├── .gitignore ├── LICENSE ├── README.md ├── deep_learning ├── README.md ├── deep_learning.ipynb └── figures │ ├── aga.png │ ├── aga_cell_cycle_stages.png │ ├── aga_path.png │ └── aga_path_kink.png ├── minimal_examples ├── README.md ├── comparisons │ ├── README.md │ ├── data │ │ ├── krumsiek11 │ │ │ ├── .DS_Store │ │ │ ├── X.csv │ │ │ ├── add │ │ │ │ ├── aga_adjacency_full_confidence.csv │ │ │ │ ├── aga_adjacency_tree_confidence_csr_data.csv │ │ │ │ ├── aga_adjacency_tree_confidence_csr_indices.csv │ │ │ │ ├── aga_adjacency_tree_confidence_csr_indptr.csv │ │ │ │ ├── aga_adjacency_tree_confidence_csr_shape.csv │ │ │ │ ├── aga_groups_colors_original.csv │ │ │ │ ├── aga_groups_order.csv │ │ │ │ ├── aga_groups_order_original.csv │ │ │ │ ├── aga_groups_sizes.csv │ │ │ │ ├── data_graph_distance_local_csr_data.csv │ │ │ │ ├── data_graph_distance_local_csr_indices.csv │ │ │ │ ├── data_graph_distance_local_csr_indptr.csv │ │ │ │ ├── data_graph_norm_weights_csr_data.csv │ │ │ │ ├── data_graph_norm_weights_csr_indices.csv │ │ │ │ ├── data_graph_norm_weights_csr_indptr.csv │ │ │ │ ├── data_graph_norm_weights_csr_shape.csv │ │ │ │ ├── diffmap_evals.csv │ │ │ │ ├── louvain_groups_colors.csv │ │ │ │ ├── louvain_groups_order.csv │ │ │ │ ├── louvain_params.csv │ │ │ │ ├── smp_keys_multicol.csv │ │ │ │ └── var_keys_multicol.csv │ │ │ ├── smp.csv │ │ │ └── var.csv │ │ └── krumsiek11_blobs │ │ │ ├── X.csv │ │ │ ├── add │ │ │ ├── aga_adjacency_full_confidence.csv │ │ │ ├── aga_adjacency_tree_confidence_csr_data.csv │ │ │ ├── aga_adjacency_tree_confidence_csr_indices.csv │ │ │ ├── aga_adjacency_tree_confidence_csr_indptr.csv │ │ │ ├── aga_adjacency_tree_confidence_csr_shape.csv │ │ │ ├── aga_groups_colors_original.csv │ │ │ ├── aga_groups_order.csv │ │ │ ├── aga_groups_order_original.csv │ │ │ ├── aga_groups_sizes.csv │ │ │ ├── data_graph_distance_local_csr_data.csv │ │ │ ├── data_graph_distance_local_csr_indices.csv │ │ │ ├── data_graph_distance_local_csr_indptr.csv │ │ │ ├── data_graph_distance_local_csr_shape.csv │ │ │ ├── data_graph_norm_weights_csr_data.csv │ │ │ ├── data_graph_norm_weights_csr_indices.csv │ │ │ ├── data_graph_norm_weights_csr_indptr.csv │ │ │ ├── data_graph_norm_weights_csr_shape.csv │ │ │ ├── diffmap_evals.csv │ │ │ ├── draw_graph_layout.csv │ │ │ ├── louvain_groups_colors.csv │ │ │ ├── louvain_groups_order.csv │ │ │ ├── louvain_params.csv │ │ │ ├── smp_keys_multicol.csv │ │ │ └── var_keys_multicol.csv │ │ │ ├── smp.csv │ │ │ └── var.csv │ ├── dpt.ipynb │ ├── eclair │ │ ├── ECLAIR_instance │ │ │ └── ECLAIR_ensemble_clustering_files │ │ │ │ └── 2017-09-15__09:09:31 │ │ │ │ ├── cluster_runs.txt │ │ │ │ ├── consensus_adjacency_matrix.txt │ │ │ │ ├── consensus_distances_matrix.txt │ │ │ │ ├── consensus_labels.txt │ │ │ │ ├── consensus_topological_distances_matrix.txt │ │ │ │ ├── ensemble_distances_distributions.txt │ │ │ │ ├── ensemble_distances_means.txt │ │ │ │ ├── ensemble_distances_medians.txt │ │ │ │ ├── ensemble_distances_variances.txt │ │ │ │ └── mst_adjacency_list.txt │ │ ├── README.md │ │ ├── X_krumsiek11.txt │ │ ├── X_krumsiek11_blobs.txt │ │ ├── X_krumsiek11_scaled.txt │ │ ├── eclair_plots.ipynb │ │ ├── figures │ │ │ ├── aga_graph_eclair_tree.png │ │ │ └── draw_graph_fr_eclair_clusters.png │ │ └── logfile_run_X_krumsiek11_scaled.txt │ ├── figures │ │ ├── diffmap_components12.png │ │ ├── diffmap_components123.png │ │ ├── diffmap_components23.png │ │ ├── draw_graph_fr.png │ │ ├── monocle2_with_blobs.png │ │ ├── monocle2_without_blobs.png │ │ └── tsne.png │ ├── monocle2.ipynb │ └── stemID │ │ ├── README.md │ │ ├── RaceID3_StemID2_class.R │ │ ├── RaceID3_StemID2_sample.R │ │ ├── Rplots_X_krumsiek11_blobs_shifted.pdf │ │ ├── Rplots_X_krumsiek11_blobs_shifted_1.png │ │ ├── Rplots_X_krumsiek11_blobs_shifted_2.png │ │ ├── X_krumsiek11_blobs_shifted.csv │ │ ├── X_krumsiek11_shifted.csv │ │ └── logfile_X_krumsiek11_blobs_shifted.txt ├── comparisons_exports.ipynb ├── figures │ ├── aga.png │ ├── aga_cluster_std1.png │ ├── aga_cluster_std10.png │ ├── aga_cluster_std6.png │ ├── aga_coarse.svg │ ├── aga_coarse_R.svg │ ├── aga_new.png │ ├── aga_path.png │ ├── aga_reference.png │ ├── aga_simple_tree.png │ ├── draw_graph_fr.png │ ├── draw_graph_fr_associated.png │ ├── krumsiek11_timeseries_heatmap.png │ ├── matrix_norm_new.png │ ├── matrix_norm_new_cropped.png │ ├── matrix_norm_reference.png │ ├── matrix_norm_reference_cropped.png │ ├── robustness_summary.png │ └── tsne.png ├── minimal_examples.ipynb └── robustness.ipynb ├── nestorowa16 ├── README.md ├── comparison_dpt.ipynb ├── comparison_monocle2.ipynb ├── figures │ ├── aga_gephi.svg │ ├── aga_gephi_inkscape.svg │ ├── aga_path.svg │ ├── monocle2_maxcomps10.png │ └── monocle2_maxcomps4.png └── nestorowa16.ipynb ├── paul15 ├── README.md ├── comparison_monocle2 │ ├── data │ │ └── MAP.csv │ ├── figures │ │ ├── alternative.png │ │ └── original.png │ ├── monocle2_alternative.ipynb │ └── monocle2_original.ipynb ├── figures │ ├── aga_fr.png │ ├── aga_gephi.svg │ ├── aga_graph_iterative_matching.png │ ├── aga_iterative_matching.png │ ├── aga_path.svg │ ├── aga_path_comparison.png │ ├── aga_without_lymphoids.png │ ├── aga_without_lymphoids_cell_types.png │ ├── legend.pdf │ └── robustness_summary.png ├── paul15.ipynb └── paul15_robustness.ipynb ├── pbmcs ├── figures │ ├── aga_pbmc3k.png │ └── aga_pbmc68k.png ├── pbmc3k.ipynb └── pbmc68k.ipynb └── planaria ├── figures └── aga.svg └── planaria.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # additional stuff 2 | .RData* 3 | .Rhistory* 4 | *.pdf 5 | 6 | # Scanpy 7 | .scanpy 8 | docs/_build 9 | docs/api/scanpy.api.* 10 | archive/ 11 | data/ 12 | figs/ 13 | */figs*/ 14 | write/ 15 | ./examples/ 16 | scanpy.egg-info/ 17 | scanpy_user/ 18 | scanpy_save/ 19 | scanpy_later/ 20 | scripts_test/ 21 | thirdp/ 22 | test.py 23 | README.html 24 | scripts/ 25 | test_notebooks.txt 26 | upload_to_pypi.sh 27 | 28 | # private files 29 | scanpy/examples/builtin_private.py 30 | scanpy/tools/paths.py 31 | scanpy/tools/tgdyn.py 32 | scanpy/tools/tgdyn_simple.py 33 | scripts/paths.py 34 | scripts/test_private.sh 35 | scripts/tgdyn.py 36 | scripts/tgdyn_simple.py 37 | 38 | 39 | # always-ignore extensions 40 | *~ 41 | 42 | # Python / Byte-compiled / optimized / DLL 43 | __pycache__/ 44 | *.py[cod] 45 | *.so 46 | .cache 47 | 48 | # OS or Editor files and folders 49 | .DS_Store 50 | Thumbs.db 51 | .ipynb_checkpoints/ 52 | .directory 53 | /.idea/ 54 | 55 | # always-ignore directories 56 | /dist/ 57 | /build/ 58 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Alex Wolf 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This repository is phased out and does not reflect the state of PAGA since Scanpy 1.2. 2 | 3 | Please visit the new repository: https://github.com/theislab/paga. -------------------------------------------------------------------------------- /deep_learning/README.md: -------------------------------------------------------------------------------- 1 | *First compiled: September 16, 2017* 2 | 3 | # Graph Abstraction for Deep Learning 4 | 5 | See [*deep_learning*](deep_learning.ipynb). -------------------------------------------------------------------------------- /deep_learning/figures/aga.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/deep_learning/figures/aga.png -------------------------------------------------------------------------------- /deep_learning/figures/aga_cell_cycle_stages.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/deep_learning/figures/aga_cell_cycle_stages.png -------------------------------------------------------------------------------- /deep_learning/figures/aga_path.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/deep_learning/figures/aga_path.png -------------------------------------------------------------------------------- /deep_learning/figures/aga_path_kink.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/deep_learning/figures/aga_path_kink.png -------------------------------------------------------------------------------- /minimal_examples/README.md: -------------------------------------------------------------------------------- 1 | *First compiled: September 13, 2017.* 2 | 3 | ## Minimal examples 4 | 5 | ### Contents 6 | 7 | * [*minimal_examples*](minimal_examples.ipynb) contains the main results 8 | * [*robustness*](robustness.ipynb) studies the robustness of these results 9 | * [*comparisons*](comparisons) discusses results obtained with other algorithms 10 | ([*comparisons_exports*](comparisons_exports.ipynb) exports data) 11 | 12 | ### Inferring the abstracted graph 13 | 14 | Here, we study a simple, almost noise-free simulated dataset, which serves as a 15 | minimal example with an unambiguous ground truth 16 | ([*krumsiek11_blobs/X.csv*](comparisons/data/krumsiek11_blobs/X.csv)). The data 17 | contains two clusters and a continous tree-like manifold associated with 18 | [simulated hematopoietic 19 | differentiation](https://github.com/theislab/scanpy_usage/tree/master/170430_krumsiek11). Below, 20 | we reproduce the abstracted graph and the inferred tree of Supplemental Figure 6 of the 21 | paper: 22 | 23 | 24 | 25 | ### Comparison with other algorithms 26 | 27 | In [*comparisons*](comparisons), we show that only graph abstraction provides meaningful results for the minimal dataset. 28 | 29 | ### Zooming into particular regions of the data 30 | 31 | Also in [*minimal_examples*](minimal_examples.ipynb), it is shown how to start with a coarse grained view of the data 32 | 33 | 34 | 35 | and zoom into particular regions. 36 | 37 | 38 | 39 | ### Robustness 40 | 41 | If we change parameters in graph abstraction, we obtain seemingly very different 42 | abstractions of the data. Here, for a fine-grained resolution 43 | 44 | 45 | 46 | and here, with a very coarse-grained resolution: 47 | 48 | 49 | 50 | Upon closer inspection, one realizes that all results represent the same 51 | topology and are correct. To measure how much two tree topologies differ, we 52 | suggest to compare all paths between leaf nodes in the inferred abstracted 53 | graphs. For example, 54 | ``` 55 | path = ['21', '8', '18', '7', '9', '2'], 56 | path_mapped = [['7', '2'], ['6', '7', '2'], ['2', '7'], ['2', '9'], ['9', '10', '3'], ['11', '10']], 57 | path_new = ['7', '2', '9', '10', '11'], 58 | -> n_agreeing_steps = 4 / n_steps = 4. 59 | ``` 60 | agree. 61 | 62 | For the simulated dataset, graph abstraction almost always yields the correct 63 | topology; no matter which parameters are chosen. 64 | 65 | 66 | 67 | See [*robustness*](robustness.ipynb) for all details. 68 | 69 | ### Different degrees of clustering 70 | 71 | In [*minimal_examples*](minimal_examples.ipynb), we also show how graph abstraction behaves on 72 | data with different degrees of clustering. 