInput.R │ ├── RemoveRepeatNo.R │ └── sysdata.rda └── man │ ├── BuildDRUMLs.Rd │ ├── BuildEMDR.Rd │ ├── DrugMarkerEnrichment.Rd │ ├── FilterSensitivity.Rd │ ├── MinMaxNormalise.Rd │ ├── OptimiseMLInput.Rd │ ├── PredictDRUML.Rd │ ├── PredictDRUML_example.Rd │ ├── RemoveRepeatNo.Rd │ └── dot-BuildModel.Rd ├── Example_models ├── models_phospho │ ├── phospho_aml_BX.912_bayes.rds │ ├── phospho_aml_BX.912_cubist.rds │ ├── phospho_aml_BX.912_dl.zip │ ├── phospho_aml_BX.912_nnet.rds │ ├── phospho_aml_BX.912_pcr.rds │ ├── phospho_aml_BX.912_pls.rds │ ├── phospho_aml_BX.912_rf.rds │ ├── phospho_aml_BX.912_svm.rds │ ├── phospho_aml_Cytarabine_bayes.rds │ ├── phospho_aml_Cytarabine_cubist.rds │ ├── phospho_aml_Cytarabine_dl.zip │ ├── phospho_aml_Cytarabine_nnet.rds │ ├── phospho_aml_Cytarabine_pcr.rds │ ├── phospho_aml_Cytarabine_pls.rds │ ├── phospho_aml_Cytarabine_rf.rds │ ├── phospho_aml_Cytarabine_svm.rds │ ├── phospho_aml_GSK269962A_bayes.rds │ ├── phospho_aml_GSK269962A_cubist.rds │ ├── phospho_aml_GSK269962A_dl.zip │ ├── phospho_aml_GSK269962A_nnet.rds │ ├── phospho_aml_GSK269962A_pcr.rds │ ├── phospho_aml_GSK269962A_pls.rds │ ├── phospho_aml_GSK269962A_rf.rds │ ├── phospho_aml_GSK269962A_svm.rds │ ├── phospho_aml_IOX2_bayes.rds │ ├── phospho_aml_IOX2_cubist.rds │ ├── phospho_aml_IOX2_dl.zip │ ├── phospho_aml_IOX2_nnet.rds │ ├── phospho_aml_IOX2_pcr.rds │ ├── phospho_aml_IOX2_pls.rds │ ├── phospho_aml_IOX2_rf.rds │ ├── phospho_aml_IOX2_svm.rds │ ├── phospho_aml_JQ12_bayes.rds │ ├── phospho_aml_JQ12_cubist.rds │ ├── phospho_aml_JQ12_dl.zip │ ├── phospho_aml_JQ12_nnet.rds │ ├── phospho_aml_JQ12_pcr.rds │ ├── phospho_aml_JQ12_pls.rds │ ├── phospho_aml_JQ12_rf.rds │ ├── phospho_aml_JQ12_svm.rds │ ├── phospho_aml_S.Trityl.L.cysteine_bayes.rds │ ├── phospho_aml_S.Trityl.L.cysteine_cubist.rds │ ├── phospho_aml_S.Trityl.L.cysteine_dl.zip │ ├── phospho_aml_S.Trityl.L.cysteine_nnet.rds │ ├── phospho_aml_S.Trityl.L.cysteine_pcr.rds │ ├── phospho_aml_S.Trityl.L.cysteine_pls.rds │ ├── phospho_aml_S.Trityl.L.cysteine_rf.rds │ ├── phospho_aml_S.Trityl.L.cysteine_svm.rds │ ├── phospho_aml_SN.38_bayes.rds │ ├── phospho_aml_SN.38_cubist.rds │ ├── phospho_aml_SN.38_dl.zip │ ├── phospho_aml_SN.38_nnet.rds │ ├── phospho_aml_SN.38_pcr.rds │ ├── phospho_aml_SN.38_pls.rds │ ├── phospho_aml_SN.38_rf.rds │ ├── phospho_aml_SN.38_svm.rds │ ├── phospho_aml_TGX221_bayes.rds │ ├── phospho_aml_TGX221_cubist.rds │ ├── phospho_aml_TGX221_dl.zip │ ├── phospho_aml_TGX221_nnet.rds │ ├── phospho_aml_TGX221_pcr.rds │ ├── phospho_aml_TGX221_pls.rds │ ├── phospho_aml_TGX221_rf.rds │ ├── phospho_aml_TGX221_svm.rds │ ├── phospho_aml_TPCA.1_bayes.rds │ ├── phospho_aml_TPCA.1_cubist.rds │ ├── phospho_aml_TPCA.1_dl.zip │ ├── phospho_aml_TPCA.1_nnet.rds │ ├── phospho_aml_TPCA.1_pcr.rds │ ├── phospho_aml_TPCA.1_pls.rds │ ├── phospho_aml_TPCA.1_rf.rds │ ├── phospho_aml_TPCA.1_svm.rds │ ├── phospho_aml_VX.11e_bayes.rds │ ├── phospho_aml_VX.11e_cubist.rds │ ├── phospho_aml_VX.11e_dl.zip │ ├── phospho_aml_VX.11e_nnet.rds │ ├── phospho_aml_VX.11e_pcr.rds │ ├── phospho_aml_VX.11e_pls.rds │ ├── phospho_aml_VX.11e_rf.rds │ ├── phospho_aml_VX.11e_svm.rds │ ├── phospho_solid_BX.912_bayes.rds │ ├── phospho_solid_BX.912_cubist.rds │ ├── phospho_solid_BX.912_dl.zip │ ├── phospho_solid_BX.912_nnet.rds │ ├── phospho_solid_BX.912_pcr.rds │ ├── phospho_solid_BX.912_pls.rds │ ├── phospho_solid_BX.912_rf.rds │ ├── phospho_solid_BX.912_svm.rds │ ├── phospho_solid_Cytarabine_bayes.rds │ ├── phospho_solid_Cytarabine_cubist.rds │ ├── phospho_solid_Cytarabine_dl.zip │ ├── phospho_solid_Cytarabine_nnet.rds │ ├── phospho_solid_Cytarabine_pcr.rds │ ├── phospho_solid_Cytarabine_pls.rds │ ├── phospho_solid_Cytarabine_rf.rds │ ├── phospho_solid_Cytarabine_svm.rds │ ├── phospho_solid_GSK269962A_bayes.rds │ ├── phospho_solid_GSK269962A_cubist.rds │ ├── phospho_solid_GSK269962A_dl.zip │ ├── phospho_solid_GSK269962A_nnet.rds │ ├── phospho_solid_GSK269962A_pcr.rds │ ├── phospho_solid_GSK269962A_pls.rds │ ├── phospho_solid_GSK269962A_rf.rds │ ├── phospho_solid_GSK269962A_svm.rds │ ├── phospho_solid_IOX2_bayes.rds │ ├── phospho_solid_IOX2_cubist.rds │ ├── phospho_solid_IOX2_dl.zip │ ├── phospho_solid_IOX2_nnet.rds │ ├── phospho_solid_IOX2_pcr.rds │ ├── phospho_solid_IOX2_pls.rds │ ├── phospho_solid_IOX2_rf.rds │ ├── phospho_solid_IOX2_svm.rds │ ├── phospho_solid_JQ12_bayes.rds │ ├── phospho_solid_JQ12_cubist.rds │ ├── phospho_solid_JQ12_dl.zip │ ├── phospho_solid_JQ12_nnet.rds │ ├── phospho_solid_JQ12_pcr.rds │ ├── phospho_solid_JQ12_pls.rds │ ├── phospho_solid_JQ12_rf.rds │ ├── phospho_solid_JQ12_svm.rds │ ├── phospho_solid_SN.38_bayes.rds │ ├── phospho_solid_SN.38_cubist.rds │ ├── phospho_solid_SN.38_dl.zip │ ├── phospho_solid_SN.38_nnet.rds │ ├── phospho_solid_SN.38_pcr.rds │ ├── phospho_solid_SN.38_pls.rds │ ├── phospho_solid_SN.38_rf.rds │ ├── phospho_solid_SN.38_svm.rds │ ├── phospho_solid_TGX221_bayes.rds │ ├── phospho_solid_TGX221_cubist.rds │ ├── phospho_solid_TGX221_dl.zip │ ├── phospho_solid_TGX221_nnet.rds │ ├── phospho_solid_TGX221_pcr.rds │ ├── phospho_solid_TGX221_pls.rds │ ├── phospho_solid_TGX221_rf.rds │ ├── phospho_solid_TGX221_svm.rds │ ├── phospho_solid_TPCA.1_bayes.rds │ ├── phospho_solid_TPCA.1_cubist.rds │ ├── phospho_solid_TPCA.1_dl.zip │ ├── phospho_solid_TPCA.1_nnet.rds │ ├── phospho_solid_TPCA.1_pcr.rds │ ├── phospho_solid_TPCA.1_pls.rds │ ├── phospho_solid_TPCA.1_rf.rds │ ├── phospho_solid_TPCA.1_svm.rds │ ├── phospho_solid_VX.11e_bayes.rds │ ├── phospho_solid_VX.11e_cubist.rds │ ├── phospho_solid_VX.11e_dl.zip │ ├── phospho_solid_VX.11e_nnet.rds │ ├── phospho_solid_VX.11e_pcr.rds │ ├── phospho_solid_VX.11e_pls.rds │ ├── phospho_solid_VX.11e_rf.rds │ └── phospho_solid_VX.11e_svm.rds ├── models_proteomics │ ├── proteomics_aml_BX.912_bayes.rds │ ├── proteomics_aml_BX.912_cubist.rds │ ├── proteomics_aml_BX.912_dl.zip │ ├── proteomics_aml_BX.912_nnet.rds │ ├── proteomics_aml_BX.912_pcr.rds │ ├── proteomics_aml_BX.912_pls.rds │ ├── proteomics_aml_BX.912_rf.rds │ ├── proteomics_aml_BX.912_svm.rds │ ├── proteomics_aml_Cytarabine_bayes.rds │ ├── proteomics_aml_Cytarabine_cubist.rds │ ├── proteomics_aml_Cytarabine_dl.zip │ ├── proteomics_aml_Cytarabine_nnet.rds │ ├── proteomics_aml_Cytarabine_pcr.rds │ ├── proteomics_aml_Cytarabine_pls.rds │ ├── proteomics_aml_Cytarabine_rf.rds │ ├── proteomics_aml_Cytarabine_svm.rds │ ├── proteomics_aml_GSK269962A_bayes.rds │ ├── proteomics_aml_GSK269962A_cubist.rds │ ├── proteomics_aml_GSK269962A_dl.zip │ ├── proteomics_aml_GSK269962A_nnet.rds │ ├── proteomics_aml_GSK269962A_pcr.rds │ ├── proteomics_aml_GSK269962A_pls.rds │ ├── proteomics_aml_GSK269962A_rf.rds │ ├── proteomics_aml_GSK269962A_svm.rds │ ├── proteomics_aml_IOX2_bayes.rds │ ├── proteomics_aml_IOX2_cubist.rds │ ├── proteomics_aml_IOX2_dl.zip │ ├── proteomics_aml_IOX2_nnet.rds │ ├── proteomics_aml_IOX2_pcr.rds │ ├── proteomics_aml_IOX2_pls.rds │ ├── proteomics_aml_IOX2_rf.rds │ ├── proteomics_aml_IOX2_svm.rds │ ├── proteomics_aml_JQ12_bayes.rds │ ├── proteomics_aml_JQ12_cubist.rds │ ├── proteomics_aml_JQ12_dl.zip │ ├── proteomics_aml_JQ12_nnet.rds │ ├── proteomics_aml_JQ12_pcr.rds │ ├── proteomics_aml_JQ12_pls.rds │ ├── proteomics_aml_JQ12_rf.rds │ ├── proteomics_aml_JQ12_svm.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_bayes.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_cubist.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_dl.zip │ ├── proteomics_aml_S.Trityl.L.cysteine_nnet.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_pcr.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_pls.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_rf.rds │ ├── proteomics_aml_S.Trityl.L.cysteine_svm.rds │ ├── proteomics_aml_SN.38_bayes.rds │ ├── proteomics_aml_SN.38_cubist.rds │ ├── proteomics_aml_SN.38_dl.zip │ ├── proteomics_aml_SN.38_nnet.rds │ ├── proteomics_aml_SN.38_pcr.rds │ ├── proteomics_aml_SN.38_pls.rds │ ├── proteomics_aml_SN.38_rf.rds │ ├── proteomics_aml_SN.38_svm.rds │ ├── proteomics_aml_TGX221_bayes.rds │ ├── proteomics_aml_TGX221_cubist.rds │ ├── proteomics_aml_TGX221_dl.zip │ ├── proteomics_aml_TGX221_nnet.rds │ ├── proteomics_aml_TGX221_pcr.rds │ ├── proteomics_aml_TGX221_pls.rds │ ├── proteomics_aml_TGX221_rf.rds │ ├── proteomics_aml_TGX221_svm.rds │ ├── proteomics_aml_TPCA.1_bayes.rds │ ├── proteomics_aml_TPCA.1_cubist.rds │ ├── proteomics_aml_TPCA.1_dl.zip │ ├── proteomics_aml_TPCA.1_nnet.rds │ ├── proteomics_aml_TPCA.1_pcr.rds │ ├── proteomics_aml_TPCA.1_pls.rds │ ├── proteomics_aml_TPCA.1_rf.rds │ ├── proteomics_aml_TPCA.1_svm.rds │ ├── proteomics_aml_VX.11e_bayes.rds │ ├── proteomics_aml_VX.11e_cubist.rds │ ├── proteomics_aml_VX.11e_dl.zip │ ├── proteomics_aml_VX.11e_nnet.rds │ ├── proteomics_aml_VX.11e_pcr.rds │ ├── proteomics_aml_VX.11e_pls.rds │ ├── proteomics_aml_VX.11e_rf.rds │ ├── proteomics_aml_VX.11e_svm.rds │ ├── proteomics_solid_BX.912_bayes.rds │ ├── proteomics_solid_BX.912_cubist.rds │ ├── proteomics_solid_BX.912_dl.zip │ ├── proteomics_solid_BX.912_nnet.rds │ ├── proteomics_solid_BX.912_pcr.rds │ ├── proteomics_solid_BX.912_pls.rds │ ├── proteomics_solid_BX.912_rf.rds │ ├── proteomics_solid_BX.912_svm.rds │ ├── proteomics_solid_Cytarabine_bayes.rds │ ├── proteomics_solid_Cytarabine_cubist.rds │ ├── proteomics_solid_Cytarabine_dl.zip │ ├── proteomics_solid_Cytarabine_nnet.rds │ ├── proteomics_solid_Cytarabine_pcr.rds │ ├── proteomics_solid_Cytarabine_pls.rds │ ├── proteomics_solid_Cytarabine_rf.rds │ ├── proteomics_solid_Cytarabine_svm.rds │ ├── proteomics_solid_GSK269962A_bayes.rds │ ├── proteomics_solid_GSK269962A_cubist.rds │ ├── proteomics_solid_GSK269962A_dl.zip │ ├── proteomics_solid_GSK269962A_nnet.rds │ ├── proteomics_solid_GSK269962A_pcr.rds │ ├── proteomics_solid_GSK269962A_pls.rds │ ├── proteomics_solid_GSK269962A_rf.rds │ ├── proteomics_solid_GSK269962A_svm.rds │ ├── proteomics_solid_IOX2_bayes.rds │ ├── proteomics_solid_IOX2_cubist.rds │ ├── proteomics_solid_IOX2_dl.zip │ ├── proteomics_solid_IOX2_nnet.rds │ ├── proteomics_solid_IOX2_pcr.rds │ ├── proteomics_solid_IOX2_pls.rds │ ├── proteomics_solid_IOX2_rf.rds │ ├── proteomics_solid_IOX2_svm.rds │ ├── proteomics_solid_JQ12_bayes.rds │ ├── proteomics_solid_JQ12_cubist.rds │ ├── proteomics_solid_JQ12_dl.zip │ ├── proteomics_solid_JQ12_nnet.rds │ ├── proteomics_solid_JQ12_pcr.rds │ ├── proteomics_solid_JQ12_pls.rds │ ├── proteomics_solid_JQ12_rf.rds │ ├── proteomics_solid_JQ12_svm.rds │ ├── proteomics_solid_SN.38_bayes.rds │ ├── proteomics_solid_SN.38_cubist.rds │ ├── proteomics_solid_SN.38_dl.zip │ ├── proteomics_solid_SN.38_nnet.rds │ ├── proteomics_solid_SN.38_pcr.rds │ ├── proteomics_solid_SN.38_pls.rds │ ├── proteomics_solid_SN.38_rf.rds │ ├── proteomics_solid_SN.38_svm.rds │ ├── proteomics_solid_TGX221_bayes.rds │ ├── proteomics_solid_TGX221_cubist.rds │ ├── proteomics_solid_TGX221_dl.zip │ ├── proteomics_solid_TGX221_nnet.rds │ ├── proteomics_solid_TGX221_pcr.rds │ ├── proteomics_solid_TGX221_pls.rds │ ├── proteomics_solid_TGX221_rf.rds │ ├── proteomics_solid_TGX221_svm.rds │ ├── proteomics_solid_TPCA.1_bayes.rds │ ├── proteomics_solid_TPCA.1_cubist.rds │ ├── proteomics_solid_TPCA.1_dl.zip │ ├── proteomics_solid_TPCA.1_nnet.rds │ ├── proteomics_solid_TPCA.1_pcr.rds │ ├── proteomics_solid_TPCA.1_pls.rds │ ├── proteomics_solid_TPCA.1_rf.rds │ ├── proteomics_solid_TPCA.1_svm.rds │ ├── proteomics_solid_VX.11e_bayes.rds │ ├── proteomics_solid_VX.11e_cubist.rds │ ├── proteomics_solid_VX.11e_dl.zip │ ├── proteomics_solid_VX.11e_nnet.rds │ ├── proteomics_solid_VX.11e_pcr.rds │ ├── proteomics_solid_VX.11e_pls.rds │ ├── proteomics_solid_VX.11e_rf.rds │ └── proteomics_solid_VX.11e_svm.rds └── models_rna │ ├── rna_aml_BX.912_bayes.rds │ ├── rna_aml_BX.912_cubist.rds │ ├── rna_aml_BX.912_dl.zip │ ├── rna_aml_BX.912_nnet.rds │ ├── rna_aml_BX.912_pcr.rds │ ├── rna_aml_BX.912_pls.rds │ ├── rna_aml_BX.912_rf.rds │ ├── rna_aml_BX.912_svm.rds │ ├── rna_aml_Cytarabine_bayes.rds │ ├── rna_aml_Cytarabine_cubist.rds │ ├── rna_aml_Cytarabine_dl.zip │ ├── rna_aml_Cytarabine_nnet.rds │ ├── rna_aml_Cytarabine_pcr.rds │ ├── rna_aml_Cytarabine_pls.rds │ ├── rna_aml_Cytarabine_rf.rds │ ├── rna_aml_Cytarabine_svm.rds │ ├── rna_aml_GSK269962A_bayes.rds │ ├── rna_aml_GSK269962A_cubist.rds │ ├── rna_aml_GSK269962A_dl.zip │ ├── rna_aml_GSK269962A_nnet.rds │ ├── rna_aml_GSK269962A_pcr.rds │ ├── rna_aml_GSK269962A_pls.rds │ ├── rna_aml_GSK269962A_rf.rds │ ├── rna_aml_GSK269962A_svm.rds │ ├── rna_aml_IOX2_bayes.rds │ ├── rna_aml_IOX2_cubist.rds │ ├── rna_aml_IOX2_dl.zip │ ├── rna_aml_IOX2_nnet.rds │ ├── rna_aml_IOX2_pcr.rds │ ├── rna_aml_IOX2_pls.rds │ ├── rna_aml_IOX2_rf.rds │ ├── rna_aml_IOX2_svm.rds │ ├── rna_aml_JQ12_bayes.rds │ ├── rna_aml_JQ12_cubist.rds │ ├── rna_aml_JQ12_dl.zip │ ├── rna_aml_JQ12_nnet.rds │ ├── rna_aml_JQ12_pcr.rds │ ├── rna_aml_JQ12_pls.rds │ ├── rna_aml_JQ12_rf.rds │ ├── rna_aml_JQ12_svm.rds │ ├── rna_aml_S.Trityl.L.cysteine_bayes.rds │ ├── rna_aml_S.Trityl.L.cysteine_cubist.rds │ ├── rna_aml_S.Trityl.L.cysteine_dl.zip │ ├── rna_aml_S.Trityl.L.cysteine_nnet.rds │ ├── rna_aml_S.Trityl.L.cysteine_pcr.rds │ ├── rna_aml_S.Trityl.L.cysteine_pls.rds │ ├── rna_aml_S.Trityl.L.cysteine_rf.rds │ ├── rna_aml_S.Trityl.L.cysteine_svm.rds │ ├── rna_aml_SN.38_bayes.rds │ ├── rna_aml_SN.38_cubist.rds │ ├── rna_aml_SN.38_dl.zip │ ├── rna_aml_SN.38_nnet.rds │ ├── rna_aml_SN.38_pcr.rds │ ├── rna_aml_SN.38_pls.rds │ ├── rna_aml_SN.38_rf.rds │ ├── rna_aml_SN.38_svm.rds │ ├── rna_aml_TGX221_bayes.rds │ ├── rna_aml_TGX221_cubist.rds │ ├── rna_aml_TGX221_dl.zip │ ├── rna_aml_TGX221_nnet.rds │ ├── rna_aml_TGX221_pcr.rds │ ├── rna_aml_TGX221_pls.rds │ ├── rna_aml_TGX221_rf.rds │ ├── rna_aml_TGX221_svm.rds │ ├── rna_aml_TPCA.1_bayes.rds │ ├── rna_aml_TPCA.1_cubist.rds │ ├── rna_aml_TPCA.1_dl.zip │ ├── rna_aml_TPCA.1_nnet.rds │ ├── rna_aml_TPCA.1_pcr.rds │ ├── rna_aml_TPCA.1_pls.rds │ ├── rna_aml_TPCA.1_rf.rds │ ├── rna_aml_TPCA.1_svm.rds │ ├── rna_solid_BX.912_bayes.rds │ ├── rna_solid_BX.912_cubist.rds │ ├── rna_solid_BX.912_dl.zip │ ├── rna_solid_BX.912_nnet.rds │ ├── rna_solid_BX.912_pcr.rds │ ├── rna_solid_BX.912_pls.rds │ ├── rna_solid_BX.912_rf.rds │ ├── rna_solid_BX.912_svm.rds │ ├── rna_solid_Cytarabine_bayes.rds │ ├── rna_solid_Cytarabine_cubist.rds │ ├── rna_solid_Cytarabine_dl.zip │ ├── rna_solid_Cytarabine_nnet.rds │ ├── rna_solid_Cytarabine_pcr.rds │ ├── rna_solid_Cytarabine_pls.rds │ ├── rna_solid_Cytarabine_rf.rds │ ├── rna_solid_Cytarabine_svm.rds │ ├── rna_solid_JQ12_bayes.rds │ ├── rna_solid_JQ12_cubist.rds │ ├── rna_solid_JQ12_dl.zip │ ├── rna_solid_JQ12_nnet.rds │ ├── rna_solid_JQ12_pcr.rds │ ├── rna_solid_JQ12_pls.rds │ ├── rna_solid_JQ12_rf.rds │ ├── rna_solid_JQ12_svm.rds │ ├── rna_solid_TGX221_bayes.rds │ ├── rna_solid_TGX221_cubist.rds │ ├── rna_solid_TGX221_dl.zip │ ├── rna_solid_TGX221_nnet.rds │ ├── rna_solid_TGX221_pcr.rds │ ├── rna_solid_TGX221_pls.rds │ ├── rna_solid_TGX221_rf.rds │ ├── rna_solid_TGX221_svm.rds │ ├── rna_solid_TPCA.1_bayes.rds │ ├── rna_solid_TPCA.1_cubist.rds │ ├── rna_solid_TPCA.1_dl.zip │ ├── rna_solid_TPCA.1_nnet.rds │ ├── rna_solid_TPCA.1_pcr.rds │ ├── rna_solid_TPCA.1_pls.rds │ ├── rna_solid_TPCA.1_rf.rds │ ├── rna_solid_TPCA.1_svm.rds │ ├── rna_solid_VX.11e_bayes.rds │ ├── rna_solid_VX.11e_cubist.rds │ ├── rna_solid_VX.11e_dl.zip │ ├── rna_solid_VX.11e_nnet.rds │ ├── rna_solid_VX.11e_pcr.rds │ ├── rna_solid_VX.11e_pls.rds │ ├── rna_solid_VX.11e_rf.rds │ └── rna_solid_VX.11e_svm.rds └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | .RData 2 | .Rhistory 3 | .Rproj.user 4 | -------------------------------------------------------------------------------- /DRUMLR_Tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CutillasLab/DRUMLR/8a8b7dda23251ed47ae23fedd207eaf2f6281978/DRUMLR_Tutorial.pdf -------------------------------------------------------------------------------- /DRUMLR_code/.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | -------------------------------------------------------------------------------- /DRUMLR_code/DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: DRUMLR 2 | Type: Package 3 | Title: Drug Sensitivity Ranking Using Machine Learning R Package 4 | Version: 0.1.0 5 | Authors@R: c(person("Henry", "Gerdes", role = c("aut", "cre"), 6 | email = "h.gerdes@qmul.ac.uk"), 7 | person("Pedro", "Cutillas", role =c("aut", "cre"), 8 | email = "p.cutillas@qmul.ac.uk")) 9 | Author: Pedro Cutillas ["aut", "cre"], 10 | Henry Gerdes ["aut", "cre"] 11 | Maintainer: Henry Gerdes 12 | Description: Functions required to generate DRUML drug sensitivity prediction models. 13 | License: What license is it under? 14 | Encoding: UTF-8 15 | LazyData: FALSE 16 | Imports: 17 | arm, 18 | backports, 19 | dplyr, 20 | foreach, 21 | htmltools, 22 | doParallel, 23 | limma, 24 | caret, 25 | h2o, 26 | Cubist, 27 | pls, 28 | glmnet, 29 | kernlab, 30 | randomForest 31 | RoxygenNote: 7.1.2 32 | Depends: 33 | R (>= 2.10) 34 | -------------------------------------------------------------------------------- /DRUMLR_code/DRUMLR.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Package 19 | PackageUseDevtools: Yes 20 | PackageInstallArgs: --no-multiarch --with-keep.source 21 | PackageRoxygenize: rd,collate,namespace 22 | -------------------------------------------------------------------------------- /DRUMLR_code/NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(.BuildModel) 4 | export(BuildDRUMLs) 5 | export(BuildEMDR) 6 | export(DrugMarkerEnrichment) 7 | export(FilterSensitivity) 8 | export(MinMaxNormalise) 9 | export(OptimiseMLInput) 10 | export(PredictDRUML) 11 | export(PredictDRUML_example) 12 | export(RemoveRepeatNo) 13 | -------------------------------------------------------------------------------- /DRUMLR_code/R/BuildDRUMLs.R: -------------------------------------------------------------------------------- 1 | #' Title 2 | #' @title Build DRUML models for a list of drugs 3 | #' 4 | #' @param df_input dataframe of input data. 5 | #' @param .marker_database Markers database which will be used to for Drug Marker Enrichment 6 | #' @param drugs list of drugs from which models will be generated. If left NULL models will be built for all drugs in the marker database. 7 | #' @param df_response Dataframe of drug responses(AAC) with cell lines as row names and column names as drugs. 8 | #' @param models list of models to use for machine learning: 9 | #' \itemize{ 10 | #' \item glm = Baysian GLM model 11 | #' \item cubist = Cubist model 12 | #' \item pls = Partial list squares model 13 | #' \item pcr = Principal components regression 14 | #' \item nnet = Neural network model 15 | #' \item scm = Super vector machines 16 | #' \item rf = Random forests regressor model 17 | #' \item dl = Deep learning model using h2o package (needs Java to be up to date) 18 | #' } 19 | #' @param save_path save location for models and results csvs 20 | #' @param save_csv if TRUE csv files containing DRUML results will be made 21 | #' @param computational_load Set as a decimal fraction of the number of cores you which to be recruited for model building e.g a value of 0.8 means 80\% of all available CPU cores will be used to build models. If left as NULL only 1 core will be used. 22 | #' @param method Statistical method for filtering distance markers for DRUML models default is spearman 23 | #' @param max_n_D maximum number of markers to use 24 | #' @param min_n_D Minimum number of markers to use 25 | #' @param order_var variable used to filter Distance models for DRUML: 26 | #' \itemize{ 27 | #' \item p = p value 28 | #' \item r = r value 29 | #' \item slope = slope of fitted linear model 30 | #' } 31 | #' @name BuildDRUMLs 32 | #' @export BuildDRUMLs 33 | #' 34 | #' @examples 35 | 36 | BuildDRUMLs <- function(df_input, 37 | .marker_database, 38 | df_response, 39 | drugs = NULL, 40 | input_type = "DRUML_model", 41 | save_path = "DRUML_models", 42 | save_csv = T, 43 | scale = T, 44 | computational_load = 0.8, 45 | partition.ratio = 0.7, 46 | models = c("glm", "cubist", "pls", "pcr", "nnet", "svm", "rf", "dl"), 47 | corr_method = "spearman", 48 | max_n_D = 30, 49 | min_n_D = 7 , 50 | order_var = "r") { 51 | if ("dplyr" %in% (.packages()) == FALSE) { 52 | library(dplyr) 53 | } 54 | if ("caret" %in% (.packages()) == FALSE) { 55 | library(caret) 56 | } 57 | if ("h2o" %in% (.packages()) == FALSE) { 58 | library(h2o) 59 | } 60 | 61 | if ("foreach" %in% (.packages()) == FALSE) { 62 | library(foreach) 63 | } 64 | 65 | if ("doParallel" %in% (.packages()) == FALSE) { 66 | library(doParallel) 67 | } 68 | 69 | if (is.null(drugs)) { 70 | drugs <- colnames(df_response) 71 | } 72 | 73 | #Initiate multicore processing if specified 74 | if (!is.null(computational_load)) { 75 | cores <- 76 | as.integer((computational_load * parallel::detectCores()), length = 1) 77 | registerDoParallel(cores = cores) 78 | print(paste("running on", cores, "cores")) 79 | } 80 | 81 | #make save_path directory if required 82 | if (!dir.exists(save_path)) { 83 | dir.create(save_path) 84 | print(paste(save_path, "created")) 85 | } 86 | 87 | #Generate distance values for input data 88 | df_distance <- DRUMLR::DrugMarkerEnrichment(df = df_input, 89 | marker_database = .marker_database) 90 | 91 | #transpose distances for ML models 92 | df_distance <- t(df_distance) %>% data.frame(stringsAsFactors = F) 93 | 94 | #Get list of cell lines by removing repeatnumber 95 | cell_lines <- DRUMLR::RemoveRepeatNo(rownames(df_distance)) 96 | 97 | #.BuildallDRUML is an internal function for building ML models for each drug 98 | .BuildallDRUML <- function(.drug) { 99 | #add response data and remove na results 100 | df_input <- df_distance %>% data.frame() 101 | response <- df_response[cell_lines, .drug, drop = F] 102 | df_input[, "response"] <- response 103 | df_input <- na.omit(df_input) 104 | response <- df_input[, "response"] 105 | #identify top 10 sensitive and resistant markers 106 | 107 | #optimise the distance values which will be used as a ML input 108 | op_markers <- DRUMLR::OptimiseMLInput( 109 | df_train = df_input, 110 | method = corr_method, 111 | max_n_D = max_n_D, 112 | min_n_D = min_n_D, 113 | partition.ratio = partition.ratio, 114 | return_corr_analysis = F, 115 | order_var = order_var 116 | ) 117 | 118 | print(paste(.drug, "markers optimised")) 119 | 120 | #combine markers to build training input data frame 121 | df_input <- df_input[, c(op_markers[["sensitive drug markers"]], 122 | op_markers[["resistant drug markers"]], 123 | "response")] 124 | 125 | #Scale the inputs by cell line 126 | df_input <- df_input[,colnames(df_input)!