73 | 74 | 75 | 76 | 77 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/README.md: -------------------------------------------------------------------------------- 1 | *First compiled: September 13, 2017.* 2 | 3 | ## Comparisons 4 | 5 | ### Contents 6 | 7 | * [*stemID2*](stemID) contains the analysis using StemID [(Grün *et al.*, Cell Stem Cell, 8 | 2016)](https://doi.org/10.1016/j.stem.2016.05.010) 9 | * [*monocle2*](monocle2.ipynb) contains the Monocle 2 analysis [(Qiu *et al.*, 10 | Nat. Meth., 2017)](https://doi.org/10.1038/nmeth.4402) 11 | * [*DPT*](dpt.ipynb) contains the DPT analysis [(Haghverdi *et al.*, Nat. Meth., 12 | 2016)](https://doi.org/10.1038/nmeth.3971) 13 | * [*eclair*](eclair) contains the analysis using Eclair [(Giecold *et al.*, Nucl. Acids 14 | Research, 2016)](https://doi.org/10.1093/nar/gkw452) 15 | 16 | ### Summary 17 | 18 | Recall the example of a continuous tree-like manifold with clusters (Figure 1 of the paper, [*krumsiek11_blobs/X.csv*](data/krumsiek11_blobs/X.csv)). This data has a clearly defined ground truth and very little noise. The following shows the result by graph abstraction. The computation took 0.55 s. 19 | 20 | 21 | 22 | Among the competing algorithms, only StemID 2, the successor of StemID [(Grün *et al.*, Cell Stem Cell, 2016)](https://doi.org/10.1016/j.stem.2016.05.010), is able to produce sensible results for this dataset. It produces a lineage tree that is partly correct (left panel) . The underlying sampled clusters and minimum spanning trees recognize one cluster as disconnected, but misinterpret the other cluster (right panel). This is from Supplemental Figure 1 of the paper. 23 | 24 | 25 | 26 | The stemID computation, including tSNE, ran for 17 min [(*stemID*)](stemID). Comparing this with 0.5 s for graph abstraction and 2.8 s for tSNE, both in Scanpy ([*minimal_examples*](../minimal_examples.ipynb)), Scanpy is 309 times faster. 27 | 28 | Monocle 2 is highly sensitive to the clusters in the data and hence not able to produce any meaningful result for this data, regardless of the choice of parameters. The three branches in the Monocle tree correspond to the two clusters and the continuous manifold (coloring as graph abstraction result). This is from Supplemental Figure 1 of the paper. 29 | 30 | 31 | 32 | The Monocle 2 computation took 13.8 s [(*monocle2*)](monocle2.ipynb) in the fastest case. Comparing this with 0.5 s for graph abstraction [(*../sim_data*)](../sim_data.ipynb), Scanpy is 28 times faster. 33 | 34 | We could not produce any result neither with Eclair [(Giecold *et al.*, 35 | Nucl. Acids Research, 2016](https://doi.org/10.1093/nar/gkw452)] nor with DPT 36 | [(Haghverdi *et al.*, Nat. Meth., 2016)](https://doi.org/10.1038/nmeth.3971) 37 | for this dataset. 38 | 39 | #### Reconstructing a simple tree 40 | 41 | To obtain sensible results also with Monocle 2, Eclair and DPT, we considered a simpler dataset that only contains the continous tree-like manifold [*krumsiek11/X.csv*](data/krumsiek11/X.csv). The abstracted graph for this looks as follows: 42 | 43 | 44 | 45 | Monocle 2 can now be tuned to yield a sensible result, which agrees with the result obtained using graph abstraction: 46 | 47 | 48 | 49 | Eclair [(Giecold *et al.*, Nucl. Acids Research, 2016)](https://doi.org/10.1093/nar/gkw452) obtains a wrong result even for the simple tree. 50 | 51 | 52 | 53 | DPT [(Haghverdi *et al.*, Nat. Meth., 2016)](https://doi.org/10.1038/nmeth.3971) does, by construction, not infer a lineage tree but merely detects branching subgroups; similar to a clustering algorithm. The branching groups detected for this simple dataset are meaningful. 54 | 55 | 56 | 57 | The visualization in diffusion maps looks as follows: 58 | 59 | 60 | 61 | Note that the choice of branching subgroups made by DPT looks very natural in diffusion maps but a bit more arbitrary in tSNE or graph drawing (FR). 62 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/data/krumsiek11/.DS_Store -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_adjacency_full_confidence.csv: -------------------------------------------------------------------------------- 1 | 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2621277725240071,1.0 2 | 0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0 3 | 0.0,0.0,0.0,0.8881492732977312,0.0,0.0,0.0,0.0,0.0 4 | 0.0,1.0,0.8881492732977312,0.0,0.0,0.0,0.9712938662420141,0.0,0.0 5 | 0.0,0.0,0.0,0.0,0.0,0.5984448517490395,0.0,0.0,0.0 6 | 0.0,0.0,0.0,0.0,0.5984448517490395,0.0,1.0,0.0,0.0 7 | 0.0,0.0,0.0,0.9712938662420141,0.0,1.0,0.0,0.0,1.0 8 | 0.2621277725240071,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 9 | 1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_adjacency_tree_confidence_csr_data.csv: -------------------------------------------------------------------------------- 1 | 0.2621277725240071 2 | 1.0 3 | 1.0 4 | 0.8881492732977312 5 | 0.9712938662420141 6 | 0.5984448517490395 7 | 1.0 8 | 1.0 9 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_adjacency_tree_confidence_csr_indices.csv: -------------------------------------------------------------------------------- 1 | 7 2 | 8 3 | 3 4 | 3 5 | 6 6 | 5 7 | 6 8 | 8 9 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_adjacency_tree_confidence_csr_indptr.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 2 3 | 3 4 | 4 5 | 5 6 | 6 7 | 7 8 | 8 9 | 8 10 | 8 11 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_adjacency_tree_confidence_csr_shape.csv: -------------------------------------------------------------------------------- 1 | 9 2 | 9 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_groups_colors_original.csv: -------------------------------------------------------------------------------- 1 | #bcbd22 2 | #d62728 3 | #9467bd 4 | #e377c2 5 | #2ca02c 6 | #17becf 7 | #8c564b 8 | #1f77b4 9 | #ff7f0e 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_groups_order.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_groups_order_original.csv: -------------------------------------------------------------------------------- 1 | 7 2 | 3 3 | 4 4 | 6 5 | 2 6 | 8 7 | 5 8 | 0 9 | 1 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/aga_groups_sizes.csv: -------------------------------------------------------------------------------- 1 | 36 2 | 83 3 | 72 4 | 46 5 | 89 6 | 35 7 | 67 8 | 107 9 | 105 10 | 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-------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/data_graph_norm_weights_csr_shape.csv: -------------------------------------------------------------------------------- 1 | 640 2 | 640 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/diffmap_evals.csv: -------------------------------------------------------------------------------- 1 | 0.9983482360839844 2 | 0.9968317151069641 3 | 0.9922014474868774 4 | 0.9918904304504395 5 | 0.9814634919166565 6 | 0.9648339748382568 7 | 0.9626019597053528 8 | 0.9497093558311462 9 | 0.9356895685195923 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/louvain_groups_colors.csv: -------------------------------------------------------------------------------- 1 | #1f77b4 2 | #ff7f0e 3 | #2ca02c 4 | #d62728 5 | #9467bd 6 | #8c564b 7 | #e377c2 8 | #bcbd22 9 | #17becf 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/louvain_groups_order.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/louvain_params.csv: -------------------------------------------------------------------------------- 1 | resolution 2 | 1.0 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/smp_keys_multicol.csv: -------------------------------------------------------------------------------- 1 | X_tsne 2 | X_diffmap 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/add/var_keys_multicol.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/data/krumsiek11/add/var_keys_multicol.csv -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11/var.csv: -------------------------------------------------------------------------------- 1 | var_names,Gata2,Gata1,Fog1,EKLF,Fli1,SCL,Cebpa,Pu.1,cJun,EgrNab,Gfi1 2 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_adjacency_full_confidence.csv: -------------------------------------------------------------------------------- 1 | 0.0,0.0,0.0,0.0,0.0,0.0,0.9563389704816692,0.0,0.0,0.0,1.0,0.0,0.0 2 | 0.0,0.0,0.0,0.8276915305612415,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 3 | 0.0,0.0,0.0,0.7958724644372855,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 4 | 0.0,0.8276915305612415,0.7958724644372855,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0 5 | 0.0,0.0,0.0,0.0,0.0,0.5630871170772119,0.0,0.0,0.0,0.0,0.0,0.0,0.0 6 | 0.0,0.0,0.0,0.0,0.5630871170772119,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0 7 | 0.9563389704816692,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 8 | 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 9 | 0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0 10 | 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2142993320271078,0.0,0.0 11 | 1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2142993320271078,0.0,0.0,0.0 12 | 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 13 | 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_adjacency_tree_confidence_csr_data.csv: -------------------------------------------------------------------------------- 1 | 1.0 2 | 0.8276915305612415 3 | 0.7958724644372855 4 | 1.0 5 | 0.5630871170772119 6 | 0.9563389704816692 7 | 1.0 8 | 1.0 9 | 0.2142993320271078 10 | 1.0 11 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_adjacency_tree_confidence_csr_indices.csv: -------------------------------------------------------------------------------- 1 | 10 2 | 1 3 | 2 4 | 6 5 | 4 6 | 0 7 | 5 8 | 7 9 | 10 10 | 11 11 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_adjacency_tree_confidence_csr_indptr.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 1 4 | 1 5 | 4 6 | 4 7 | 5 8 | 7 9 | 7 10 | 8 11 | 9 12 | 9 13 | 9 14 | 10 15 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_adjacency_tree_confidence_csr_shape.csv: -------------------------------------------------------------------------------- 1 | 13 2 | 13 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_groups_colors_original.csv: -------------------------------------------------------------------------------- 1 | #ff9896 2 | #bcbd22 3 | #17becf 4 | #ffbb78 5 | #e377c2 6 | #98df8a 7 | #aec7e8 8 | #ff7f0e 9 | #2ca02c 10 | #9467bd 11 | #8c564b 12 | #d62728 13 | #1f77b4 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_groups_order.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | 9 11 | 10 12 | 11 13 | 12 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_groups_order_original.csv: -------------------------------------------------------------------------------- 1 | 12 2 | 7 3 | 8 4 | 10 5 | 6 6 | 11 7 | 9 8 | 1 9 | 2 10 | 4 11 | 5 12 | 3 13 | 0 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/aga_groups_sizes.csv: -------------------------------------------------------------------------------- 1 | 34 2 | 76 3 | 67 4 | 61 5 | 87 6 | 39 7 | 65 8 | 185 9 | 135 10 | 107 11 | 104 12 | 121 13 | 199 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/data_graph_distance_local_csr_indptr.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 29 3 | 58 4 | 87 5 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-------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/data_graph_norm_weights_csr_shape.csv: -------------------------------------------------------------------------------- 1 | 1280 2 | 1280 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/diffmap_evals.csv: -------------------------------------------------------------------------------- 1 | 1.0 2 | 0.9999999403953552 3 | 0.9984188079833984 4 | 0.9969583749771118 5 | 0.9920812249183655 6 | 0.9909048676490784 7 | 0.9824625849723816 8 | 0.9651806950569153 9 | 0.9614251255989075 10 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/draw_graph_layout.csv: -------------------------------------------------------------------------------- 1 | fr 2 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/louvain_groups_colors.csv: -------------------------------------------------------------------------------- 1 | #1f77b4 2 | #ff7f0e 3 | #2ca02c 4 | #d62728 5 | #9467bd 6 | #8c564b 7 | #e377c2 8 | #bcbd22 9 | #17becf 10 | #aec7e8 11 | #ffbb78 12 | #98df8a 13 | #ff9896 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/louvain_groups_order.csv: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | 9 11 | 10 12 | 11 13 | 12 14 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/louvain_params.csv: -------------------------------------------------------------------------------- 1 | resolution 2 | 2.5 3 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/smp_keys_multicol.csv: -------------------------------------------------------------------------------- 1 | X_tsne 2 | X_diffmap 3 | X_draw_graph_fr 4 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/add/var_keys_multicol.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/data/krumsiek11_blobs/add/var_keys_multicol.csv -------------------------------------------------------------------------------- /minimal_examples/comparisons/data/krumsiek11_blobs/var.csv: -------------------------------------------------------------------------------- 1 | var_names,Gata2,Gata1,Fog1,EKLF,Fli1,SCL,Cebpa,Pu.1,cJun,EgrNab,Gfi1 2 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/consensus_adjacency_matrix.txt: -------------------------------------------------------------------------------- 1 | 0.0 0.0 1.0 0.0 2 | 0.0 0.0 0.0 1.0 3 | 1.0 0.0 0.0 1.0 4 | 0.0 1.0 1.0 0.0 5 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/consensus_distances_matrix.txt: -------------------------------------------------------------------------------- 1 | 0.0000 3.9659 1.9659 2.9659 2 | 3.9659 0.0000 2.0000 1.0000 3 | 1.9659 2.0000 0.0000 1.0000 4 | 2.9659 1.0000 1.0000 0.0000 5 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/consensus_labels.txt: -------------------------------------------------------------------------------- 1 | 3 2 | 3 3 | 3 4 | 3 5 | 3 6 | 3 7 | 3 8 | 3 9 | 3 10 | 3 11 | 3 12 | 3 13 | 3 14 | 3 15 | 3 16 | 3 17 | 3 18 | 3 19 | 3 20 | 3 21 | 3 22 | 2 23 | 2 24 | 2 25 | 2 26 | 2 27 | 2 28 | 2 29 | 2 30 | 2 31 | 2 32 | 2 33 | 2 34 | 2 35 | 2 36 | 2 37 | 2 38 | 2 39 | 2 40 | 2 41 | 2 42 | 2 43 | 2 44 | 2 45 | 2 46 | 2 47 | 2 48 | 2 49 | 2 50 | 2 51 | 2 52 | 2 53 | 2 54 | 2 55 | 2 56 | 2 57 | 2 58 | 2 59 | 2 60 | 2 61 | 2 62 | 2 63 | 2 64 | 2 65 | 2 66 | 2 67 | 2 68 | 2 69 | 2 70 | 0 71 | 0 72 | 0 73 | 0 74 | 0 75 | 0 76 | 0 77 | 0 78 | 0 79 | 0 80 | 0 81 | 0 82 | 0 83 | 0 84 | 0 85 | 0 86 | 0 87 | 0 88 | 0 89 | 0 90 | 0 91 | 0 92 | 0 93 | 0 94 | 0 95 | 0 96 | 0 97 | 0 98 | 0 99 | 0 100 | 0 101 | 0 102 | 0 103 | 0 104 | 0 105 | 0 106 | 0 107 | 0 108 | 0 109 | 0 110 | 0 111 | 0 112 | 0 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363 | 2 364 | 2 365 | 2 366 | 2 367 | 2 368 | 2 369 | 2 370 | 2 371 | 2 372 | 2 373 | 2 374 | 2 375 | 2 376 | 2 377 | 2 378 | 2 379 | 2 380 | 2 381 | 2 382 | 2 383 | 2 384 | 2 385 | 2 386 | 2 387 | 2 388 | 2 389 | 2 390 | 2 391 | 2 392 | 2 393 | 2 394 | 2 395 | 2 396 | 0 397 | 0 398 | 0 399 | 0 400 | 0 401 | 0 402 | 0 403 | 0 404 | 0 405 | 0 406 | 0 407 | 0 408 | 0 409 | 0 410 | 0 411 | 0 412 | 0 413 | 0 414 | 0 415 | 0 416 | 0 417 | 0 418 | 0 419 | 0 420 | 0 421 | 0 422 | 0 423 | 0 424 | 0 425 | 0 426 | 0 427 | 0 428 | 0 429 | 0 430 | 0 431 | 0 432 | 0 433 | 0 434 | 0 435 | 0 436 | 0 437 | 0 438 | 0 439 | 0 440 | 0 441 | 0 442 | 0 443 | 0 444 | 0 445 | 0 446 | 0 447 | 0 448 | 0 449 | 0 450 | 0 451 | 0 452 | 0 453 | 0 454 | 0 455 | 0 456 | 0 457 | 0 458 | 0 459 | 0 460 | 0 461 | 0 462 | 0 463 | 0 464 | 0 465 | 0 466 | 0 467 | 0 468 | 0 469 | 0 470 | 0 471 | 0 472 | 0 473 | 0 474 | 0 475 | 0 476 | 0 477 | 0 478 | 0 479 | 0 480 | 0 481 | 3 482 | 3 483 | 3 484 | 3 485 | 3 486 | 3 487 | 3 488 | 3 489 | 3 490 | 3 491 | 3 492 | 3 493 | 3 494 | 3 495 | 3 496 | 3 497 | 3 498 | 3 499 | 3 500 | 3 501 | 3 502 | 3 503 | 3 504 | 3 505 | 3 506 | 3 507 | 3 508 | 3 509 | 3 510 | 3 511 | 3 512 | 3 513 | 3 514 | 3 515 | 3 516 | 3 517 | 3 518 | 3 519 | 3 520 | 3 521 | 3 522 | 3 523 | 3 524 | 3 525 | 3 526 | 3 527 | 3 528 | 3 529 | 3 530 | 3 531 | 3 532 | 3 533 | 3 534 | 3 535 | 3 536 | 3 537 | 3 538 | 3 539 | 3 540 | 3 541 | 3 542 | 3 543 | 3 544 | 3 545 | 3 546 | 3 547 | 3 548 | 3 549 | 3 550 | 3 551 | 3 552 | 3 553 | 3 554 | 3 555 | 3 556 | 3 557 | 3 558 | 3 559 | 3 560 | 3 561 | 3 562 | 3 563 | 3 564 | 3 565 | 3 566 | 3 567 | 3 568 | 3 569 | 3 570 | 3 571 | 3 572 | 3 573 | 3 574 | 3 575 | 3 576 | 3 577 | 3 578 | 3 579 | 3 580 | 3 581 | 3 582 | 3 583 | 3 584 | 3 585 | 3 586 | 3 587 | 3 588 | 3 589 | 3 590 | 3 591 | 3 592 | 3 593 | 3 594 | 3 595 | 3 596 | 3 597 | 3 598 | 3 599 | 3 600 | 3 601 | 3 602 | 3 603 | 3 604 | 3 605 | 3 606 | 3 607 | 3 608 | 3 609 | 3 610 | 3 611 | 3 612 | 3 613 | 3 614 | 3 615 | 3 616 | 3 617 | 3 618 | 3 619 | 3 620 | 3 621 | 3 622 | 3 623 | 3 624 | 3 625 | 3 626 | 3 627 | 3 628 | 3 629 | 3 630 | 3 631 | 3 632 | 3 633 | 3 634 | 3 635 | 3 636 | 3 637 | 3 638 | 3 639 | 3 640 | 3 641 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/consensus_topological_distances_matrix.txt: -------------------------------------------------------------------------------- 1 | 0.0000 3.0000 1.0000 2.0000 2 | 3.0000 0.0000 2.0000 1.0000 3 | 1.0000 2.0000 0.0000 1.0000 4 | 2.0000 1.0000 1.0000 0.0000 5 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/ensemble_distances_distributions.txt: -------------------------------------------------------------------------------- 1 | (cluster a, cluster b, a < b) (value of the distance, number of occurrences) 2 | (1, 429250.0) (3, 459550.0) 3 | (1, 582400.0) (3, 544000.0) 4 | (2, 2068000.0) 5 | (2, 646400.0) 6 | (1, 1186750.0) 7 | (1, 1504000.0) 8 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/ensemble_distances_means.txt: -------------------------------------------------------------------------------- 1 | 0.0000 2.0341 1.9659 2.0000 2 | 2.0341 0.0000 2.0000 1.0000 3 | 1.9659 2.0000 0.0000 1.0000 4 | 2.0000 1.0000 1.0000 0.0000 5 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/ensemble_distances_medians.txt: -------------------------------------------------------------------------------- 1 | 0 3 1 2 2 | 3 0 2 1 3 | 1 2 0 1 4 | 2 1 1 0 5 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/ensemble_distances_variances.txt: -------------------------------------------------------------------------------- 1 | 0.000000 0.998838 0.998838 0.000000 2 | 0.998838 0.000000 0.000000 0.000000 3 | 0.998838 0.000000 0.000000 0.000000 4 | 0.000000 0.000000 0.000000 0.000000 5 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/ECLAIR_instance/ECLAIR_ensemble_clustering_files/2017-09-15__09:09:31/mst_adjacency_list.txt: -------------------------------------------------------------------------------- 1 | RUN 0 2 | 4 3 | 2 4 | 3 5 | 1 6 | 4 7 | 1 8 | 0 9 | 2 10 | 11 | 0 12 | 1 13 | 3 14 | 5 15 | 6 16 | 8 17 | 18 | RUN 1 19 | 2 20 | 3 21 | 4 22 | 0 23 | 4 24 | 1 25 | 1 26 | 2 27 | 28 | 0 29 | 1 30 | 3 31 | 5 32 | 6 33 | 8 34 | 35 | RUN 2 36 | 2 37 | 4 38 | 3 39 | 4 40 | 0 41 | 1 42 | 0 43 | 1 44 | 45 | 0 46 | 2 47 | 4 48 | 5 49 | 6 50 | 8 51 | 52 | RUN 3 53 | 4 54 | 3 55 | 3 56 | 4 57 | 1 58 | 2 59 | 0 60 | 2 61 | 62 | 0 63 | 1 64 | 2 65 | 4 66 | 6 67 | 8 68 | 69 | RUN 4 70 | 1 71 | 4 72 | 0 73 | 3 74 | 4 75 | 1 76 | 0 77 | 2 78 | 79 | 0 80 | 2 81 | 4 82 | 5 83 | 6 84 | 8 85 | 86 | RUN 5 87 | 4 88 | 3 89 | 3 90 | 4 91 | 1 92 | 2 93 | 0 94 | 2 95 | 96 | 0 97 | 1 98 | 2 99 | 4 100 | 6 101 | 8 102 | 103 | RUN 6 104 | 2 105 | 4 106 | 2 107 | 3 108 | 0 109 | 1 110 | 1 111 | 0 112 | 113 | 0 114 | 2 115 | 4 116 | 6 117 | 7 118 | 8 119 | 120 | RUN 7 121 | 2 122 | 4 123 | 2 124 | 3 125 | 0 126 | 1 127 | 1 128 | 0 129 | 130 | 0 131 | 2 132 | 4 133 | 6 134 | 7 135 | 8 136 | 137 | RUN 8 138 | 3 139 | 4 140 | 2 141 | 3 142 | 1 143 | 0 144 | 1 145 | 0 146 | 147 | 0 148 | 2 149 | 4 150 | 5 151 | 7 152 | 8 153 | 154 | RUN 9 155 | 2 156 | 2 157 | 4 158 | 0 159 | 1 160 | 4 161 | 1 162 | 3 163 | 164 | 0 165 | 1 166 | 3 167 | 5 168 | 6 169 | 8 170 | 171 | RUN 10 172 | 3 173 | 4 174 | 2 175 | 3 176 | 1 177 | 0 178 | 1 179 | 0 180 | 181 | 0 182 | 2 183 | 4 184 | 5 185 | 7 186 | 8 187 | 188 | RUN 11 189 | 3 190 | 4 191 | 2 192 | 1 193 | 3 194 | 0 195 | 2 196 | 0 197 | 198 | 0 199 | 2 200 | 3 201 | 5 202 | 7 203 | 8 204 | 205 | RUN 12 206 | 3 207 | 2 208 | 4 209 | 1 210 | 3 211 | 0 212 | 2 213 | 1 214 | 215 | 0 216 | 1 217 | 3 218 | 5 219 | 7 220 | 8 221 | 222 | RUN 13 223 | 2 224 | 4 225 | 2 226 | 3 227 | 0 228 | 1 229 | 1 230 | 0 231 | 232 | 0 233 | 2 234 | 4 235 | 6 236 | 7 237 | 8 238 | 239 | RUN 14 240 | 2 241 | 4 242 | 2 243 | 3 244 | 0 245 | 1 246 | 1 247 | 0 248 | 249 | 0 250 | 2 251 | 4 252 | 6 253 | 7 254 | 8 255 | 256 | RUN 15 257 | 3 258 | 4 259 | 3 260 | 4 261 | 0 262 | 1 263 | 0 264 | 2 265 | 266 | 0 267 | 2 268 | 3 269 | 4 270 | 6 271 | 8 272 | 273 | RUN 16 274 | 3 275 | 2 276 | 3 277 | 1 278 | 4 279 | 0 280 | 1 281 | 2 282 | 283 | 0 284 | 1 285 | 3 286 | 5 287 | 7 288 | 8 289 | 290 | RUN 17 291 | 4 292 | 2 293 | 3 294 | 1 295 | 4 296 | 1 297 | 0 298 | 2 299 | 300 | 0 301 | 1 302 | 3 303 | 5 304 | 6 305 | 8 306 | 307 | RUN 18 308 | 4 309 | 2 310 | 3 311 | 1 312 | 4 313 | 1 314 | 0 315 | 2 316 | 317 | 