="response"] %>% DRUMLR::MinMaxNormalise(.margin = 1) 127 | df_input[, "response"] <- response 128 | 129 | #split data into training and testing subsets 130 | train.set <- caret::createDataPartition(df_input[, "response"], 131 | p = partition.ratio, 132 | list = FALSE, 133 | times = 1) 134 | 135 | 136 | #make training dataset for model 137 | df_train <- df_input[train.set, ] 138 | df_test <-df_input[-train.set[, 1],] 139 | 140 | #build models using the .BuildModel function 141 | DRUML_mods <- 142 | lapply( 143 | models, 144 | FUN = function(x) { 145 | #DRUMLR:: 146 | DRUMLR::.BuildModel( 147 | df_train = df_train, 148 | df_test = df_test, 149 | model_type = x, 150 | drug = .drug, 151 | input_type = input_type, 152 | save_path = save_path, 153 | ) 154 | } 155 | ) 156 | 157 | 158 | #Reorganize ML building output to build performance results csvs DRUML models 159 | n_list <- c(1:length(DRUML_mods)) 160 | mod_infos <- 161 | lapply( 162 | n_list, 163 | FUN = function(x) { 164 | DRUML_mods[[x]]["model_info"][[1]] 165 | } 166 | ) %>% dplyr::bind_rows() %>% data.frame(stringsAsFactors = F) 167 | mod_results <- 168 | lapply( 169 | n_list, 170 | FUN = function(x) { 171 | DRUML_mods[[x]]["model_results"][[1]] 172 | } 173 | ) %>% dplyr::bind_rows() %>% data.frame(stringsAsFactors = F) 174 | 175 | colnames(mod_infos) <- gsub(x = colnames(mod_infos), pattern = "model_info.",replacement = "", fixed = T) 176 | colnames(mod_results) <- gsub(x = colnames(mod_results), pattern = "model_results.",replacement = "", fixed = T) 177 | 178 | mod_infos$res_markers <- 179 | paste(op_markers$`resistant drug markers`, collapse = ";") %>% unlist() 180 | mod_infos$sens_markers <- 181 | paste(op_markers$`sensitive drug markers`, collapse = ";") %>% unlist() 182 | 183 | 184 | output <- list(mod_infos, mod_results) 185 | names(output) <- c("mod_info", "mod_results") 186 | 187 | return(output) 188 | } 189 | 190 | #if dl models are being h2o will be initiated 191 | if ("dl" %in% models){ 192 | h2o.init() 193 | } 194 | 195 | print("Building models") 196 | #drugs <- drugs[1:5] 197 | if (is.null(computational_load)) { 198 | DRUML_output <- 199 | foreach::foreach( 200 | i = drugs, 201 | .inorder = T, 202 | .errorhandling = "pass", 203 | .combine = "c", 204 | .packages = c( 205 | "dplyr", 206 | "caret", 207 | "Cubist", 208 | "pls", 209 | "h2o", 210 | "glmnet", 211 | "kernlab", 212 | "randomForest" 213 | ) 214 | ) %do% { 215 | out <- .BuildallDRUML(.drug = i) 216 | return(out) 217 | } 218 | } else{ 219 | DRUML_output <- 220 | foreach::foreach( 221 | i = drugs, 222 | .inorder = T, 223 | .errorhandling = "pass", 224 | .combine = "c", 225 | .packages = c( 226 | "dplyr", 227 | "arm", 228 | "h2o", 229 | "caret", 230 | "Cubist", 231 | "pls", 232 | "glmnet", 233 | "kernlab", 234 | "randomForest" 235 | ) 236 | ) %dopar% { 237 | out <- .BuildallDRUML(.drug = i) 238 | return(out) 239 | } 240 | } 241 | 242 | print("models built") 243 | 244 | #Organise DRUML output into results and paths dfs 245 | DRUML_infos <- 246 | DRUML_output[names(DRUML_output) %in% "mod_info"] %>% dplyr::bind_rows(.id = "") %>% data.frame(stringsAsFactors = F) 247 | DRUML_results <- 248 | DRUML_output[names(DRUML_output) %in% "mod_results"] %>% dplyr::bind_rows(.id = "") %>% data.frame(stringsAsFactors = F) 249 | 250 | 251 | #make names for path and result outputs 252 | paths <- paste(input_type, "model_paths", sep = "_") 253 | results <- paste(input_type, "model_results", sep = "_") 254 | 255 | 256 | #write csv files and save in model directory 257 | if (save_csv) { 258 | write.csv(file = paste(save_path, paste(paths, "csv", sep = "."), sep = "/"), x = DRUML_infos) 259 | write.csv(file = paste(save_path, paste(results, "csv", sep = "."), sep = "/"), x = DRUML_results) 260 | print(paste("csv files saved in", save_path)) 261 | } 262 | 263 | output <- list(DRUML_infos, DRUML_results) 264 | names(output) <- c(paths, results) 265 | 266 | #close mulitcore processing 267 | if (is.null(computational_load) == F) { 268 | doParallel::stopImplicitCluster() 269 | } 270 | 271 | #if DL models were built user will be given the option to close h2o connection 272 | if ("dl" %in% models){ 273 | h2o.shutdown() 274 | 275 | } 276 | 277 | return(output) 278 | } 279 | 280 | -------------------------------------------------------------------------------- /DRUMLR_code/R/BuildEMDR.R: -------------------------------------------------------------------------------- 1 | #GenerateEMSR 2 | # Empirical Markers of Sensitivity of Drug Response 3 | #' @name BuildEMDR 4 | #' @export BuildEMDR 5 | #' @title Empirical Markers of Drug Response 6 | #' @param df_input input data with cell lines as colnames and varaibles as rownames 7 | #' @param df_response response values dataframe with colnames as cell lines and drugs as column 8 | #' @param drug drug generate markers for 9 | #' @param p.cutoff P value threshold to define a repeat comparison as significant 10 | #' @param fold.cut.off fold value threshold to define a repeat comparison direction 11 | #' @param resampling.times number of resampling times 12 | #' @param return_limma Set as TRUE if for limma analysis results to be returned 13 | #' @param scale_input Set as TRUE to scale input data 14 | #' @param computational_load Set as a decimal fraction of cores you want to use. If left as NULL only 1 core will be used. 15 | 16 | BuildEMDR <- function(df_input = NULL, 17 | df_response = NULL, 18 | drugs, 19 | p.cutoff = 0.05, 20 | fold.cut.off = 1, 21 | resampling.times = 5, 22 | return_limma = F, 23 | computational_load = NULL, 24 | nfolds = 10, 25 | maxmarker_res = NULL, 26 | maxmarker_sens = NULL) { 27 | 28 | #Ensure relevent libraries are in the environment 29 | if ("dplyr" %in% (.packages()) == FALSE) { 30 | library(dplyr) 31 | } 32 | if ("limma" %in% (.packages()) == FALSE) { 33 | library(dplyr) 34 | } 35 | if ("foreach" %in% (.packages()) == FALSE) { 36 | library(foreach) 37 | } 38 | if ("doParallel" %in% (.packages()) == FALSE) { 39 | library(doParallel) 40 | } 41 | #################################################################################### 42 | #internal functions for building models 43 | ##################################################################################### 44 | 45 | #Function for Limma analysis 46 | .LimmaEMSR <- function(ii, .fm.up, .fm.do, .df_res, .df_sens){ 47 | 48 | #get sensitivity and resistance inputs from folds 49 | sensitive_cells <- 50 | .fm.up[grid[ii, "Var1"]][[1]] 51 | resistant_cells <- 52 | .fm.do[grid[ii, "Var2"]][[1]] 53 | df1 <- 54 | .df_sens[, sensitive_cells, drop = F] 55 | df2 <- 56 | .df_res[, resistant_cells, drop = F] 57 | 58 | #combine senstitive and resistant dataframes 59 | dfall <- 60 | cbind(df1, df2) 61 | 62 | #label resistant and sensitive lines 63 | sensitivity <- 64 | c(1, 2)[as.factor(unclass(colnames(dfall) %in% colnames(df_sens)))] 65 | 66 | #carry out limma analysis 67 | design <- 68 | stats::model.matrix( ~ 0 + factor(c(sensitivity))) 69 | colnames(design) <- 70 | c("sensitive", "resistant") 71 | contrast.matrix <- 72 | limma::makeContrasts(sensitive - resistant, 73 | levels = 74 | design) 75 | 76 | fit <- 77 | limma::lmFit(dfall, design) 78 | fit2 <- 79 | limma::contrasts.fit(fit, contrast.matrix) 80 | fit2 <- 81 | limma::eBayes(fit2) 82 | 83 | fvals <- 84 | data.frame(fit2$coefficients) 85 | 86 | print(paste("fold ", ii, " analysed")) 87 | out <- fvals 88 | colnames(out) <- ii 89 | 90 | return(out) 91 | } 92 | 93 | #Internal function for generating marker distances 94 | .GetDistances <- function(i) { 95 | 96 | x <- grid[i, "Var1"] 97 | y <- grid[i, "Var2"] 98 | 99 | #draw in markers 100 | sens_m <- sens_markers[[x]] 101 | res_m <- res_markers[[y]] 102 | 103 | #remove any Na values 104 | sens_m <- sens_m[!is.na(sens_m)] 105 | res_m <- res_m[!is.na(res_m)] 106 | sens <- df_input[sens_m,] 107 | 108 | #calculate distance 109 | s_med <- apply( 110 | sens, 111 | 2, 112 | FUN = function(x) 113 | (median(x, na.rm = T)) 114 | ) 115 | s_q3 <- 116 | apply( 117 | sens, 118 | 2, 119 | FUN = function(x) 120 | (quantile( 121 | x, probs = 0.75, na.rm = T 122 | )) 123 | ) 124 | 125 | res <- df_input[res_m,] 126 | r_med <- apply( 127 | res, 128 | 2, 129 | FUN = function(x) 130 | (median(x, na.rm = T)) 131 | ) 132 | r_q3 <- 133 | apply( 134 | res, 135 | 2, 136 | FUN = function(x) 137 | (quantile( 138 | x, probs = 0.75, na.rm = T 139 | )) 140 | ) 141 | 142 | D <- data.frame((s_med - r_med) + (s_q3 - r_q3)) 143 | colnames(D) <- i 144 | return(D) 145 | } 146 | 147 | #Internal function of analysing marker distance correlations with drug sensitivity 148 | .SpearmanMarkerAnalysis <- function(iii) { 149 | 150 | cc <- 151 | cor.test(df_dist[,iii], 152 | df_dist[,"response"], 153 | method = "spearman", 154 | complete.cases = TRUE, 155 | exact = FALSE) 156 | out <- 157 | data.frame("pvalue" = cc$p.value, 158 | "estimate" = cc$estimate) 159 | out$rep <- iii 160 | return(out) 161 | } 162 | ##################################################################################### 163 | 164 | #Verify rownames are variables 165 | if (is.character(rownames(df_input)) == F & 166 | is.factor(rownames(df_input)) == F) { 