0 318 | 1 319 | 3 320 | 5 321 | 6 322 | 8 323 | 324 | RUN 19 325 | 2 326 | 2 327 | 4 328 | 0 329 | 1 330 | 4 331 | 1 332 | 3 333 | 334 | 0 335 | 1 336 | 3 337 | 5 338 | 6 339 | 8 340 | 341 | RUN 20 342 | 2 343 | 3 344 | 4 345 | 0 346 | 3 347 | 1 348 | 2 349 | 1 350 | 351 | 0 352 | 1 353 | 3 354 | 5 355 | 7 356 | 8 357 | 358 | RUN 21 359 | 4 360 | 2 361 | 3 362 | 1 363 | 4 364 | 1 365 | 0 366 | 2 367 | 368 | 0 369 | 1 370 | 3 371 | 5 372 | 6 373 | 8 374 | 375 | RUN 22 376 | 4 377 | 2 378 | 3 379 | 1 380 | 4 381 | 1 382 | 0 383 | 2 384 | 385 | 0 386 | 1 387 | 3 388 | 5 389 | 6 390 | 8 391 | 392 | RUN 23 393 | 2 394 | 4 395 | 2 396 | 3 397 | 0 398 | 1 399 | 1 400 | 0 401 | 402 | 0 403 | 2 404 | 4 405 | 6 406 | 7 407 | 8 408 | 409 | RUN 24 410 | 2 411 | 4 412 | 2 413 | 3 414 | 0 415 | 1 416 | 1 417 | 0 418 | 419 | 0 420 | 2 421 | 4 422 | 6 423 | 7 424 | 8 425 | 426 | RUN 25 427 | 1 428 | 2 429 | 0 430 | 4 431 | 0 432 | 3 433 | 2 434 | 1 435 | 436 | 0 437 | 2 438 | 4 439 | 6 440 | 7 441 | 8 442 | 443 | RUN 26 444 | 3 445 | 4 446 | 2 447 | 3 448 | 1 449 | 0 450 | 1 451 | 0 452 | 453 | 0 454 | 2 455 | 4 456 | 5 457 | 7 458 | 8 459 | 460 | RUN 27 461 | 3 462 | 4 463 | 2 464 | 3 465 | 1 466 | 0 467 | 1 468 | 0 469 | 470 | 0 471 | 2 472 | 4 473 | 5 474 | 7 475 | 8 476 | 477 | RUN 28 478 | 3 479 | 4 480 | 2 481 | 3 482 | 1 483 | 0 484 | 1 485 | 0 486 | 487 | 0 488 | 2 489 | 4 490 | 5 491 | 7 492 | 8 493 | 494 | RUN 29 495 | 3 496 | 4 497 | 2 498 | 1 499 | 3 500 | 0 501 | 2 502 | 0 503 | 504 | 0 505 | 2 506 | 3 507 | 5 508 | 7 509 | 8 510 | 511 | RUN 30 512 | 4 513 | 3 514 | 3 515 | 4 516 | 1 517 | 2 518 | 0 519 | 2 520 | 521 | 0 522 | 1 523 | 2 524 | 4 525 | 6 526 | 8 527 | 528 | RUN 31 529 | 4 530 | 2 531 | 3 532 | 1 533 | 4 534 | 1 535 | 0 536 | 2 537 | 538 | 0 539 | 1 540 | 3 541 | 5 542 | 6 543 | 8 544 | 545 | RUN 32 546 | 1 547 | 4 548 | 0 549 | 3 550 | 4 551 | 1 552 | 0 553 | 2 554 | 555 | 0 556 | 2 557 | 4 558 | 5 559 | 6 560 | 8 561 | 562 | RUN 33 563 | 3 564 | 3 565 | 4 566 | 4 567 | 0 568 | 1 569 | 1 570 | 2 571 | 572 | 0 573 | 1 574 | 3 575 | 4 576 | 6 577 | 8 578 | 579 | RUN 34 580 | 1 581 | 3 582 | 0 583 | 4 584 | 4 585 | 0 586 | 1 587 | 2 588 | 589 | 0 590 | 2 591 | 4 592 | 5 593 | 6 594 | 8 595 | 596 | RUN 35 597 | 3 598 | 4 599 | 2 600 | 1 601 | 4 602 | 0 603 | 0 604 | 2 605 | 606 | 0 607 | 2 608 | 3 609 | 5 610 | 6 611 | 8 612 | 613 | RUN 36 614 | 2 615 | 3 616 | 4 617 | 0 618 | 3 619 | 1 620 | 2 621 | 1 622 | 623 | 0 624 | 1 625 | 3 626 | 5 627 | 7 628 | 8 629 | 630 | RUN 37 631 | 1 632 | 4 633 | 0 634 | 3 635 | 4 636 | 1 637 | 0 638 | 2 639 | 640 | 0 641 | 2 642 | 4 643 | 5 644 | 6 645 | 8 646 | 647 | RUN 38 648 | 1 649 | 3 650 | 0 651 | 2 652 | 1 653 | 4 654 | 0 655 | 2 656 | 657 | 0 658 | 2 659 | 4 660 | 6 661 | 7 662 | 8 663 | 664 | RUN 39 665 | 3 666 | 4 667 | 3 668 | 4 669 | 0 670 | 1 671 | 0 672 | 2 673 | 674 | 0 675 | 2 676 | 3 677 | 4 678 | 6 679 | 8 680 | 681 | RUN 40 682 | 1 683 | 2 684 | 0 685 | 3 686 | 0 687 | 4 688 | 1 689 | 2 690 | 691 | 0 692 | 2 693 | 4 694 | 6 695 | 7 696 | 8 697 | 698 | RUN 41 699 | 3 700 | 4 701 | 2 702 | 3 703 | 1 704 | 0 705 | 1 706 | 0 707 | 708 | 0 709 | 2 710 | 4 711 | 5 712 | 7 713 | 8 714 | 715 | RUN 42 716 | 3 717 | 4 718 | 2 719 | 3 720 | 1 721 | 0 722 | 1 723 | 0 724 | 725 | 0 726 | 2 727 | 4 728 | 5 729 | 7 730 | 8 731 | 732 | RUN 43 733 | 2 734 | 3 735 | 4 736 | 0 737 | 3 738 | 1 739 | 2 740 | 1 741 | 742 | 0 743 | 1 744 | 3 745 | 5 746 | 7 747 | 8 748 | 749 | RUN 44 750 | 1 751 | 3 752 | 0 753 | 4 754 | 4 755 | 0 756 | 1 757 | 2 758 | 759 | 0 760 | 2 761 | 4 762 | 5 763 | 6 764 | 8 765 | 766 | RUN 45 767 | 2 768 | 4 769 | 2 770 | 0 771 | 1 772 | 4 773 | 0 774 | 3 775 | 776 | 0 777 | 2 778 | 3 779 | 5 780 | 6 781 | 8 782 | 783 | RUN 46 784 | 2 785 | 3 786 | 4 787 | 0 788 | 3 789 | 1 790 | 2 791 | 1 792 | 793 | 0 794 | 1 795 | 3 796 | 5 797 | 7 798 | 8 799 | 800 | RUN 47 801 | 2 802 | 3 803 | 3 804 | 0 805 | 4 806 | 0 807 | 1 808 | 2 809 | 810 | 0 811 | 2 812 | 3 813 | 5 814 | 7 815 | 8 816 | 817 | RUN 48 818 | 1 819 | 4 820 | 0 821 | 3 822 | 4 823 | 1 824 | 0 825 | 2 826 | 827 | 0 828 | 2 829 | 4 830 | 5 831 | 6 832 | 8 833 | 834 | RUN 49 835 | 3 836 | 4 837 | 2 838 | 3 839 | 1 840 | 0 841 | 1 842 | 0 843 | 844 | 0 845 | 2 846 | 4 847 | 5 848 | 7 849 | 8 850 | 851 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/README.md: -------------------------------------------------------------------------------- 1 | # Comparison with ECLAIR [(Giecold *et al.*, Nucl. Acids Research, 2016)](https://doi.org/10.1093/nar/gkw452) 2 | 3 | ## Contents 4 | 5 | * [*eclair_plots*](eclair_plots.ipynb) visualizes the results of running eclair 6 | * [*logfile_run_X_krumsiek11_scaled.txt*](logfile_run_X_krumsiek11_scaled.txt) shows the logging output and the parameters that allow to reproduce the results discussed [here](../) 7 | * [*X_krumsiek11_scaled.txt*](X_krumsiek11_scaled.txt) contains the scaled data matrix for the simple tree, [*X_krumsiek11.txt*](X_krumsiek11.txt) contains the equivalent unscaled matrix and [*X_krumsiek11_blobs.txt*](X_krumsiek11_blobs.txt) contains the data describing tree and clusters; all in tab-separated format as required by ECLAIR. These files have been generated in [*../../comparisons_exports*](../../comparisons_exports.ipynb), which also provides visualizations of the data. 8 | 9 | ## Notes 10 | 11 | * See the discussion of the results [here](../). 12 | 13 | * We acknowledge help by G. Giecold and S. P. Garcia, who adviced to scale the data matrix and provided default parameters. Parst of the correspondence are archived here: https://github.com/GGiecold/ECLAIR/issues/3 14 | 15 | * The algorithm could not handle the unnormalized data matrices [*X_krumsiek11.txt*](X_krumsiek11.txt) and [*X_krumsiek11_blobs.txt*](X_krumsiek11_blobs.txt). 16 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/figures/aga_graph_eclair_tree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/eclair/figures/aga_graph_eclair_tree.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/figures/draw_graph_fr_eclair_clusters.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/eclair/figures/draw_graph_fr_eclair_clusters.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/eclair/logfile_run_X_krumsiek11_scaled.txt: -------------------------------------------------------------------------------- 1 | (py27) Alexs-MBP:eclair alexwolf$ python -m ECLAIR.Build_instance 2 | 3 | ***************************************** 4 | ***************************************** 5 | *** ECLAIR *** 6 | ***************************************** 7 | ***************************************** 8 | 9 | 10 | ECLAIR: provide the path to the file holding the data to be analyzed: 11 | ./X_krumsiek11_scaled.txt 12 | 13 | ECLAIR: how may rows count as header in this file? Enter '0' if the file is not adorned by any header: 14 | 1 15 | 16 | ECLAIR: which column of the data-file holds the names, tags or IDs of its samples? Enter '0' for the 1st column, '1' for the second, etc.: 17 | 0 18 | 19 | ECLAIR: does this data-set include some time information? [Y/n] 20 | n 21 | 22 | ECLAIR: you may choose to exclude some columns as features. If this option does not apply, simply press 'Enter'. Otherwise, provide a list of numbers: 23 | 24 | 25 | ECLAIR: please give an estimate of the number of samples in this data-set: 26 | 600 27 | 28 | ECLAIR: please enter the number of trees that will be bagged into a forest (a value of '50' is recommended): 29 | 50 30 | 31 | ECLAIR: how many points do you want to sample from the dataset? Please provide a fraction of the total number of cells: 32 | 0.8 33 | 34 | ECLAIR: choose the clustering algorithm to be applied to each of 50 subsamples from your data-set. 35 | Available methods: affinity propagation (1), DBSCAN (2), hierarchical clustering (3) & k-means (4) 36 | 37 | 4 38 | 39 | ECLAIR: how many centroids to generate for each run of k-means clustering? 40 | 5 41 | 42 | ECLAIR: the total number of consensus clusters defaults to the highest number of clusters encountered in each of the 50 independent runs of subsamplings and clusterings. Do you want to provide a value instead? [Y/n] 43 | n 44 | 45 | 46 | 47 | ECLAIR INFO 2017-09-15 09:09:31: ready to proceed! 48 | 49 | 50 | 51 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 1. 52 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0896 seconds. 53 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 2. 54 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0386 seconds. 55 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 3. 56 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.046 seconds. 57 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 4. 58 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0503 seconds. 59 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 5. 60 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0482 seconds. 61 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 6. 62 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0516 seconds. 63 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 7. 64 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0496 seconds. 65 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 8. 66 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0489 seconds. 67 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 9. 68 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0468 seconds. 69 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 10. 70 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0479 seconds. 71 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 11. 72 | ECLAIR INFO 2017-09-15 09:09:31: done with this round of clustering; it took 0.0498 seconds. 73 | ECLAIR INFO 2017-09-15 09:09:31: starting run of clustering number 12. 74 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.048 seconds. 75 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 13. 76 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0505 seconds. 77 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 14. 78 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0546 seconds. 79 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 15. 80 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0499 seconds. 81 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 16. 82 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0489 seconds. 83 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 17. 84 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.044 seconds. 85 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 18. 86 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0456 seconds. 87 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 19. 88 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0472 seconds. 89 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 20. 90 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0508 seconds. 91 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 21. 92 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0442 seconds. 93 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 22. 94 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0487 seconds. 95 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 23. 96 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0489 seconds. 97 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 24. 98 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0489 seconds. 99 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 25. 100 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0454 seconds. 101 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 26. 102 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0485 seconds. 103 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 27. 104 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.048 seconds. 105 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 28. 106 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0515 seconds. 107 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 29. 108 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0501 seconds. 109 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 30. 110 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.0459 seconds. 111 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 31. 112 | ECLAIR INFO 2017-09-15 09:09:32: done with this round of clustering; it took 0.046 seconds. 113 | ECLAIR INFO 2017-09-15 09:09:32: starting run of clustering number 32. 114 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0423 seconds. 115 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 33. 116 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0441 seconds. 117 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 34. 118 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.047 seconds. 119 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 35. 120 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0457 seconds. 121 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 36. 122 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0509 seconds. 123 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 37. 124 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.051 seconds. 125 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 38. 126 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0536 seconds. 127 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 39. 128 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0467 seconds. 129 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 40. 130 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0459 seconds. 131 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 41. 132 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0487 seconds. 133 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 42. 134 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0511 seconds. 135 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 43. 136 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.047 seconds. 137 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 44. 138 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0457 seconds. 139 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 45. 140 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0428 seconds. 141 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 46. 142 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0444 seconds. 143 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 47. 144 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0469 seconds. 145 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 48. 146 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0524 seconds. 147 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 49. 148 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0457 seconds. 149 | ECLAIR INFO 2017-09-15 09:09:33: starting run of clustering number 50. 150 | ECLAIR INFO 2017-09-15 09:09:33: done with this round of clustering; it took 0.0502 seconds. 151 | ***** 152 | INFO: Cluster_Ensembles: CSPA: consensus clustering using CSPA. 153 | 154 | # 155 | INFO: Cluster_Ensembles: wgraph: writing wgraph_CSPA. 156 | # 157 | 158 | # 159 | INFO: Cluster_Ensembles: sgraph: calling gpmetis for graph partitioning. 160 | ****************************************************************************** 161 | METIS 5.0 Copyright 1998-13, Regents of the University of Minnesota 162 | (HEAD: , Built on: Sep 15 2017, 09:03:02) 163 | size of idx_t: 32bits, real_t: 32bits, idx_t *: 64bits 164 | 165 | Graph Information ----------------------------------------------------------- 166 | Name: ./wgraph_CSPA, #Vertices: 640, #Edges: 48338, #Parts: 5 167 | 168 | Options --------------------------------------------------------------------- 169 | ptype=kway, objtype=cut, ctype=shem, rtype=greedy, iptype=metisrb 170 | dbglvl=0, ufactor=1.030, no2hop=NO, minconn=NO, contig=NO, nooutput=NO 171 | seed=-1, niter=10, ncuts=1 172 | 173 | Direct k-way Partitioning --------------------------------------------------- 174 | - Edgecut: 16419216, communication volume: 405. 175 | 176 | - Balance: 177 | constraint #0: 1.023 out of 0.008 178 | 179 | - Most overweight partition: 180 | pid: 4, actual: 131, desired: 128, ratio: 1.02. 181 | 182 | - Subdomain connectivity: max: 3, min: 0, avg: 1.60 183 | 184 | - The original graph had 5 connected components and the resulting 185 | partitioning after removing the cut edges has 8 components. 186 | Timing Information ---------------------------------------------------------- 187 | I/O: 0.008 sec 188 | Partitioning: 0.003 sec (METIS time) 189 | Reporting: 0.001 sec 190 | 191 | Memory Information ---------------------------------------------------------- 192 | Max memory used: 0.963 MB 193 | ****************************************************************************** 194 | INFO: Cluster_Ensembles: sgraph: (hyper)-graph partitioning completed; loading wgraph_CSPA.part.5 195 | # 196 | 197 | INFO: Cluster_Ensembles: cluster_ensembles: CSPA at 0.776945860086. 198 | ***** 199 | 200 | ***** 201 | INFO: Cluster_Ensembles: HGPA: consensus clustering using HGPA. 202 | 203 | # 204 | INFO: Cluster_Ensembles: wgraph: writing wgraph_HGPA. 205 | INFO: Cluster_Ensembles: wgraph: 640 vertices and 250 non-zero hyper-edges. 206 | # 207 | 208 | # 209 | INFO: Cluster_Ensembles: sgraph: calling shmetis for hypergraph partitioning. 210 | ******************************************************************************* 211 | HMETIS 1.5.3 Copyright 1998, Regents of the University of Minnesota 212 | 213 | HyperGraph Information ----------------------------------------------------- 214 | Name: ./wgraph_HGPA, #Vtxs: 640, #Hedges: 250, #Parts: 5, UBfactor: 0.15 215 | Options: HFC, FM, Reconst-False, V-cycles @ End, No Fixed Vertices 216 | 217 | Recursive Partitioning... -------------------------------------------------- 218 | 219 | Bisecting a hgraph of size [vertices=640, hedges=250, balance=0.40] 220 | The mincut for this bisection = 32000, (average = 32000.0) (balance = 0.50) 221 | 222 | Bisecting a hgraph of size [vertices=320, hedges=0, balance=0.50] 223 | The mincut for this bisection = 0, (average = 0.0) (balance = 0.50) 224 | 225 | Bisecting a hgraph of size [vertices=320, hedges=0, balance=0.33] 226 | The mincut for this bisection = 0, (average = 0.0) (balance = 0.50) 227 | 228 | Bisecting a hgraph of size [vertices=160, hedges=0, balance=0.50] 229 | The mincut for this bisection = 0, (average = 0.0) (balance = 0.50) 230 | 231 | -------------------------------------------------------------------------- 232 | Summary for the 5-way partition: 233 | Hyperedge Cut: 32000 (minimize) 234 | Sum of External Degrees: 160000 (minimize) 235 | Scaled Cost: 5.47e-01 (minimize) 236 | Absorption: 240.97 (maximize) 237 | 238 | Partition Sizes & External Degrees: 239 | 160[32000] 160[32000] 160[32000] 80[32000] 80[32000] 240 | 241 | 242 | Timing Information --------------------------------------------------------- 243 | Partitioning Time: 0.004sec 244 | I/O Time: 0.004sec 245 | ******************************************************************************* 246 | INFO: Cluster_Ensembles: sgraph: (hyper)-graph partitioning completed; loading wgraph_HGPA.part.5 247 | # 248 | 249 | INFO: Cluster_Ensembles: cluster_ensembles: HGPA at 0.000178067785293. 250 | ***** 251 | 252 | ***** 253 | INFO: Cluster_Ensembles: MCLA: consensus clustering using MCLA. 254 | INFO: Cluster_Ensembles: MCLA: preparing graph for meta-clustering. 255 | INFO: Cluster_Ensembles: MCLA: done filling hypergraph adjacency matrix. Starting computation of Jaccard similarity matrix. 256 | INFO: Cluster_Ensembles: MCLA: starting computation of Jaccard similarity matrix. 257 | INFO: Cluster_Ensembles: MCLA: done computing the matrix of pairwise Jaccard similarity scores. 258 | 259 | # 260 | INFO: Cluster_Ensembles: wgraph: writing wgraph_MCLA. 261 | # 262 | 263 | # 264 | INFO: Cluster_Ensembles: sgraph: calling gpmetis for graph partitioning. 265 | ****************************************************************************** 266 | METIS 5.0 Copyright 1998-13, Regents of the University of Minnesota 267 | (HEAD: , Built on: Sep 15 2017, 09:03:02) 268 | size of idx_t: 32bits, real_t: 32bits, idx_t *: 64bits 269 | 270 | Graph Information ----------------------------------------------------------- 271 | Name: ./wgraph_MCLA, #Vertices: 250, #Edges: 6125, #Parts: 5 272 | 273 | Options --------------------------------------------------------------------- 274 | ptype=kway, objtype=cut, ctype=shem, rtype=greedy, iptype=metisrb 275 | dbglvl=0, ufactor=1.030, no2hop=NO, minconn=NO, contig=NO, nooutput=NO 276 | seed=-1, niter=10, ncuts=1 277 | 278 | Direct k-way Partitioning --------------------------------------------------- 279 | - Edgecut: 139000, communication volume: 150. 280 | 281 | - Balance: 282 | constraint #0: 1.020 out of 0.037 283 | 284 | - Most overweight partition: 285 | pid: 0, actual: 652500, desired: 640000, ratio: 1.02. 286 | 287 | - Subdomain connectivity: max: 3, min: 0, avg: 1.60 288 | 289 | - The original graph had 5 connected components and the resulting 290 | partitioning after removing the cut edges has 8 components. 