167 | print("rownames are not variable names") 168 | errorCondition(message = "rownames are not variable names") 169 | stop() 170 | } 171 | 172 | #Initiate multicore processing if specified 173 | if (is.null(computational_load) == F) { 174 | cores <- round((computational_load * parallel::detectCores()), 0) 175 | registerDoParallel(cores = cores) 176 | print(paste("running on", cores, "cores")) 177 | } 178 | 179 | #convert column names into cell lines 180 | col_cell_lines <- DRUMLR::RemoveRepeatNo(colnames(df_input)) 181 | 182 | #remove cell lines not present in database 183 | df_response_ori <- 184 | df_response[,colnames(df_response) %in% col_cell_lines, drop=F] 185 | 186 | if (is.null(drugs)){ 187 | drugs <- rownames(df_response) 188 | } 189 | 190 | 191 | ###################################################################################################################################### 192 | #loop for generating markers 193 | ###################################################################################################################################### 194 | marker_db <- foreach(i = drugs, 195 | .inorder = T, 196 | .errorhandling = "remove", 197 | .combine = "rbind", 198 | .packages = c("dplyr", "DRUMLR", "caret", "foreach"))%do%{ 199 | 200 | #store drug name as drug 201 | drug <- i 202 | 203 | #Split Cell lines into resistant and sensitive groups 204 | df_response <- df_response_ori[drug, ,drop = F] %>% t()%>% data.frame() %>% na.omit() 205 | 206 | #sort cells into sensitive and resistant groups using median values 207 | median_filter <- 208 | df_response[, drug] >= median(df_response[, drug], na.rm = T) 209 | 210 | #make sensitive data.frame 211 | sensitive_cells <- 212 | rownames(df_response)[median_filter] %>% toupper() 213 | df_sens <- df_input[, col_cell_lines %in% sensitive_cells] 214 | 215 | #make resistant data.frame 216 | resistant_cells <- 217 | rownames(df_response)[!median_filter] %>% toupper() 218 | 219 | df_res <- df_input[, col_cell_lines %in% resistant_cells] 220 | 221 | print("Cells split into sensitive and resistant groups") 222 | 223 | # split sensitive and resistant cells into k*rs groups 224 | fm.up <- 225 | caret::createMultiFolds(colnames(df_sens), k = 2, times = resampling.times) 226 | fm.do <- 227 | caret::createMultiFolds(colnames(df_res), k = 2, times = resampling.times) 228 | 229 | #build tuning grid for markers 230 | grid <- expand.grid(1:length(fm.up), 1:length(fm.do)) 231 | sumfolds <- nrow(grid) 232 | 233 | #begin EMSR analysis 234 | print( 235 | paste( 236 | Sys.time(), 237 | ":Initiating", 238 | drug, 239 | "EMSR analysis. Analysing", 240 | sumfolds, 241 | "folds" 242 | ) 243 | ) 244 | 245 | # filter variables with <3 markers 246 | bfold <- lengths(fm.up) >=3 & lengths(fm.do) >=3 247 | fm.up <- fm.up[bfold] 248 | fm.do <- fm.do[bfold] 249 | 250 | #Begin Limma analysis of fold change 251 | startt <- Sys.time() 252 | 253 | df_folds <- foreach::foreach( 254 | i = rownames(grid), 255 | .inorder = T, 256 | .errorhandling = "pass", 257 | .packages = c("limma", "dplyr"), 258 | .combine = "cbind" 259 | ) %dopar% { 260 | 261 | .LimmaEMSR(ii=i, .fm.up = fm.up, .fm.do = fm.do, .df_res = df_res, .df_sens= df_sens) 262 | } 263 | 264 | print(paste(Sys.time(), ":Processing EMSR results")) 265 | #split data into fold and pvalue data 266 | 267 | #count number of iterations which satisfied pvalue and fold thresholds 268 | ############################################################################################################### 269 | 270 | #get average fold values 271 | mean_fold <- 272 | apply( 273 | df_folds, 274 | 1, 275 | FUN = function(x) { 276 | mean(x, na.rm = T) 277 | } 278 | ) 279 | 280 | #get fold up and down regulations 281 | df_folds[df_folds < fold.cut.off & df_folds > -(fold.cut.off)] <- NA 282 | df_folds[df_folds >= fold.cut.off] <- 1 283 | df_folds[df_folds <= -(fold.cut.off)] <- -1 284 | 285 | df_limma <- data.frame( 286 | "ratio" = apply(df_folds, 1, function(x){sum(x, na.rm = T)}), 287 | "mean_fold" = mean_fold 288 | ) 289 | 290 | 291 | ################################################################################ 292 | # Begin optimisation of the sensitive and resistant markers 293 | ################################################################################ 294 | 295 | #alter resistant fold values so they increase with significance 296 | res_m <- df_limma[df_limma$ratio <0,]* -(1) 297 | sens_m <- df_limma[df_limma$ratio >0,] 298 | 299 | #check to see if there are enough significant markers to continue analysis 300 | if(nrow(res_m)<10|nrow(res_m)<10){ 301 | out <- data.frame( 302 | "drugs" = drug, 303 | "m_sens" = 0, 304 | "m_res" = 0, 305 | "senstive_markers" = "no sig markers", 306 | "resistant_markers" ="no sig markers") 307 | 308 | }else{ 309 | 310 | #reorder markers by mean fold 311 | res_m <- res_m[order(res_m[, "mean_fold"], res_m[, "ratio"], decreasing = T), ] 312 | res_m_names <- rownames(res_m) 313 | sens_m <- sens_m[order(sens_m[, "mean_fold"], sens_m[, "ratio"], decreasing = T), ] 314 | sens_m_names <- rownames(sens_m) 315 | #identify testing paramaters for marker lengths 316 | if (is.null(maxmarker_res)) { 317 | maxmarker_res <- nrow(res_m) 318 | } 319 | if (is.null(maxmarker_sens)) { 320 | maxmarker_sens <- nrow(sens_m) 321 | } 322 | 323 | sens_test <- 324 | seq( 325 | from = 5, 326 | to = maxmarker_sens, 327 | by = round(maxmarker_sens / nfolds, 0) 328 | ) #%>% list() 329 | res_test <- 330 | seq( 331 | from = 5, 332 | to = maxmarker_res, 333 | by = round(maxmarker_res / nfolds, 0) 334 | ) #%>% list() 335 | 336 | print("generating markers lists") 337 | #generate list of markers using lengths of sens_test and res_test 338 | sens_markers <- 339 | lapply( 340 | sens_test, 341 | FUN = function(x) { 342 | sens_m_names[1:x] 343 | } 344 | ) 345 | res_markers <- 346 | lapply( 347 | res_test, 348 | FUN = function(x){ 349 | res_m_names[1:x] 350 | 351 | } 352 | ) 353 | 354 | #build tuning grid for foreachf unction 355 | grid <- expand.grid(1: length(sens_markers), 1:length(res_markers)) 356 | 357 | #get distances using marker lists 358 | df_dist <- foreach( 359 | i = rownames(grid), 360 | .inorder = T, 361 | .combine = "cbind", 362 | .errorhandling = "pass", 363 | .packages = "dplyr" 364 | ) %dopar%{ 365 | 366 | out <- .GetDistances(i) %>% data.frame(stringsAsFactors = F) 367 | print(paste( 368 | "Distances complete for comparison: ", 369 | i, 370 | "/", 371 | nrow(grid), 372 | sep = "" 373 | )) 374 | return(out) 375 | } 376 | 377 | print("Begininning spearman correlation analysis of marker groups") 378 | 379 | #get response values for optimisation 380 | df_dist[, "response"] <- data.frame(df_response)[col_cell_lines,] 381 | 382 | #analyse correlation between distances and drug sensitivity using spearman ranking on multiple cores 383 | marker_analysis <- foreach( 384 | i = 1:(ncol(df_dist)-1), 385 | .inorder = T, 386 | .combine = "rbind", 387 | .errorhandling = "pass", 388 | .packages = c("dplyr") 389 | ) %dopar%{ 390 | 391 | out <- .SpearmanMarkerAnalysis(iii = i) 392 | print(paste( 393 | "Spearman analysis complete for comparison: ", 394 | i, 395 | "/", 396 | ncol(df_dist), 397 | sep = "" 398 | )) 399 | return(out) 400 | } 401 | 402 | #get numbers of markers used for each comparison 403 | marker_analysis$rep <- 1:nrow(marker_analysis) 404 | 405 | marker_analysis$n_sens <- 406 | lapply( 407 | marker_analysis$rep, 408 | FUN = function(x) { 409 | sens_test[grid[x, "Var1"]] 410 | } 411 | ) %>% unlist() %>% as.double() 412 | 413 | marker_analysis$n_res <- 414 | lapply( 415 | marker_analysis$rep, 416 | FUN = function(x) { 417 | res_test[grid[x, "Var2"]] 418 | } 419 | ) %>% unlist() %>% as.double() 420 | 421 | #identify optimal marker length combination 422 | optimal <- 423 | marker_analysis[marker_analysis$pvalue == min(marker_analysis$pvalue),] 424 | 425 | #if multiple combinations generate optimal marker combinations use the one with the fewest total markers 426 | if (nrow(optimal) > 1) { 427 | tot <- optimal$n_sens + optimal$n_res 428 | optimal <- optimal[tot %in% min(tot),] 429 | } 430 | 431 | #generate marker output 432 | print("analysis complete") 433 | out <- data.frame( 434 | "drugs" = drug, 435 | "m_sens" = optimal$n_sens, 436 | "m_res" = optimal$n_res, 437 | "senstive_markers" = paste(sens_markers[[grid[optimal$rep, "Var1"]]], collapse = "-"), 438 | "resistant_markers" = paste(res_markers[[grid[optimal$rep, "Var2"]]], collapse = "-"), 439 | stringsAsFactors = F) 440 | 441 | 442 | colnames(out) <- c("drugs", "m_sens", "m_res", "sensitive_markers", "resistant_markers") 443 | 444 | } 445 | return(out) 446 | } 447 | 448 | if (is.null(computational_load) == F) { 449 | doParallel::stopImplicitCluster() 450 | } 451 | 452 | return(marker_db) 453 | } 454 | 455 | -------------------------------------------------------------------------------- /DRUMLR_code/R/BuildModels.R: -------------------------------------------------------------------------------- 1 | #' Title Internal function for building machine learning models 2 | #' @title Internal Build model function 3 | #' @name .BuildModel 4 | #' @export .BuildModel 5 | #' @param df_input dataframe to use for model training. Response value must be in column named response. 