291 | Timing Information ---------------------------------------------------------- 292 | I/O: 0.001 sec 293 | Partitioning: 0.001 sec (METIS time) 294 | Reporting: 0.000 sec 295 | 296 | Memory Information ---------------------------------------------------------- 297 | Max memory used: 0.232 MB 298 | ****************************************************************************** 299 | INFO: Cluster_Ensembles: sgraph: (hyper)-graph partitioning completed; loading wgraph_MCLA.part.5 300 | # 301 | INFO: Cluster_Ensembles: MCLA: delivering 4 clusters. 302 | INFO: Cluster_Ensembles: MCLA: average posterior probability is 0.360500625232 303 | 304 | INFO: Cluster_Ensembles: cluster_ensembles: MCLA at 0.935544511804. 305 | ***** 306 | 307 | ECLAIR INFO 2017-09-15 09:09:35: done with computing the means and variances for the pair of distances along each minimum spanning tree between the groups of cells underlying each cluster of the ensemble clustering. The full probability distributions are available and stored in HDF5 format and available for further analysis. 308 | 309 | ECLAIR INFO 2017-09-15 09:09:35: the various runs of subsampling and clustering took 2.5613 seconds, ensemble clustering took 1.0971 seconds, while calculating the distribution of pairwise distances took 0.4174 seconds. The whole process lasted 4.5862 seconds 310 | 311 | ***************************************** 312 | ***************************************** -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/diffmap_components12.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/diffmap_components12.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/diffmap_components123.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/diffmap_components123.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/diffmap_components23.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/diffmap_components23.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/draw_graph_fr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/draw_graph_fr.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/monocle2_with_blobs.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/monocle2_with_blobs.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/monocle2_without_blobs.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/monocle2_without_blobs.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/figures/tsne.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/figures/tsne.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/stemID/README.md: -------------------------------------------------------------------------------- 1 | # Comparison with stemID 2 2 | 3 | Reproduce the comparison with stemID 2, the successor of stemID [(Grün *et al.*, Cell. Stem Cell, 4 | 2016)](https://doi.org/10.1016/j.stem.2016.05.010), 5 | by running [*RaceID3_StemID2_sample.R*](RaceID3_StemID2_sample.R). 6 | ``` 7 | time Rscript RaceID3_StemID2_sample.R 8 | ``` 9 | See [*logfile_X_krumsiek11_blobs_shifted.txt*](*logfile_X_krumsiek11_blobs_shifted.txt*) for the logging output that we produced. 10 | 11 | We acknowledge D. Grün for providing the code via Email; parameters were chosen 12 | by D. Grün so that stemID would produce sensible results. The code is an 13 | improved version of the original stemID, which is available from https://github.com/dgrun/StemID/. 14 | 15 | We note that we also tried running the code on the simpler data file *X_krumsiek11_shifted.csv* with the same parameters, but were not able to do so as this resulted in errors. -------------------------------------------------------------------------------- /minimal_examples/comparisons/stemID/RaceID3_StemID2_sample.R: -------------------------------------------------------------------------------- 1 | ## install required packages (only at first time) 2 | ## install.packages(c("tsne","pheatmap","MASS","cluster","mclust","flexmix","lattice","fpc","RColorBrewer","permute","amap","locfit","vegan","Rtsne","scran","randomForest","rgl")) 3 | ## source("https://bioconductor.org/biocLite.R") 4 | ## biocLite("scran") 5 | ## biocLite("DESeq2") 6 | ## biocLite("biomaRt") 7 | 8 | ## !!!! novel: 9 | ## normalization to minimum instead of median as alternative to downsampling, sum factor normalization (sfn), house keeping gene normalization (sfn), improved faster clustering (k-medoids), new possible metrics (e. g. logpearson), feature selection (FSelect), novel plotexptsne (highest expression on top), comptsne (fast,perplexity), cdiff with negative binomial, fast version of StemID: projback, lineagetree, comppvalue (fast), plot StemID score and lineage trees with linkscore threshold (scthr), filter out single genes and correlating groups of genes for clustering, random forest based (OOB) correction, PCA-based cell cycle correction, correcting batch effect by regression, FateID + Pseudotemporal ordering 10 | 11 | ## load class definition and functions 12 | 13 | source("RaceID3_StemID2_class.R") 14 | 15 | ## input data 16 | x <- read.csv("./X_krumsiek11_blobs_shifted.csv", sep=',', header=TRUE) 17 | n <- x$name 18 | x <- as.data.frame(t(x[,-1])) 19 | colnames(x) <- n 20 | prdata <- x 21 | 22 | ## RaceID3 23 | # initialize SCseq object with transcript counts 24 | sc <- SCseq(prdata) 25 | # filtering of expression data 26 | sc <- filterdata(sc, mintotal=100, minexpr=0, minnumber=1, maxexpr=Inf, downsample=FALSE, sfn=FALSE, hkn=FALSE, dsn=1, rseed=17000, CGenes=NULL, FGenes=NULL, ccor=.4) 27 | # k-medoids clustering 28 | sc <- clustexp(sc,clustnr=30,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids",FSelect=TRUE) 29 | # compute t-SNE map 30 | sc <- comptsne(sc,rseed=15555,sammonmap=FALSE,initial_cmd=TRUE,fast=TRUE,perplexity=30) 31 | # detect outliers and redefine clusters 32 | sc <- findoutliers(sc, outminc=5,outlg=2,probthr=1e-3,thr=2**-(1:40),outdistquant=.95) 33 | 34 | ## diagnostic plots 35 | # plot within-cluster dispersion as a function of the cluster number: only if sat == TRUE 36 | # plotsaturation(sc,disp=TRUE) 37 | # plot change of the within-cluster dispersion as a function of the cluster number: only # # if sat == TRUE 38 | # plotsaturation(sc) 39 | # silhouette of k-medoids clusters 40 | # plotsilhouette(sc) 41 | # Jaccard's similarity of k-medoids clusters 42 | # plotjaccard(sc) 43 | # barchart of outlier probabilities 44 | # plotoutlierprobs(sc) 45 | # regression of background model 46 | # plotbackground(sc) 47 | # dependence of outlier number on probability threshold (probthr) 48 | # plotsensitivity(sc) 49 | # heatmap of k-medoids cluster 50 | # clustheatmap(sc,final=FALSE,hmethod="single") 51 | # heatmap of final cluster 52 | # clustheatmap(sc,final=TRUE,hmethod="single") 53 | # highlight k-medoids clusters in t-SNE map 54 | # plottsne(sc,final=FALSE) 55 | # highlight final clusters in t-SNE map 56 | # plottsne(sc,final=TRUE) 57 | # highlight cell labels in t-SNE map 58 | # plotlabelstsne(sc,labels=sub("(\\_\\d+)","",names(sc@ndata))) 59 | # highlight groups of cells by symbols in t-SNE map 60 | # plotsymbolstsne(sc,types=sub("(\\_\\d+)$","", names(sc@ndata))) 61 | # highlight transcirpt counts of a set of genes in t-SNE map 62 | # plotexptsne(sc,"X1",logsc=TRUE) 63 | 64 | ## identification of marker genes 65 | # differentially regulated genes in each cluster compared to the full ensemble 66 | # cdiff <- clustdiffgenes(sc,pvalue=.01) 67 | 68 | ## write results to text files 69 | # final clusters 70 | # x <- data.frame(CELLID=names(sc@cpart),cluster=sc@cpart) 71 | # write.table(x[order(x$cluster,decreasing=FALSE),],"cell_clust.xls",row.names=FALSE,col.names=TRUE,sep="\t",quote=FALSE) 72 | 73 | # differentially expressed genes in cluster 74 | # for ( n in names(cdiff) ) write.table(data.frame(GENEID=rownames(cdiff[[n]]),cdiff[[n]]),paste(paste("cell_clust_diff_genes",sub("\\.","\\_",n),sep="_"),".xls",sep=""),row.names=FALSE,col.names=TRUE,sep="\t",quote=FALSE) 75 | 76 | # differentially expressed genes between two sets of clusters, e. g. cluster 3 and clusters 2,11 77 | # d <- diffgenes(sc,cl1=1,cl2=c(2,3),mincount=1) 78 | # plotdiffgenes(d,gene=names(d$z)[1]) 79 | 80 | # differentially expressed genes between two sets of cells, e. g. cluster 3 and clusters 2,11 by a different approach based on the overlap of transctipt count distributions 81 | # A <- names(sc@cpart)[sc@cpart == 1] 82 | # B <- names(sc@cpart)[sc@cpart %in% c(2,3)] 83 | # x <- diffexpnb(sc@ndata, A=A, B=B, method="pooled",norm=FALSE, DESeq=FALSE, vfit=sc@background$vfit, locreg=FALSE) 84 | # plotdiffgenesnb(x,pthr=.05,lthr=1,mthr=1,Aname="Cl.3",Bname="Cl.2,11",show_names=TRUE,padj=TRUE) 85 | 86 | # plot 3D tSNE 87 | # plot3dtsne(sc,perplexity=1,fast=TRUE,x=NULL,g=NULL,logsc=FALSE,final=TRUE,ret=FALSE,tp=1) 88 | 89 | ## StemID2 90 | 91 | # initialization 92 | ltr <- Ltree(sc) 93 | # computation of the entropy 94 | ltr <- compentropy(ltr) 95 | # computation of the projections for all cells 96 | ltr <- projcells(ltr,cthr=0,nmode=FALSE) 97 | # computation of the projections for all cells after randomization 98 | ltr <- projback(ltr,pdishuf=2000,nmode=FALSE,fast=FALSE,rseed=17000) 99 | # assembly of the lineage tree 100 | ltr <- lineagetree(ltr,pthr=0.05,nmode=FALSE,fast=FALSE) 101 | # determination of significant differentiation trajectories 102 | ltr <- comppvalue(ltr,pethr=0.05,nmode=FALSE,fast=FALSE) 103 | 104 | ## diagnostic plots 105 | # histogram of ratio between cell-to-cell distances in the embedded and the input space 106 | # plotdistanceratio(ltr) 107 | # t-SNE map of the clusters with more than cthr cells including a minimum spanning tree for the cluster medoids 108 | plotmap(ltr) 109 | # visualization of the projections in t-SNE space overlayed with a minimum spanning tree connecting the cluster medoids 110 | # plotmapprojections(ltr) 111 | # lineage tree showing the projections of all cells in t-SNE space 112 | plottree(ltr,showCells=TRUE,scthr=.2) 113 | # lineage tree without showing the projections of all cells 114 | # plottree(ltr,showCells=FALSE,scthr=.2) 115 | # heatmap of the enrichment p-values for all inter-cluster links 116 | # plotlinkpv(ltr) 117 | # heatmap of the link score for all inter-cluster links 118 | # plotlinkscore(ltr) 119 | # heatmap showing the fold enrichment (or depletion) for significantly enriched or depleted links 120 | # projenrichment(ltr) 121 | 122 | ## computing the StemID2 score 123 | # x <- compscore(ltr,nn=1,scthr=.2) 124 | #plotting the StemID2 score 125 | # plotscore(ltr,nn=1,scthr=.2) 126 | 127 | 128 | 129 | 130 | 131 | -------------------------------------------------------------------------------- /minimal_examples/comparisons/stemID/Rplots_X_krumsiek11_blobs_shifted.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/stemID/Rplots_X_krumsiek11_blobs_shifted.pdf -------------------------------------------------------------------------------- /minimal_examples/comparisons/stemID/Rplots_X_krumsiek11_blobs_shifted_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/stemID/Rplots_X_krumsiek11_blobs_shifted_1.