6 | #' @param partition.ratio partition ratio for making testing dataset 7 | #' @param model_type string of model type to build e.g cubist, glm, nnet, pcr, pls, rf and svm 8 | #' @param drug drug to use for naming model 9 | #' @param input_type input type to use for naming model 10 | #' @param save_path save file location 11 | #' @param metric error metric to use to build model 12 | #' @examples .BuildModel(df_input = df_distances, model_type = "rf", drug = "ABT-199", save_path = "my_model_dir") 13 | 14 | 15 | .BuildModel <- function(df_train, 16 | df_test, 17 | model_type, 18 | drug, 19 | input_type, 20 | save_path, 21 | metric = "RMSE", 22 | initiate_h2o = T) { 23 | require(arm) 24 | require(checkmate) 25 | require(backports) 26 | #set seed 27 | set.seed(123) 28 | 29 | #set build start time 30 | build_start <- Sys.time() 31 | 32 | if (model_type == "glm") { 33 | #build training control 34 | control <- trainControl(method = "repeatedcv", 35 | number = 5, 36 | repeats = 1) 37 | #build tuning grid 38 | grid <- 39 | expand.grid(C = c(0, 0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 5)) 40 | 41 | #Build Bayes glm model 42 | model <- caret::train( 43 | response ~ ., 44 | data = df_train, 45 | method = 'bayesglm', 46 | #tunegrid=grid, 47 | trControl = control 48 | ) 49 | 50 | } else if (model_type == "cubist") { 51 | #generate seeds 52 | seeds <- vector(mode = "list", length = nrow(df_train) + 1) 53 | seeds <- lapply(seeds, function(x) 54 | 1:20) 55 | 56 | #build training control 57 | train_control <- 58 | caret::trainControl(method = "repeatedcv", 59 | number = 3, 60 | seeds = seeds) 61 | 62 | # cubist tuning grid 63 | grid <- expand.grid(committees = c(1, 10, 50, 100), 64 | neighbors = c(0, 1, 5, 9)) 65 | #build cubist model 66 | model <- caret::train( 67 | response ~ ., 68 | data = df_train, 69 | method = "cubist", 70 | metric = metric, 71 | trControl = train_control 72 | ) 73 | } else if (model_type == "nnet") { 74 | #build training control 75 | control <- trainControl(method = "repeatedcv", 76 | number = 10, 77 | repeats = 3) 78 | 79 | #make tuning grid 80 | tunegrid <- expand.grid(expand.grid( 81 | size = c(1, 5, 10, 100), 82 | decay = c(0.1, 0.2, 0.5, 1) 83 | )) 84 | 85 | #make neural net model 86 | model <- train( 87 | response ~ ., 88 | data = df_train, 89 | method = "nnet", 90 | metric = metric, 91 | #tuneGrid=tunegrid, 92 | trControl = control, 93 | trace = F, 94 | maxit = 2000, 95 | linout = FALSE 96 | ) 97 | 98 | } else if (model_type == "pcr") { 99 | #build training control 100 | train_control <- trainControl(method = "LOOCV") 101 | 102 | #build Principal Components regression model 103 | model <- train( 104 | response ~ ., 105 | data = df_train, 106 | method = "pcr", 107 | metric = metric, 108 | tuneLength = 7, 109 | maximize = TRUE, 110 | trControl = train_control 111 | ) 112 | 113 | } else if (model_type == "pls") { 114 | #build training control 115 | train_control <- trainControl(method = "LOOCV") 116 | 117 | #build Partial List Squares model 118 | model <- train( 119 | response ~ ., 120 | data = df_train, 121 | method = "pls", 122 | metric = "RMSE", 123 | tuneLength = 7, 124 | maximize = TRUE, 125 | trControl = train_control 126 | ) 127 | 128 | 129 | } else if (model_type == "rf") { 130 | #make training control 131 | control <- 132 | trainControl(method = "repeatedcv", 133 | number = 10, 134 | repeats = 3) 135 | 136 | #make tuning grid for random forests 137 | mtry <- c(1:5) 138 | tunegrid <- expand.grid(.mtry = mtry) 139 | #tunegrid <- expand.grid(.mtry=c(1:15), .ntree=c(1000, 1500, 2000, 2500)) 140 | 141 | #build random forest model 142 | model <- train( 143 | response ~ ., 144 | data = df_train, 145 | method = "rf", 146 | metric = metric, 147 | tuneGrid = tunegrid, 148 | trControl = control, 149 | ntree = 1000, 150 | importance = TRUE 151 | ) 152 | 153 | 154 | 155 | } else if (model_type == "svm") { 156 | #build training control 157 | control <- 158 | trainControl(method = "repeatedcv", 159 | number = 10, 160 | repeats = 3) 161 | 162 | #build tuning grid 163 | mtry <- c(1:5) 164 | tunegrid <- expand.grid(C = c(0, 0.000001, 0.00001, 0.0001)) 165 | 166 | #build svm model 167 | model <- train( 168 | response ~ ., 169 | data = df_train, 170 | #method="svmRadial", 171 | method = "svmLinear", 172 | metric = metric, 173 | #tuneGrid=tunegrid, 174 | Cost = 0.0001, 175 | trControl = control, 176 | tuneLengh = 100 177 | ) 178 | 179 | 180 | }else if(model_type == "dl"){ 181 | if(initiate_h2o==T){ 182 | h2o::h2o.init() 183 | } 184 | #convert training and testing df to h2o frames 185 | df_train.h2o <- df_train %>% as.h2o() 186 | df_test.h2o <- df_test %>% as.h2o() 187 | #build model 188 | model = h2o.deeplearning(x = setdiff(colnames(df_train), 189 | c("response")), 190 | y = "response", 191 | training_frame=df_train.h2o, 192 | validation_frame=df_test.h2o, 193 | #hidden=c(32,32,32), ## small network, runs faster 194 | epochs=10000, ## hopefully converges earlier... 195 | score_validation_samples=10000, ## sample the validation dataset (faster) 196 | stopping_rounds=2, 197 | stopping_metric="RMSE", 198 | stopping_tolerance=0.01, 199 | variable_importances=T) 200 | } 201 | 202 | #Get true responses 203 | df_test_resp <- df_test[, "response", drop = F] 204 | df_train_resp <- df_train[, "response", drop = F] 205 | df_test <- df_test[,!colnames(df_test) %in% "response"] 206 | df_train <- df_train[,!colnames(df_train) %in% "response"] 207 | 208 | #make model save names 209 | model_name = paste(input_type, drug, model_type, sep = "_") 210 | model_path = paste(save_path, "/", model_name, ".rds", sep = "") 211 | 212 | #predict and save models- h2o models require different code to all others 213 | if(model_type=="dl"){ 214 | 215 | #make tested dataframe 216 | df_test.h2o <- as.h2o(df_test) 217 | df_train.h2o <-as.h2o(df_train) 218 | 219 | #predict responses using model 220 | predicted_response_test <- 221 | predict(model, newdata = df_test.h2o) %>% as.data.frame() 222 | predicted_response_train <- 223 | predict(model, newdata = df_train.h2o) %>% as.data.frame() 224 | 225 | #save DL model 226 | dest <- h2o::h2o.save_mojo( 227 | object = model, 228 | path = model_path) 229 | 230 | file.rename(dest, paste(save_path, "/", model_name, ".zip", sep = "")) 231 | file.remove(model_path) 232 | 233 | }else{ 234 | 235 | #predict responses using model 236 | predicted_response_test <- 237 | predict(model, newdata = df_test[,!colnames(df_train) %in% "response"]) 238 | predicted_response_train <- 239 | predict(model, newdata = df_train[,!colnames(df_train) %in% "response"]) 240 | 241 | #save model in correct location 242 | saveRDS(object = model, 243 | file = model_path, 244 | compress = "xz") 245 | } 246 | 247 | #output results into dataframe 248 | df_err_tr <- 249 | data.frame( 250 | sample = DRUMLR::RemoveRepeatNo(rownames(df_train)), 251 | measured_response = df_train_resp, 252 | predicted_response = predicted_response_train, 253 | data.set = "training" 254 | ) 255 | 256 | df_err_ts <- 257 | data.frame( 258 | sample = DRUMLR::RemoveRepeatNo(rownames(df_test)), 259 | measured_response = df_test_resp, 260 | predicted_response = predicted_response_test, 261 | data.set = "testing" 262 | ) 263 | colnames(df_err_tr) <- c("sample", "measured_response", "predicted_response", "data.set") 264 | colnames(df_err_ts) <- c("sample", "measured_response", "predicted_response", "data.set") 265 | df_results <- rbind(df_err_tr, df_err_ts) 266 | 267 | #rename columns 268 | colnames(df_results) <- 269 | c("sample" , "measured_response", "predicted_response") 270 | 271 | #add additional error calculations 272 | df_results$accuracy <- 273 | (df_results$measured_response - df_results$predicted_response) / df_results$measured_response * 274 | 100 275 | df_results$SE <- 276 | (df_results$measured_response - df_results$predicted_response) ^ 2 277 | df_results$AbsError <- 278 | abs(df_results$measured_response - df_results$predicted_response) 279 | df_results$model <- model_type 280 | 281 | 282 | #get errors data frame 283 | mod_rmse = data.frame(val.rmse = RMSE(df_err_ts[, "measured_response"], df_err_ts[, "predicted_response"], na.rm = T)) 284 | 285 | #build dataframe with model info 286 | model_info <- data.frame( 287 | model_path = model_name, 288 | model_type = model_type, 289 | input_type = input_type, 290 | drug = drug, 291 | rmse = mod_rmse 292 | ) 293 | 294 | #return dataframes of model info, results table and results error 295 | out <- list(model_info, df_results) 296 | names(out) <- c("model_info", "model_results") 297 | 298 | #print model information 299 | comp_time <- paste(round(Sys.time() - build_start, 1), "seconds") 300 | print(paste(model_type, "completed for", drug, "in", comp_time)) 301 | print(paste("model stored at:", model_path)) 302 | print(paste("model error is", round(mod_rmse, 3))) 303 | 304 | 305 | return(out) 306 | 307 | } 308 | -------------------------------------------------------------------------------- /DRUMLR_code/R/DRUMLPredict.R: -------------------------------------------------------------------------------- 1 | #roxygen description 2 | #' @name PredictDRUML 3 | #' @export PredictDRUML 4 | #' @param df_distance dataframe of caluclated D values of input data made using DRUMLR::DrugMarkerEnrichment() 5 | #' @param drugs Drugs for which predictions will be made.If "all" is stated all drugs will be used 6 | #' @param models list of models for which you would like to use for predictions if "all" is stated all models will be used. Model choices are "bayes", "cubist", "nnet", "pcr", "pls", "rf", "svm" and "dl". 7 | #' @param path_file path file containing model information. If none is specified the default path file save location will be used 8 | #' @param shut_h2o if TRUE the Instance of H20 will be shut down after DL model predictions 9 | #' @title Make a heatmap of marker enrichment 10 | #' @usage PredictDRUML(df_distance = validation_data, drugs = c("BYL.719", "ABT.119"), models= c("rf", "dl"), models_dir = "~/Downloads/Models", path_file = df_path) 11 | 12 | PredictDRUML <- function(df_distance, 13 | drugs="all", 14 | models= "all", 15 | models_dir, 16 | path_file = NULL, 17 | shut_h2o = F){ 18 | 19 | if("tidyverse" %in% (.packages())==FALSE){library(dplyr)} 20 | if("caret" %in% (.packages())==FALSE){library(caret)} 21 | if("h2o" %in% (.packages())==FALSE){library(h2o)} 22 | if("doParallel" %in% (.packages())==FALSE){library(doParallel)} 23 | if("foreach" %in% (.packages())==FALSE){library(foreach)} 24 | 25 | if(is.null(path_file)){ 26 | path_file <- list.files(models_dir) %>% grep(pattern = "model_paths.csv", fixed = T, value = T) 27 | path_file <- read.csv(paste(models_dir, path_file, sep = "/"),row.names = 1, stringsAsFactors = F) 28 | 29 | } 30 | 31 | 32 | if(models == "all"){ 33 | models <- c("bayes", "cubist", "nnet", "pcr", "pls", "rf", "svm", "dl") 34 | } 35 | if(drugs == "all"){ 36 | drugs <- as.factor(path_file$drug) %>% levels() %>% paste() 37 | } 38 | 39 | #get model path for prediction 40 | models_path <- path_file[path_file$model_type %in% models & 41 | path_file$drug %in% drugs,] 42 | 43 | models_dl <- models_path[models_path$model_type == "dl", ] 44 | models_all <- models_path[models_path$model_type != "dl", ] 45 | 46 | #generate distance values which will be used for prediction 47 | .trim_df <- function(markers){ 48 | markers <- base::strsplit(x = markers, split = ";", fixed = T) 49 | markers <- unlist(markers) 50 | return(markers) 51 | } 52 | 53 | dist_list <- lapply(paste(models_path$res_markers, models_path$sens_markers, sep = ";"), FUN = .trim_df) 54 | names(dist_list) <- models_path$model_path 55 | colnames(models_all) 56 | 57 | #reformat distance and get input names for prediction table labels 58 | df_distance <- df_distance %>% t() %>% data.frame() 59 | 60 | #predict by drawing models into environment sequentially 61 | predictions_all <- foreach(i = models_all$model_path, .combine = "cbind")%do%{ 62 | model <- readRDS(paste(models_dir, i, sep = "/")) 63 | mod_input <- dist_list[[i]] 64 | df_mod <- DRUMLR::MinMaxNormalise(df_distance[,mod_input],.margin=2) 65 | predicted_vals <- predict(model, df_distance)%>% data.frame() 66 | rownames(predicted_vals) <- rownames(df_distance) 67 | colnames(predicted_vals) <- paste(models_all[models_all$model_path == i, c("drug", "input_type", "model_type")], collapse = "_") 68 | return(predicted_vals) 69 | } 70 | 71 | #predict for dl models drawing models into environment sequentially 72 | #initiate h2o and make df_distance a h2o frame 73 | if("dl" %in% models){ 74 | h2o.init() 75 | predictions_dl <- foreach(i = models_dl$model_path, .combine = "cbind")%do%{ 76 | #get model info and filter and scale df 77 | mod_imp <- dist_list[[i]] 78 | df_mod <- DRUMLR::MinMaxNormalise(df_distance[,dist_list[[mod_imp]]],.margin=2) %>% as.h2o() 79 | 80 | model <- h2o::h2o.import_mojo(paste(models_dir, i, sep = "")) 81 | predicted_vals <- h2o::h2o.predict(model, df_mod) %>% as.data.frame() 82 | model_variabiles <- paste(models_dl[models_dl$model_path == i, c("drug", "input_type", "model_type")], collapse = "_") 83 | colnames(predicted_vals) <- model_variabiles 84 | return(predicted_vals) 85 | } %>% data.frame(stringsAsFactors = F) 86 | 87 | #shutdown h2o instance 88 | if(shut_h2o==T){ 89 | h2o::h2o.shutdown() 90 | "Y"} 91 | #combine predictions 92 | if(length(models)>1){ 93 | predictions <- cbind(predictions_all, predictions_dl) 94 | 95 | }else{ 96 | predictions <- predictions_dl 97 | } 98 | }else{ 99 | predictions <- predictions_all 100 | } 101 | 102 | } 103 | 104 | -------------------------------------------------------------------------------- /DRUMLR_code/R/DRUMLPredict_example.R: -------------------------------------------------------------------------------- 1 | #roxygen description 2 | #' @name PredictDRUML_example 3 | #' @export PredictDRUML_example 4 | #' @param df_input input dataframe 5 | #' @param .scale if TRUE data will be scaled 6 | #' @param input_type Input data type/model label 7 | #' @param drugs Drug for which predictions will be made 8 | #' @param models list of models for which you would like to use for predictions 9 | #' @param model_path_file path to model path file which is created in model dir by build models 10 | #' @param .marker_database markers database which will be used 11 | #' @param shut_h2o if TRUE the h2o will be shutdown after predictions are made 12 | #' @title Make a heatmap of marker enrichment 13 | #' @usage PredictDRUML(markers = GetMarkers("barasetib")$sensitive$.,inputdata = LoadData("PDBphos"),tissuefilter = "haematopoietic_and_lymphoid_tissue",metric = "aac", drug = "ABT-199") 14 | 15 | PredictDRUML_example <- function(df_distance, 16 | input_type, 17 | drugs="all", 18 | cancer_type, 19 | models= "all", 20 | models_dir, 21 | shut_h2o =F, 22 | computational_load = NULL){w 23 | 24 | if("tidyverse" %in% (.packages())==FALSE){library(dplyr)} 25 | if("caret" %in% (.packages())==FALSE){library(caret)} 26 | if("h2o" %in% (.packages())==FALSE){library(h2o)} 27 | if("doParallel" %in% (.packages())==FALSE){library(doParallel)} 28 | if("foreach" %in% (.packages())==FALSE){library(foreach)} 29 | 30 | if(!is.null(computational_load)) { 31 | cores <- 32 | as.integer((computational_load * parallel::detectCores()), length = 1) 33 | 34 | }else{ 35 | cores <- 1 36 | } 37 | registerDoParallel(cores = cores) 38 | print(paste("running on", cores, "cores")) 39 | 40 | path_file <- DRUMLR:::DRUMLpaths 41 | 42 | if("all" %in% models){ 43 | models <- c("bayes", "cubist", "nnet", "pcr", "pls", "rf", "svm", "dl") 44 | } 45 | if( "all" %in% drugs){ 46 | drugs <- as.factor(path_file$drug) %>% levels() %>% paste() 47 | } 48 | 49 | #get model path for prediction 50 | models_path <- path_file[path_file$data_input == input_type& 51 | path_file$model %in% models & 52 | path_file$type %in% cancer_type & 53 | path_file$drug %in% drugs,] 54 | 55 | models_path <- models_path[models_path$local_path != "not found", ] 56 | models_path$input_id <- paste(models_path$type,models_path$drug, sep = "_") 57 | models_dl <- models_path[models_path$model == "dl", ] 58 | models_all <- models_path[models_path$model != "dl", ] 59 | 60 | #generate distance values which will be used for prediction 61 | 62 | if(cancer_type == "solid"){ 63 | rownames(df_distance) <- paste(rownames(df_distance),"st", sep = ".") 64 | }else{ 65 | rownames(df_distance) <- paste(rownames(df_distance),cancer_type, sep = ".")} 66 | 67 | #reformat distance and get input names for prediction table labels 68 | df_distance <- df_distance %>% t() %>% data.frame() 69 | mod_inputs <- DRUMLR:::DRUMLMLinputs 70 | mod_inputs <- mod_inputs[mod_inputs$drug %in% drugs& 71 | mod_inputs$data_input == input_type& 72 | mod_inputs$type %in% cancer_type,] 73 | 74 | .trim_df <- function(markers){ 75 | markers <- base::strsplit(x = markers, split = "-", fixed = T) 76 | markers <- unlist(markers) 77 | return(markers) 78 | } 79 | 80 | dist_list <- lapply(paste(mod_inputs$input_markers), FUN = .trim_df) 81 | names(dist_list) <- mod_inputs$input_id 82 | 83 | #predict by drawing models into environment sequentially 84 | predictions_all <- foreach(i = models_all$local_path, .combine = "cbind")%dopar%{ 85 | #get model info and filter and scale df 86 | mod_imp <- models_all[models_all$local_path == i, "input_id"] 87 | df_mod <- DRUMLR::MinMaxNormalise(df_distance[,dist_list[[mod_imp]]],.margin=1) 88 | 89 | model <- readRDS(paste(models_dir, i, sep = "")) 90 | predicted_vals <- predict(model, df_mod)%>% data.frame() 91 | model_variabiles<- paste(models_all[models_all$local_path == i, c("drug", "type", "model")], collapse = "_") 92 | colnames(predicted_vals) <- model_variabiles 93 | return(predicted_vals) 94 | } %>% data.frame(stringsAsFactors = F) 95 | 96 | doParallel::stopImplicitCluster() 97 | #predict for dl models drawing models into environment sequentially 98 | #initiate h2o and make df_distance a h2o frame 99 | 100 | 101 | if("dl" %in% models){ 102 | h2o.init(nthreads = cores) 103 | predictions_dl <- foreach(i = models_dl$local_path, .combine = "cbind")%do%{ 104 | #get model info and filter and scale df 105 | mod_imp <- models_dl[models_dl$local_path == i, "input_id"] 106 | df_mod <- DRUMLR::MinMaxNormalise(df_distance[,dist_list[[mod_imp]]],.margin=1) %>% as.h2o() 107 | 108 | model <- h2o::h2o.import_mojo(paste(models_dir, i, sep = "")) 109 | predicted_vals <- h2o::h2o.predict(model, df_mod) %>% as.data.frame() 110 | model_variabiles <- paste(models_dl[models_dl$local_path == i, c("drug", "type", "model")], collapse = "_") 111 | colnames(predicted_vals) <- model_variabiles 112 | return(predicted_vals) 113 | } %>% data.frame(stringsAsFactors = F) 114 | 115 | #shutdown h2o instance 116 | if(shut_h2o==T){ 117 | h2o.shutdown() 118 | "Y"} 119 | #combine predictions 120 | if(length(models)>1){ 121 | predictions <- cbind(predictions_all, predictions_dl) 122 | 123 | }else{ 124 | predictions <- predictions_dl 125 | } 126 | }else{ 127 | predictions <- predictions_all 128 | } 129 | 130 | #return predictions 131 | return(predictions) 132 | } 133 | -------------------------------------------------------------------------------- /DRUMLR_code/R/DrugMarkerEnrichment.R: -------------------------------------------------------------------------------- 1 | #Building models with different markers 2 | # roxygen description 3 | #' @title Generate D values from input data and marker database 4 | #' @name DrugMarkerEnrichment 5 | #' @export DrugMarkerEnrichment 6 | #' @usage DrugMarkerEnrichment(df = df.ppindex, marker_database = "aml_prot_markers", scale = T, output = c("Distance", "zscore")) 7 | #' @description Get Distance values or zscores between sensitive and resistant markers 8 | #' @param df input data with cell lines as colnames and varaibles as rownames 9 | #' @param marker_database markers database to be used can be the following: 10 | #' \itemize{ 11 | #' \item phospho aml 12 | #' \item phospho solid_markers 13 | #' \item prot aml 14 | #' \item prot solid 15 | #' \item rna aml 16 | #' \item rna solid 17 | #' } 18 | #' @param marker_database_path path of the directory containing the DRUMLR marker file 19 | #' @param scale Set as TRUE to scale input data 20 | #' @param output The output metric you want to use can be "Distance" or "zscore" 21 | 22 | DrugMarkerEnrichment <- 23 | function(df, 24 | marker_database) { 25 | 26 | if ("dplyr" %in% (.packages()) == FALSE) { 27 | library(dplyr) 28 | } 29 | 30 | if ("sites" %in% colnames(df) == F) { 31 | dat_edges <- paste(rownames(df)) 32 | } else{ 33 | dat_edges <- paste(df$sites) 34 | df <- df[, colnames(df) != "sites"] 35 | } 36 | 37 | #get markers if using DRUML markers 38 | 39 | dbs <- marker_database 40 | 41 | #split marker edges into list 42 | resistant_markers <- strsplit(as.character(dbs$resistant_markers),"-") 43 | names(resistant_markers) <- dbs$drugs 44 | 45 | colnames(dbs) 46 | #split marker edges into list 47 | sensitive_markers <- strsplit(as.character(dbs$sensitive_markers),"-") 48 | names(sensitive_markers) <- dbs$drugs 49 | 50 | #filter edges by rownames retain only nodes with more than 3 edges 51 | 52 | sensitive_markers <- lapply(sensitive_markers, FUN= function(x){x <- x[x %in%dat_edges]}) 53 | sensitive_markers <- sensitive_markers[lengths(sensitive_markers)>=3] 54 | 55 | resistant_markers <- lapply(resistant_markers, FUN= function(x){x <- x[x %in%dat_edges]}) 56 | resistant_markers <- resistant_markers[lengths(resistant_markers)>=3] 57 | 58 | #ensure that values for resistant and sensitve markers are present 59 | com_markers <- dbs$drugs[dbs$drugs %in% names(sensitive_markers)&dbs$drugs %in% names(resistant_markers)] 60 | sensitive_markers <- sensitive_markers[com_markers] 61 | resistant_markers <- resistant_markers[com_markers] 62 | 63 | #function for getting inputs 64 | .GetDistInputs <- function(x){ 65 | df <-df[x,] 66 | median <- apply(df, MARGIN=2, function(xx){median(xx, na.rm = T)}) 67 | Q3 <- apply(df, MARGIN=2, function(xx){quantile(xx,probs = 0.75, na.rm = T)}) 68 | out <- data.frame("D"= Q3 + median) 69 | return(out) 70 | } 71 | 72 | print("calculating resistance values") 73 | resistant_inputs <- lapply(resistant_markers, .GetDistInputs) 74 | 75 | print("calculating sensitivity values") 76 | sensitive_inputs <- lapply(sensitive_markers, .GetDistInputs) 77 | 78 | out <- lapply(com_markers, FUN = function(x){ 79 | df <- sensitive_inputs[[x]]-resistant_inputs[x] 80 | colnames(df) <- x 81 | return(df) 82 | }) 83 | 84 | out <- data.frame(out) %>%t() 85 | 86 | print("Drug Enrichment Complete") 87 | return(out) 88 | 89 | } 90 | 91 | -------------------------------------------------------------------------------- /DRUMLR_code/R/FilterSensitivity.R: -------------------------------------------------------------------------------- 1 | # Emperical Markers of Sensitivity and resistantce 2 | #' @name FilterSensitivity 3 | #' @export FilterSensitivity 4 | #' @title Filter Drugs by sensitivity data 5 | #' @param sensitivity_database sensitvitiy data with cell lines as rownamed and drugs as columns 6 | #' @param iqr_tolerance IQR range cut off to filter drug list 7 | #' @param n_coverage n_coverage cut off required to ensure models are reliable 8 | #' @param cell_names List of cell names to subset sensitivity data 9 | 10 | FilterSensitivity <- function(sensitivity_database = NULL, 11 | iqr_tolerance = 0.04, 12 | n_coverage = 0.8, 13 | cell_names = NULL){ 14 | 15 | if("dplyr" %in% (.packages())==FALSE){library(dplyr)} 16 | 17 | #filter sensitivity by cell lines 18 | if(is.null(cell_names)==FALSE){ 19 | sensitivity_database <- sensitivity_database[rownames(sensitivity_database) %in% cell_names,] 20 | } 21 | 22 | #filter by n_coverage 23 | na_cut_off <- nrow(sensitivity_database)*n_coverage 24 | na_cut_off <- apply(sensitivity_database, MARGIN = 2, FUN = function(x){sum(!is.na(x)) >= na_cut_off}) %>% unlist() 25 | 26 | sensitivity_database <- sensitivity_database[,na_cut_off] 27 | 28 | #filter by iqr 29 | iqr_cut_off <- apply(sensitivity_database, MARGIN = 2, FUN = function(x){ 30 | out <- IQR(x, na.rm = T) >= iqr_tolerance 31 | if(is.na(out)){out <- FALSE} 32 | return(out) 33 | }) %>% unlist() 34 | 35 | sensitivity_database <- sensitivity_database[,iqr_cut_off] 36 | 37 | drugs <- colnames(sensitivity_database[,-1]) 38 | return(drugs) 39 | } 40 | 41 | FilterSensitivity <- compiler::cmpfun(FilterSensitivity) 42 | -------------------------------------------------------------------------------- /DRUMLR_code/R/MinMaxNormalise.R: -------------------------------------------------------------------------------- 1 | #roxygen description 2 | #' @name MinMaxNormalise 3 | #' @export MinMaxNormalise 4 | #' @param x dataframe or matrix to be scaled (all data must be numeric) 5 | #' @param margin can be 1 or 2. x will be scaled by row if .margin =1 or column .margin=2 6 | #' @title Normalise data by min max scaler 7 | #' @usage MinMaxNormalise(df = DRUMLRdata:::DRUML_phos, .margin=2) 8 | 9 | 10 | MinMaxNormalise <- function(x, .margin=2){ 11 | require(dplyr) 12 | 13 | .min_max <- function(x){ 14 | mn <-min(x, na.rm = T) 15 | mx <-max(x, na.rm = T) 16 | out <- (x-mn)/(mx-mn) 17 | 18 | return(out) 19 | } 20 | 21 | out <- base::apply(x, MARGIN = .margin, FUN = .min_max) 22 | 23 | if(.margin==1){ 24 | out <- out %>% t() %>% data.frame(stringsAsFactors = F) 25 | } else if (.margin==2) { 26 | out <- data.frame(out, stringsAsFactors = F) 27 | } 28 | 29 | return(out) 30 | } 31 | -------------------------------------------------------------------------------- /DRUMLR_code/R/OptimiseMLInput.R: -------------------------------------------------------------------------------- 1 | #' Title 2 | #' @title Optimise ML input distance markers 3 | #' @name OptimiseMLInput 4 | #' @param df_train training dataset for machine learning. df_input with response variables in a column named response 5 | #' @param method statistical method used to determine correlation 6 | #' @param max_n_D maximum number of markers to use 7 | #' @param min_n_D Minimum number of markers to use 8 | #' @param return_corr_analysis TRUE results of correlation analysis will be returned in addition to markers lists 9 | #' @param order_var variable used to order which markers to use 10 | #' @export OptimiseMLInput 11 | 12 | OptimiseMLInput <- function(df_train, 13 | method = "spearman", 14 | max_n_D = 30, 15 | min_n_D = 7, 16 | partition.ratio = 0.7, 17 | return_corr_analysis = FALSE, 18 | order_var = "r") { 19 | train.set <- 20 | createDataPartition( 21 | na.omit(df_train$response), 22 | p = partition.ratio, 23 | list = FALSE, 24 | times = 1 25 | ) 26 | 27 | 28 | #get training input and response datasets 29 | 30 | df_trr <- df_train[train.set, "response"] 31 | df_tri <- df_train[train.set, !colnames(df_train) %in% "response"] 32 | 33 | #correlation analysis of distances against response values 34 | 35 | df_corr <- lapply( 36 | colnames(df_tri), 37 | FUN = function(x) { 38 | #get x and y values for analysis 39 | marker <- x 40 | x <- df_tri[, x] 41 | y <- df_trr 42 | #analyse correlation 43 | d_corr <- cor.test(x = x, y = y, method = method) 44 | #draw r and p values from analysis 45 | p <- d_corr$p.value 46 | r <- d_corr$estimate 47 | #calculate slope 48 | if (!is.na(r)) { 49 | slope <- coef(lm(formula = y ~ x))[2] 50 | } else{ 51 | slope <- 0 52 | } 53 | 54 | #generate output data 55 | output <- data.frame( 56 | "marker" = marker, 57 | "p" = p, 58 | "r" = r, 59 | "slope" = slope 60 | ) 61 | return(output) 62 | } 63 | ) %>% dplyr::bind_rows() %>% as.data.frame() 64 | 65 | 66 | r_positive <- df_corr[order(-df_corr[, order_var]), ] 67 | r_negative <- df_corr[order(df_corr[, order_var]), ] 68 | 69 | top_r_positive <- r_positive[r_negative$p <= 0.05 & r_negative[, order_var]>0, ] 70 | top_r_negative <- r_negative[r_negative$p <= 0.05 & r_negative[, order_var]<0, ] 71 | 72 | #function to determine how many markers to use 73 | .n_check <- function(x){ 74 | x <- nrow(x) 75 | if(x>max_n_D){ 76 | out <- max_n_D 77 | }else if(x% paste() 90 | top_r_negative <- r_negative[1:top_neg_n, "marker"] %>% paste() 91 | 92 | output <- list(top_r_positive, top_r_negative) 93 | names(output) <- 94 | c("sensitive drug markers", "resistant drug markers") 95 | 96 | if (return_corr_analysis) { 97 | output <- c(output, df_corr) 98 | } 99 | return(output) 100 | } 101 | -------------------------------------------------------------------------------- /DRUMLR_code/R/RemoveRepeatNo.R: -------------------------------------------------------------------------------- 1 | # roxygen description 2 | #' @title Update Internal Databases for KSEA 3 | #' @name RemoveRepeatNo 4 | #' @usage RemoveRepeatNo(colnames(df_input)) 5 | #' @export RemoveRepeatNo 6 | #' @description Removes the repeat numbers for triplicate data so that datasets can be filtered by cell name 7 | #' @param repeatseperator The string which separates cell name from repeat number. For DRUML data "__" is used as default. 8 | 9 | 10 | RemoveRepeatNo <- function(x, repeatseperator = "__") { 11 | if ("dplyr" %in% (.packages()) == FALSE) { 12 | library(dplyr) 13 | } 14 | out <- 15 | lapply( 16 | x, 17 | FUN = function(x) { 18 | strsplit(x, repeatseperator)[[1]][[1]] 19 | } 20 | ) %>% unlist() 21 | return(out) 22 | } 23 | -------------------------------------------------------------------------------- /DRUMLR_code/R/sysdata.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CutillasLab/DRUMLR/8a8b7dda23251ed47ae23fedd207eaf2f6281978/DRUMLR_code/R/sysdata.rda -------------------------------------------------------------------------------- /DRUMLR_code/man/BuildDRUMLs.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/BuildDRUMLs.R 3 | \name{BuildDRUMLs} 4 | \alias{BuildDRUMLs} 5 | \title{Build DRUML models for a list of drugs} 6 | \usage{ 7 | BuildDRUMLs( 8 | df_input, 9 | .marker_database, 10 | df_response, 11 | drugs = NULL, 12 | input_type = "DRUML_model", 13 | save_path = "DRUML_models", 14 | save_csv = T, 15 | scale = T, 16 | computational_load = 0.8, 17 | partition.ratio = 0.7, 18 | models = c("glm", "cubist", "pls", "pcr", "nnet", "svm", "rf", "dl"), 19 | corr_method = "spearman", 20 | max_n_D = 30, 21 | min_n_D = 7, 22 | order_var = "r" 23 | ) 24 | } 25 | \arguments{ 26 | \item{df_input}{dataframe of input data.} 27 | 28 | \item{.marker_database}{Markers database which will be used to for Drug Marker Enrichment} 29 | 30 | \item{df_response}{Dataframe of drug responses(AAC) with cell lines as row names and column names as drugs.} 31 | 32 | \item{drugs}{list of drugs from which models will be generated. If left NULL models will be built for all drugs in the marker database.} 33 | 34 | \item{save_path}{save location for models and results csvs} 35 | 36 | \item{save_csv}{if TRUE csv files containing DRUML results will be made} 37 | 38 | \item{computational_load}{Set as a decimal fraction of the number of cores you which to be recruited for model building e.g a value of 0.8 means 80\% of all available CPU cores will be used to build models. If left as NULL only 1 core will be used.} 39 | 40 | \item{models}{list of models to use for machine learning: 41 | \itemize{ 42 | \item glm = Baysian GLM model 43 | \item cubist = Cubist model 44 | \item pls = Partial list squares model 45 | \item pcr = Principal components regression 46 | \item nnet = Neural network model 47 | \item scm = Super vector machines 48 | \item rf = Random forests regressor model 49 | \item dl = Deep learning model using h2o package (needs Java to be up to date) 50 | }} 51 | 52 | \item{max_n_D}{maximum number of markers to use} 53 | 54 | \item{min_n_D}{Minimum number of markers to use} 55 | 56 | \item{order_var}{variable used to filter Distance models for DRUML: 57 | \itemize{ 58 | \item p = p value 59 | \item r = r value 60 | \item slope = slope of fitted linear model 61 | }} 62 | 63 | \item{method}{Statistical method for filtering distance markers for DRUML models default is spearman} 64 | } 65 | \description{ 66 | Title 67 | } 68 | -------------------------------------------------------------------------------- /DRUMLR_code/man/BuildEMDR.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/BuildEMDR.R 3 | \name{BuildEMDR} 4 | \alias{BuildEMDR} 5 | \title{Empirical Markers of Drug Response} 6 | \usage{ 7 | BuildEMDR( 8 | df_input = NULL, 9 | df_response = NULL, 10 | drugs, 11 | p.cutoff = 0.05, 12 | fold.cut.off = 1, 13 | resampling.times = 5, 14 | return_limma = F, 15 | computational_load = NULL, 16 | nfolds = 10, 17 | maxmarker_res = NULL, 18 | maxmarker_sens = NULL 19 | ) 20 | } 21 | \arguments{ 22 | \item{df_input}{input data with cell lines as colnames and varaibles as rownames} 23 | 24 | \item{df_response}{response values dataframe with colnames as cell lines and drugs as column} 25 | 26 | \item{p.cutoff}{P value threshold to define a repeat comparison as significant} 27 | 28 | \item{fold.cut.off}{fold value threshold to define a repeat comparison direction} 29 | 30 | \item{resampling.times}{number of resampling times} 31 | 32 | \item{return_limma}{Set as TRUE if for limma analysis results to be returned} 33 | 34 | \item{computational_load}{Set as a decimal fraction of cores you want to use. If left as NULL only 1 core will be used.} 35 | 36 | \item{drug}{drug generate markers for} 37 | 38 | \item{scale_input}{Set as TRUE to scale input data} 39 | } 40 | \description{ 41 | Empirical Markers of Drug Response 42 | } 43 | -------------------------------------------------------------------------------- /DRUMLR_code/man/DrugMarkerEnrichment.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/DrugMarkerEnrichment.R 3 | \name{DrugMarkerEnrichment} 4 | \alias{DrugMarkerEnrichment} 5 | \title{Generate D values from input data and marker database} 6 | \usage{ 7 | DrugMarkerEnrichment(df = df.ppindex, marker_database = "aml_prot_markers", scale = T, output = c("Distance", "zscore")) 8 | } 9 | \arguments{ 10 | \item{df}{input data with cell lines as colnames and varaibles as rownames} 11 | 12 | \item{marker_database}{markers database to be used can be the following: 13 | \itemize{ 14 | \item phospho aml 15 | \item phospho solid_markers 16 | \item prot aml 17 | \item prot solid 18 | \item rna aml 19 | \item rna solid 20 | } 21 | @param marker_database_path path of the directory containing the DRUMLR marker file} 22 | 23 | \item{scale}{Set as TRUE to scale input data} 24 | 25 | \item{output}{The output metric you want to use can be "Distance" or "zscore"} 26 | } 27 | \description{ 28 | Get Distance values or zscores between sensitive and resistant markers 29 | } 30 | -------------------------------------------------------------------------------- /DRUMLR_code/man/FilterSensitivity.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/FilterSensitivity.R 3 | \name{FilterSensitivity} 4 | \alias{FilterSensitivity} 5 | \title{Filter Drugs by sensitivity data} 6 | \usage{ 7 | FilterSensitivity( 8 | sensitivity_database = NULL, 9 | iqr_tolerance = 0.04, 10 | n_coverage = 0.8, 11 | cell_names = NULL 12 | ) 13 | } 14 | \arguments{ 15 | \item{sensitivity_database}{sensitvitiy data with cell lines as rownamed and drugs as columns} 16 | 17 | \item{iqr_tolerance}{IQR range cut off to filter drug list} 18 | 19 | \item{n_coverage}{n_coverage cut off required to ensure models are reliable} 20 | 21 | \item{cell_names}{List of cell names to subset sensitivity data} 22 | } 23 | \description{ 24 | Filter Drugs by sensitivity data 25 | } 26 | -------------------------------------------------------------------------------- /DRUMLR_code/man/MinMaxNormalise.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/MinMaxNormalise.R 3 | \name{MinMaxNormalise} 4 | \alias{MinMaxNormalise} 5 | \title{Normalise data by min max scaler} 6 | \usage{ 7 | MinMaxNormalise(df = DRUMLRdata:::DRUML_phos, .margin=2) 8 | } 9 | \arguments{ 10 | \item{x}{dataframe or matrix to be scaled (all data must be numeric)} 11 | 12 | \item{margin}{can be 1 or 2. x will be scaled by row if .margin =1 or column .margin=2} 13 | } 14 | \description{ 15 | Normalise data by min max scaler 16 | } 17 | -------------------------------------------------------------------------------- /DRUMLR_code/man/OptimiseMLInput.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/OptimiseMLInput.R 3 | \name{OptimiseMLInput} 4 | \alias{OptimiseMLInput} 5 | \title{Optimise ML input distance markers} 6 | \usage{ 7 | OptimiseMLInput( 8 | df_train, 9 | method = "spearman", 10 | max_n_D = 30, 11 | min_n_D = 7, 12 | partition.ratio = 0.7, 13 | return_corr_analysis = FALSE, 14 | order_var = "r" 15 | ) 16 | } 17 | \arguments{ 18 | \item{df_train}{training dataset for machine learning. df_input with response variables in a column named response} 19 | 20 | \item{method}{statistical method used to determine correlation} 21 | 22 | \item{max_n_D}{maximum number of markers to use} 23 | 24 | \item{min_n_D}{Minimum number of markers to use} 25 | 26 | \item{return_corr_analysis}{TRUE results of correlation analysis will be returned in addition to markers lists} 27 | 28 | \item{order_var}{variable used to order which markers to use} 29 | } 30 | \description{ 31 | Title 32 | } 33 | -------------------------------------------------------------------------------- /DRUMLR_code/man/PredictDRUML.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/DRUMLPredict.R 3 | \name{PredictDRUML} 4 | \alias{PredictDRUML} 5 | \title{Make a heatmap of marker enrichment} 6 | \usage{ 7 | PredictDRUML(df_distance = validation_data, drugs = c("BYL.719", "ABT.119"), models= c("rf", "dl"), models_dir = "~/Downloads/Models", path_file = df_path) 8 | } 9 | \arguments{ 10 | \item{df_distance}{dataframe of caluclated D values of input data made using DRUMLR::DrugMarkerEnrichment()} 11 | 12 | \item{drugs}{Drugs for which predictions will be made.If "all" is stated all drugs will be used} 13 | 14 | \item{models}{list of models for which you would like to use for predictions if "all" is stated all models will be used. Model choices are "bayes", "cubist", "nnet", "pcr", "pls", "rf", "svm" and "dl".} 15 | 16 | \item{path_file}{path file containing model information. If none is specified the default path file save location will be used} 17 | 18 | \item{shut_h2o}{if TRUE the Instance of H20 will be shut down after DL model predictions} 19 | } 20 | \description{ 21 | Make a heatmap of marker enrichment 22 | } 23 | -------------------------------------------------------------------------------- /DRUMLR_code/man/PredictDRUML_example.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/DRUMLPredict_example.R 3 | \name{PredictDRUML_example} 4 | \alias{PredictDRUML_example} 5 | \title{Make a heatmap of marker enrichment} 6 | \usage{ 7 | PredictDRUML(markers = GetMarkers("barasetib")$sensitive$.,inputdata = LoadData("PDBphos"),tissuefilter = "haematopoietic_and_lymphoid_tissue",metric = "aac", drug = "ABT-199") 8 | } 9 | \arguments{ 10 | \item{input_type}{Input data type/model label} 11 | 12 | \item{drugs}{Drug for which predictions will be made} 13 | 14 | \item{models}{list of models for which you would like to use for predictions} 15 | 16 | \item{shut_h2o}{if TRUE the h2o will be shutdown after predictions are made} 17 | 18 | \item{df_input}{input dataframe} 19 | 20 | \item{.scale}{if TRUE data will be scaled} 21 | 22 | \item{model_path_file}{path to model path file which is created in model dir by build models} 23 | 24 | \item{.marker_database}{markers database which will be used} 25 | } 26 | \description{ 27 | Make a heatmap of marker enrichment 28 | } 29 | -------------------------------------------------------------------------------- /DRUMLR_code/man/RemoveRepeatNo.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/RemoveRepeatNo.R 3 | \name{RemoveRepeatNo} 4 | \alias{RemoveRepeatNo} 5 | \title{Update Internal Databases for KSEA} 6 | \usage{ 7 | RemoveRepeatNo(colnames(df_input)) 8 | } 9 | \arguments{ 10 | \item{repeatseperator}{The string which separates cell name from repeat number. For DRUML data "__" is used as default.} 11 | } 12 | \description{ 13 | Removes the repeat numbers for triplicate data so that datasets can be filtered by cell name 14 | } 15 | -------------------------------------------------------------------------------- /DRUMLR_code/man/dot-BuildModel.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/BuildModels.R 3 | \name{.BuildModel} 4 | \alias{.BuildModel} 5 | \title{Internal Build model function} 6 | \usage{ 7 | .BuildModel( 8 | df_train, 9 | df_test, 10 | model_type, 11 | drug, 12 | input_type, 13 | save_path, 14 | metric = "RMSE", 15 | initiate_h2o = T 16 | ) 17 | } 18 | \arguments{ 19 | \item{model_type}{string of model type to build e.g cubist, glm, nnet, pcr, pls, rf and svm} 20 | 21 | \item{drug}{drug to use for naming model} 22 | 23 | \item{input_type}{input type to use for naming model} 24 | 25 | \item{save_path}{save file location} 26 | 27 | \item{metric}{error metric to use to build model} 28 | 29 | \item{df_input}{dataframe to use for model training. Response value must be in column named response.} 30 | 31 | \item{partition.ratio}{partition ratio for making testing dataset} 32 | } 33 | \description{ 34 | Title Internal function for building machine learning models 35 | } 36 | \examples{ 37 | .BuildModel(df_input = df_distances, model_type = "rf", drug = "ABT-199", save_path = "my_model_dir") 38 | } 39 | -------------------------------------------------------------------------------- /Example_models/models_phospho/phospho_aml_BX.912_bayes.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CutillasLab/DRUMLR/8a8b7dda23251ed47ae23fedd207eaf2f6281978/Example_models/models_phospho/phospho_aml_BX.912_bayes.rds -------------------------------------------------------------------------------- /Example_models/models_phospho/phospho_aml_BX.912_cubist.rds: -------------------------------------------------------------------------------- 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Rajeeve 4, Jude Fitzgibbon 5, Jon Travers 6, David Britton 1,2, Shirin Khorsandi 7 & Pedro R. Cutillas 1,4,8* 5 | 6 | 1 Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom 2 Current address: Kinomica Ltd, Alderley Park, Alderley Edge, Macclesfield SK10 4TG, United Kingdom 3 Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan 4 Mass spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom 5 Personalised Medicine Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom 6 Astra Zeneca Ltd, 1 Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, CB2 0AA, United Kingdom 7 Kings College London, Denmark Hill, Brixton, London SE5 9RS, United Kingdom 8 The Alan Turing Institute, The British Library, 2QR, 96 Euston Rd, London NW1 2DB, United Kingdom 7 | 8 | ## Copyright 9 | The copyright holder for these data is the author. This resource is made available under a Creative Commons Attribution-NonCommercial-NoDerivatives CC-BY-NC-ND 4.0 International license. 10 | 11 | ## How to install 12 | 13 | To install the package install the devtools package and run: 14 | devtools::install_github(repo="CutillasLab/DRUMLR", subdir="DRUMLR_code") 15 | 16 | ## Code dependencies 17 | dplyr (1.0.2)\ 18 | foreach (1.5.1)\ 19 | doParallel (1.0.16)\ 20 | limma (3.42.2)\ 21 | caret (6.0-86)\ 22 | h2o (3.32.0.1)*\ 23 | Cubist (0.2.3)\ 24 | pls(2.7-3)\ 25 | glmnet (4.0-2)\ 26 | kernlab (0.9-29)\ 27 | randomForest (4.6-14)\ 28 | \* this package requires up to date java and H2O packages to operate, however, DRUMLR is written with backwards compatibility for H2O. 29 | --------------------------------------------------------------------------------