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/stemID/Rplots_X_krumsiek11_blobs_shifted_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/minimal_examples/comparisons/stemID/Rplots_X_krumsiek11_blobs_shifted_2.png -------------------------------------------------------------------------------- /minimal_examples/comparisons/stemID/logfile_X_krumsiek11_blobs_shifted.txt: -------------------------------------------------------------------------------- 1 | (py35) Alexs-MBP:stemID alexwolf$ time Rscript RaceID3_StemID2_sample.R 2 | Loading required package: tsne 3 | Loading required package: pheatmap 4 | Loading required package: MASS 5 | Loading required package: cluster 6 | Loading required package: mclust 7 | Package 'mclust' version 5.3 8 | Type 'citation("mclust")' for citing this R package in publications. 9 | Loading required package: flexmix 10 | Loading required package: lattice 11 | Loading required package: fpc 12 | Loading required package: amap 13 | Loading required package: RColorBrewer 14 | Loading required package: locfit 15 | locfit 1.5-9.1 2013-03-22 16 | Loading required package: vegan 17 | Loading required package: permute 18 | This is vegan 2.4-4 19 | Loading required package: Rtsne 20 | Loading required package: scran 21 | Loading required package: BiocParallel 22 | Loading required package: methods 23 | Loading required package: scater 24 | Loading required package: Biobase 25 | Loading required package: BiocGenerics 26 | Loading required package: parallel 27 | 28 | Attaching package: ‘BiocGenerics’ 29 | 30 | The following objects are masked from ‘package:parallel’: 31 | 32 | clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, 33 | clusterExport, clusterMap, parApply, parCapply, parLapply, 34 | parLapplyLB, parRapply, parSapply, parSapplyLB 35 | 36 | The following object is masked from ‘package:flexmix’: 37 | 38 | unique 39 | 40 | The following objects are masked from ‘package:stats’: 41 | 42 | IQR, mad, xtabs 43 | 44 | The following objects are masked from ‘package:base’: 45 | 46 | anyDuplicated, append, as.data.frame, cbind, colnames, do.call, 47 | duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, 48 | is.unsorted, lapply, lengths, Map, mapply, match, mget, order, 49 | paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, 50 | Reduce, rownames, sapply, setdiff, sort, table, tapply, union, 51 | unique, unsplit, which, which.max, which.min 52 | 53 | Welcome to Bioconductor 54 | 55 | Vignettes contain introductory material; view with 56 | 'browseVignettes()'. 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available from [blood.stemcells.cam.ac.uk/single_cell_atlas](http://blood.stemcells.cam.ac.uk/single_cell_atlas). 6 | 7 | ### Contents 8 | 9 | * [*nestorowa16*](nestorowa16.ipynb) presents the main results obtained with graph abstraction 10 | * [*comparison_monocle2*](comparison_monocle2.ipynb) presents results obtained with Monocle 2 11 | * [*comparison_dpt*](comparison_dpt.ipynb) presents results obtained with diffusion pseudotime -------------------------------------------------------------------------------- /nestorowa16/figures/monocle2_maxcomps10.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theislab/graph_abstraction/0dd82eca99c8c8a5ebcc86d5bee3236a853c0431/nestorowa16/figures/monocle2_maxcomps10.png -------------------------------------------------------------------------------- /nestorowa16/figures/monocle2_maxcomps4.png: -------------------------------------------------------------------------------- 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Data is available from the original publication, or for convenience, within Scanpy as [`scanpy.api.datasets.paul15`](https://scanpy.readthedocs.io/en/latest/api/scanpy.api.datasets.paul15.html) or as an RData from https://github.com/theislab/scAnalysisTutorial. 6 | 7 | ### Contents 8 | 9 | * [*paul15*](paul15.ipynb) presents the main results obtained with graph abstraction 10 | * [*comparison_monocle2/...original*](comparison_monocle2/monocle2_original.ipynb) reproduces the published results of Monocle 2 [(Qiu *et al.*, Nat. Meth. 2017)](https://doi.org/10.1038/nmeth.4402) 11 | * [*comparison_monocle2/...alternative*](comparison_monocle2/monocle2_alternative.ipynb) produces results obtained with an alternative preprocessing that does not involve removing a lymphoid cluster from the data, but leaves everything else unchanged 12 | 13 | ### Summary 14 | 15 | While [*comparison_monocle2/...original*](comparison_monocle2/monocle2_original.ipynb) reproduces the published results of Monocle 2 [(Qiu *et al.*, Nat. Meth. 2017)](https://doi.org/10.1038/nmeth.4402) 16 | 17 | 18 | 19 | The preprocessing there involves a step that manually removes a cluster of lymphoid cells. But what if we had no labels for this cluster? We could then not remove it from the data and would hope to obtain a meaningful result also with this cluster present. 20 | 21 | In [*comparison_monocle2/...alternative*](comparison_monocle2/monocle2_alternative.ipynb), we run Monocle 2 with an alternative preprocessing that does not involve removing the lymphoid cluster from the data, but leaves everything else completely unchanged. The result is the following. 22 | 23 | 24 | 25 | Evidently, the result is entirely different from the previous result. For example, the megaryocyte (MK) state is very distant from the progenitor (MP/EP) cells, even though it is expexted to branch off very early. 26 | 27 | -------------------------------------------------------------------------------- /paul15/comparison_monocle2/data/MAP.csv: -------------------------------------------------------------------------------- 1 | W31105,7 2 | W31106,15 3 | W31107,3 4 | W31108,15 5 | W31109,3 6 | W31110,15 7 | W31111,4 8 | W31113,2 9 | W31114,3 10 | W31115,2 11 | W31116,2 12 | W31117,17 13 | W31118,14 14 | W31119,13 15 | W31120,3 16 | W31121,7 17 | W31122,3 18 | W31123,3 19 | W31124,2 20 | W31125,8 21 | W31126,3 22 | W31127,9 23 | W31128,8 24 | W31129,10 25 | W31130,15 26 | W31131,16 27 | W31132,4 28 | W31133,13 29 | W31134,17 30 | W31136,3 31 | W31137,2 32 | W31138,3 33 | W31139,5 34 | W31140,3 35 | W31141,13 36 | W31142,1 37 | W31143,3 38 | W31144,6 39 | W31145,15 40 | W31146,3 41 | W31147,3 42 | W31148,14 43 | W31149,19 44 | W31150,12 45 | W31151,3 46 | 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| W37480,14 1257 | W37481,13 1258 | W37482,5 1259 | W37483,10 1260 | W37484,7 1261 | W37485,2 1262 | W37486,2 1263 | W37487,2 1264 | W37488,14 1265 | W37489,7 1266 | W37490,8 1267 | W37491,14 1268 | W37492,2 1269 | W37493,2 1270 | W37494,14 1271 | W37496,13 1272 | W37497,7 1273 | W37498,4 1274 | W37499,13 1275 | W37500,13 1276 | W37501,5 1277 | W37502,2 1278 | W37503,18 1279 | W37504,15 1280 | W37505,5 1281 | W37506,7 1282 | W37507,2 1283 | W37508,13 1284 | W37509,14 1285 | W37510,6 1286 | W37511,5 1287 | W37512,4 1288 | W37514,13 1289 | W37515,7 1290 | W37516,5 1291 | W37517,5 1292 | W37518,10 1293 | W37519,16 1294 | W37520,2 1295 | W37521,14 1296 | W37522,3 1297 | W37523,2 1298 | W37524,10 1299 | W37525,16 1300 | W37526,8 1301 | W37527,6 1302 | W37528,15 1303 | W37529,5 1304 | W37530,10 1305 | W37531,7 1306 | W37532,14 1307 | W37533,19 1308 | W37534,2 1309 | W37535,15 1310 | W37537,3 1311 | W37538,6 1312 | W37539,1 1313 | W37540,2 1314 | W37541,3 1315 | W37542,4 1316 | W37545,6 1317 | W37546,13 1318 | W37548,3 1319 | W37549,5 1320 | W37551,14 1321 | W37552,13 1322 | W37553,13 1323 | W37554,3 1324 | W37555,4 1325 | W37557,7 1326 | W37558,10 1327 | W37559,11 1328 | W37561,3 1329 | W37562,13 1330 | W37563,2 1331 | W37564,13 1332 | W37565,2 1333 | W37566,4 1334 | W37567,10 1335 | W37568,16 1336 | W37569,14 1337 | W37570,16 1338 | W37572,10 1339 | W37573,2 1340 | W37574,2 1341 | W37575,6 1342 | W37577,3 1343 | W37578,14 1344 | W37579,14 1345 | W37580,15 1346 | W37581,10 1347 | W37582,13 1348 | W37583,3 1349 | W37584,7 1350 | W37585,13 1351 | W37586,2 1352 | W37587,14 1353 | W37588,14 1354 | W37589,15 1355 | W37591,4 1356 | W37593,15 1357 | W37594,14 1358 | W37595,10 1359 | W37596,13 1360 | W37597,16 1361 | W37599,8 1362 | W37601,13 1363 | W37602,5 1364 | W37603,6 1365 | W37604,14 1366 | W37605,2 1367 | W37606,6 1368 | W37607,3 1369 | W37608,14 1370 | W37609,7 1371 | W37610,7 1372 | W37611,14 1373 | W37612,5 1374 | W37613,13 1375 | W37614,2 1376 | W37615,16 1377 | 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| W37709,7 1439 | W37710,2 1440 | W37711,16 1441 | W37712,10 1442 | W37713,6 1443 | W37714,2 1444 | W37715,15 1445 | W37716,14 1446 | W37717,15 1447 | W37718,3 1448 | W37719,12 1449 | W37720,2 1450 | W37721,13 1451 | W37722,13 1452 | W37723,7 1453 | W37724,6 1454 | W37725,5 1455 | W37726,2 1456 | W37727,19 1457 | W37728,14 1458 | W37729,12 1459 | W37730,2 1460 | W37731,16 1461 | W37737,4 1462 | W37738,6 1463 | W37739,13 1464 | W37745,4 1465 | W37746,2 1466 | W37747,4 1467 | W37748,15 1468 | W37753,1 1469 | W37754,15 1470 | W37756,13 1471 | W37758,15 1472 | W37759,3 1473 | W37761,6 1474 | W37762,12 1475 | W37763,12 1476 | W37764,13 1477 | W37765,5 1478 | W37766,16 1479 | W37769,6 1480 | W37770,13 1481 | W37771,2 1482 | W37772,14 1483 | W37776,3 1484 | W37777,14 1485 | W37778,8 1486 | W37779,2 1487 | W37780,9 1488 | W37781,4 1489 | W37782,3 1490 | W37786,3 1491 | W37788,14 1492 | W37790,2 1493 | W37793,10 1494 | W37794,16 1495 | W37795,6 1496 | W37796,14 1497 | W37797,16 1498 | W37798,5 1499 | W37799,10 1500 | W37800,16 1501 | W37801,12 1502 | W37802,11 1503 | W37803,15 1504 | W37804,2 1505 | W37805,13 1506 | W37806,7 1507 | W37807,13 1508 | W37809,13 1509 | W37811,2 1510 | W37812,6 1511 | W37813,14 1512 | W37817,5 1513 | W37818,16 1514 | W37819,5 1515 | W37820,6 1516 | W37821,18 1517 | W37822,14 1518 | W37825,14 1519 | W37826,13 1520 | W37828,15 1521 | W37830,2 1522 | W37833,2 1523 | W37835,13 1524 | W37838,3 1525 | W37841,2 1526 | W37842,15 1527 | W37843,12 1528 | W37844,7 1529 | W37847,3 1530 | W37848,4 1531 | W37849,15 1532 | W37850,14 1533 | W37851,4 1534 | W37852,4 1535 | W37853,13 1536 | W37854,9 1537 | W37857,12 1538 | W37858,2 1539 | W37859,4 1540 | W37860,9 1541 | W37861,16 1542 | W37863,16 1543 | W37864,2 1544 | W37865,2 1545 | W37866,16 1546 | W37867,7 1547 | W37868,14 1548 | W37870,14 1549 | W37872,14 1550 | W37873,5 1551 | W37874,5 1552 | W37875,3 1553 | W37876,3 1554 | W37878,2 1555 | W37879,18 1556 | W37880,14 1557 | W37881,3 1558 | W37882,14 1559 | W37883,14 1560 | W37885,12 1561 | W37886,14 1562 | W37887,4 1563 | W37888,3 1564 | W37889,14 1565 | W37890,7 1566 | W37891,5 1567 | W37892,12 1568 | W37893,2 1569 | W37894,15 1570 | W37895,14 1571 | W37896,3 1572 | W37898,14 1573 | W37899,2 1574 | W37900,3 1575 | W37901,2 1576 | W37902,16 1577 | W37903,7 1578 | W37904,2 1579 | W37906,10 1580 | W37907,7 1581 | W37908,5 1582 | W37909,5 1583 | W37910,13 1584 | W37913,6 1585 | W37914,7 1586 | W37915,3 1587 | W37916,10 1588 | W37917,5 1589 | W37918,5 1590 | W37919,1 1591 | W37921,13 1592 | W37922,15 1593 | W37923,3 1594 | W37924,6 1595 | W37929,14 1596 | W37930,11 1597 | W37935,3 1598 | W37937,3 1599 | W37938,3 1600 | W37939,16 1601 | W37940,3 1602 | W37941,9 1603 | W37942,14 1604 | W37943,12 1605 | W37945,9 1606 | W37946,14 1607 | W37948,14 1608 | W37951,8 1609 | W37952,5 1610 | W37953,15 1611 | W37954,15 1612 | W37955,3 1613 | W37956,16 1614 | W37957,15 1615 | W37959,4 1616 | W37960,3 1617 | W37961,8 1618 | W37962,15 1619 | W37963,14 1620 | W37964,5 1621 | W37965,3 1622 | W37967,4 1623 | W37969,2 1624 | W37970,8 1625 | W37971,7 1626 | W37972,10 1627 | W37974,15 1628 | W37975,14 1629 | W37977,13 1630 | W37978,3 1631 | W37979,7 1632 | W37980,2 1633 | W37982,13 1634 | W37983,2 1635 | W37984,10 1636 | W37985,2 1637 | W37986,2 1638 | W37988,2 1639 | W37989,12 1640 | W37990,14 1641 | W37991,1 1642 | W37992,14 1643 | W37993,14 1644 | W37994,2 1645 | W37995,14 1646 | W37996,16 1647 | W37997,14 1648 | W38000,8 1649 | W38001,16 1650 | W38002,16 1651 | W38003,2 1652 | W38004,8 1653 | W38005,5 1654 | W38006,6 1655 | W38007,19 1656 | W38008,15 1657 | W38009,13 1658 | W38010,5 1659 | W38011,14 1660 | W38012,5 1661 | W38013,6 1662 | W38014,16 1663 | W38017,3 1664 | W38018,15 1665 | W38019,7 1666 | W38020,7 1667 | W38021,10 1668 | W38022,14 1669 | W38023,3 1670 | W38025,4 1671 | W38026,13 1672 | W38027,15 1673 | W38028,11 1674 | W38030,17 1675 | W38031,4 1676 | W38032,6 1677 | W38033,3 1678 | W38034,3 1679 | W38035,7 1680 | W38036,8 1681 | W38037,6 1682 | W38038,11 1683 | W38039,16 1684 | W38040,3 1685 | W38041,14 1686 | W38042,4 1687 | W38043,4 1688 | W38044,11 1689 | W38045,3 1690 | W38046,14 1691 | W38047,15 1692 | W38048,3 1693 | W38049,10 1694 | W38050,6 1695 | W38051,2 1696 | W38052,16 1697 | W38053,15 1698 | W38054,7 1699 | W38055,3 1700 | W38056,10 1701 | W38057,3 1702 | W38058,14 1703 | W38059,7 1704 | W38060,6 1705 | W38061,10 1706 | W38062,10 1707 | W38063,3 1708 | W38064,7 1709 | W38065,2 1710 | W38066,4 1711 | W38067,7 1712 | W38068,13 1713 | W38069,6 1714 | W38070,15 1715 | W38071,8 1716 | W38072,10 1717 | W38073,4 1718 | W38074,4 1719 | W38075,3 1720 | W38076,7 1721 | W38077,2 1722 | W38078,16 1723 | W38079,3 1724 | W38080,3 1725 | W38081,6 1726 | W38082,6 1727 | W38083,7 1728 | W38084,4 1729 | W38085,16 1730 | W38086,2 1731 | W38087,13 1732 | W38088,10 1733 | W38090,3 1734 | W38091,3 1735 | W38092,10 1736 | W38093,6 1737 | W38094,14 1738 | W38095,15 1739 | W38096,7 1740 | W38097,13 1741 | W38098,14 1742 | W38100,14 1743 | W38101,14 1744 | W38102,2 1745 | W38103,16 1746 | W38104,7 1747 | W38105,14 1748 | W38106,10 1749 | W38107,14 1750 | W38108,6 1751 | W38109,14 1752 | W38110,16 1753 | W38111,2 1754 | W38112,10 1755 | W38113,16 1756 | W38114,14 1757 | W38115,14 1758 | W38116,5 1759 | W38117,12 1760 | W38118,4 1761 | W38119,3 1762 | W38121,13 1763 | W38123,2 1764 | W38124,4 1765 | W38125,4 1766 | W38126,3 1767 | W38127,3 1768 | W38128,13 1769 | W38129,12 1770 | W38130,14 1771 | W38131,16 1772 | W38132,3 1773 | W38133,5 1774 | W38134,6 1775 | W38135,7 1776 | W38136,4 1777 | W38137,4 1778 | W38138,5 1779 | W38140,14 1780 | W38141,14 1781 | W38142,2 1782 | W38143,12 1783 | W38144,3 1784 | W38145,5 1785 | W38146,2 1786 | W38147,2 1787 | W38148,16 1788 | W38149,7 1789 | W38150,5 1790 | W38151,2 1791 | W38152,7 1792 | W38153,3 1793 | W38154,4 1794 | W38155,2 1795 | W38156,5 1796 | W38157,10 1797 | W38158,1 1798 | W38159,10 1799 | W38160,11 1800 | W38161,16 1801 | W38162,2 1802 | W38163,2 1803 | W38164,2 1804 | W38165,5 1805 | W38166,5 1806 | W38167,14 1807 | W38168,14 1808 | W38169,16 1809 | W38170,10 1810 | W38171,14 1811 | W38172,14 1812 | W38173,4 1813 | W38175,2 1814 | W38176,6 1815 | W38177,2 1816 | W38178,19 1817 | W38180,16 1818 | W38181,1 1819 | W38182,13 1820 | W38183,3 1821 | W38184,15 1822 | W38185,3 1823 | W38186,5 1824 | W38187,2 1825 | W38188,2 1826 | W38189,16 1827 | W38190,4 1828 | W38191,12 1829 | W38192,3 1830 | W38193,12 1831 | W38194,14 1832 | W38195,15 1833 | W38196,14 1834 | W38197,10 1835 | W38198,6 1836 | W38199,12 1837 | W38200,2 1838 | W38201,3 1839 | W38202,4 1840 | W38203,7 1841 | W38204,4 1842 | W38205,15 1843 | W38206,2 1844 | W38207,7 1845 | W38208,14 1846 | W38209,3 1847 | W38210,13 1848 | W38211,3 1849 | W38212,15 1850 | W38213,15 1851 | W38214,13 1852 | W38215,14 1853 | W38217,4 1854 | W38218,14 1855 | W38219,6 1856 | W38220,3 1857 | W38221,14 1858 | W38222,8 1859 | W38223,8 1860 | W38224,7 1861 | W38225,15 1862 | W38226,15 1863 | W38227,13 1864 | W38228,16 1865 | W38229,6 1866 | W38230,3 1867 | W38231,2 1868 | W38232,13 1869 | W38233,3 1870 | W38234,3 1871 | W38235,13 1872 | W38236,11 1873 | W38237,10 1874 | W38238,4 1875 | W38239,2 1876 | W38240,7 1877 | W38241,7 1878 | W38242,6 1879 | W38243,3 1880 | W38244,16 1881 | W38245,7 1882 | W38246,14 1883 | W38247,3 1884 | W38248,6 1885 | W38249,9 1886 | W38250,12 1887 | W38251,3 1888 | W38252,14 1889 | W38253,10 1890 | W38254,4 1891 | W38255,7 1892 | W38256,6 1893 | W38257,14 1894 | W38258,14 1895 | W38259,4 1896 | W38260,3 1897 | W38261,3 1898 | W38262,2 1899 | W38263,16 1900 | W38264,3 1901 | W38266,7 1902 | W38267,18 1903 | W38268,6 1904 | W38269,13 1905 | W38270,4 1906 | W38271,10 1907 | W38272,6 1908 | W38273,2 1909 | W38274,12 1910 | W38275,1 1911 | W38276,4 1912 | W38277,13 1913 | W38278,14 1914 | W38279,14 1915 | W38280,13 1916 | W38282,3 1917 | W38283,3 1918 | W38284,16 1919 | W38285,15 1920 | W38286,14 1921 | W38287,3 1922 | W38288,6 1923 | W38289,2 1924 | W38290,10 1925 | W38291,14 1926 | W38292,5 1927 | W38293,4 1928 | W38294,15 1929 | W38295,13 1930 | W38296,15 1931 | W38297,13 1932 | W38298,14 1933 | W38299,12 1934 | W38300,16 1935 | W38301,13 1936 | W38302,6 1937 | W38303,15 1938 | W38304,4 1939 | W38305,14 1940 | W38306,13 1941 | W38307,15 1942 | W38308,3 1943 | W38309,10 1944 | W38310,13 1945 | W38311,4 1946 | W38313,8 1947 | W38314,3 1948 | W38315,14 1949 | W38316,15 1950 | W38318,16 1951 | W38319,15 1952 | W38320,9 1953 | W38321,16 1954 | W38322,12 1955 | W38323,10 1956 | W38324,14 1957 | W38326,14 1958 | W38327,14 1959 | W38328,7 1960 | W38329,15 1961 | W38330,17 1962 | W38331,2 1963 | W38332,14 1964 | W38333,14 1965 | W38334,4 1966 | W38335,6 1967 | W38336,15 1968 | W38337,2 1969 | W38338,2 1970 | W38339,5 1971 | W38340,2 1972 | W38341,2 1973 | W38342,14 1974 | W38343,2 1975 | W38344,14 1976 | W38345,15 1977 | W38346,5 1978 | W38347,6 1979 | W38348,13 1980 | W38349,6 1981 | W38350,13 1982 | W38351,5 1983 | W38352,15 1984 | W38353,3 1985 | W38354,10 1986 | W38355,2 1987 | W38356,14 1988 | W38357,2 1989 | W38358,14 1990 | W38359,2 1991 | W38360,2 1992 | W38361,2 1993 | W38362,5 1994 | W38363,14 1995 | W38364,1 1996 | W38365,5 1997 | W38366,4 1998 | W38367,2 1999 | W38368,10 2000 | W38369,2 2001 | W38370,2 2002 | W38372,7 2003 | W38373,2 2004 | W38374,7 2005 | W38375,1 2006 | W38376,7 2007 | W38377,9 2008 | W38379,2 2009 | W38380,17 2010 | W38381,14 2011 | W38382,14 2012 | W38383,13 2013 | W38384,7 2014 | W38385,16 2015 | W38386,2 2016 | W38387,2 2017 | W38388,7 2018 | W38389,6 2019 | W38390,14 2020 | W38391,4 2021 | W38392,6 2022 | W38393,2 2023 | W38394,4 2024 | W38395,1 2025 | W38396,5 2026 | W38397,19 2027 | W38398,8 2028 | W38399,4 2029 | W38400,6 2030 | W38401,13 2031 | W38402,6 2032 | W38403,13 2033 | W38404,7 2034 | W38405,3 2035 | W38406,3 2036 | W38407,13 2037 | W38409,3 2038 | W38410,15 2039 | W38411,14 2040 | W38412,6 2041 | W38413,10 2042 | W38414,16 2043 | W38415,4 2044 | W38416,14 2045 | W38417,14 2046 | W38418,10 2047 | W38419,3 2048 | W38420,10 2049 | W38421,14 2050 | W38422,3 2051 | W38423,4 2052 | W38424,19 2053 | W38425,14 2054 | W38426,3 2055 | W38427,15 2056 | W38428,15 2057 | W38429,10 2058 | W38430,15 2059 | W38431,15 2060 | W38433,14 2061 | W38434,13 2062 | W38435,16 2063 | W38436,14 2064 | W38437,4 2065 | W38438,14 2066 | W38439,9 2067 | W38440,3 2068 | W38441,14 2069 | W38443,3 2070 | W38444,10 2071 | W38445,16 2072 | W38446,16 2073 | W38447,13 2074 | W38448,1 2075 | W38449,6 2076 | W38450,2 2077 | W38451,14 2078 | W38452,2 2079 | W38453,14 2080 | W38454,5 2081 | W38455,2 2082 | W38456,9 2083 | W38457,13 2084 | W38458,2 2085 | W38459,3 2086 | W38460,16 2087 | W38461,13 2088 | W38462,14 2089 | W38463,4 2090 | W38464,3 2091 | W38465,6 2092 | W38466,7 2093 | W38467,10 2094 | W38468,4 2095 | W38469,2 2096 | W38470,16 2097 | W38471,12 2098 | W38472,19 2099 | W38474,7 2100 | W38475,13 2101 | W38477,4 2102 | W38478,7 2103 | W38480,4 2104 | W38481,12 2105 | W38482,5 2106 | W38483,2 2107 | W38484,2 2108 | W38485,9 2109 | W38486,1 2110 | W38487,11 2111 | W38488,3 2112 | W38489,14 2113 | W38491,16 2114 | W38492,2 2115 | W38493,5 2116 | W38494,14 2117 | W38495,15 2118 | W38496,9 2119 | W38497,9 2120 | W38498,14 2121 | W38500,2 2122 | W38501,4 2123 | W38502,5 2124 | W38503,4 2125 | W38505,3 2126 | W38506,16 2127 | W38507,2 2128 | W38509,16 2129 | W38510,2 2130 | W38512,13 2131 | W38515,2 2132 | W38516,6 2133 | W38517,6 2134 | W38519,14 2135 | W38521,2 2136 | W38522,2 2137 | W38523,8 2138 | W38524,8 2139 | W38525,14 2140 | W38526,14 2141 | W38527,1 2142 | W38528,4 2143 | W38529,7 2144 | W38530,16 2145 | W38531,16 2146 | W38532,14 2147 | W38533,3 2148 | W38534,2 2149 | W38535,16 2150 | W38536,17 2151 | W38537,6 2152 | W38538,2 2153 | W38539,14 2154 | W38540,16 2155 | W38541,7 2156 | W38543,2 2157 | W38544,3 2158 | W38545,2 2159 | W38546,6 2160 | W38547,6 2161 | W38548,6 2162 | W38549,10 2163 | W38550,12 2164 | W38551,12 2165 | W38552,4 2166 | W38553,2 2167 | W38554,4 2168 | W38555,2 2169 | W38556,14 2170 | W38558,3 2171 | W38559,6 2172 | W38560,3 2173 | W38561,10 2174 | W38563,2 2175 | W38564,14 2176 | W38565,16 2177 | W38567,18 2178 | W38568,13 2179 | W38569,6 2180 | W38570,14 2181 | W38571,13 2182 | W38572,2 2183 | W38573,2 2184 | W38575,5 2185 | W38576,10 2186 | W38577,5 2187 | W38578,14 2188 | W38579,13 2189 | W38580,14 2190 | W38581,16 2191 | W38582,2 2192 | W38583,10 2193 | W38584,4 2194 | W38585,16 2195 | W38586,6 2196 | W38587,15 2197 | W38588,14 2198 | W38589,12 2199 | W38590,7 2200 | W38591,10 2201 | W38592,15 2202 | W38593,3 2203 | W38594,15 2204 | W38595,3 2205 | W38596,9 2206 | W38597,2 2207 | W38598,7 2208 | W38599,3 2209 | W38601,13 2210 | W38602,16 2211 | W38603,13 2212 | W38604,2 2213 | W38605,15 2214 | W38606,7 2215 | W38607,6 2216 | W38608,5 2217 | W38609,13 2218 | W38610,10 2219 | W38611,9 2220 | W38612,13 2221 | W38613,13 2222 | W38614,12 2223 | W38615,5 2224 | W38616,13 2225 | W38617,15 2226 | W38618,14 2227 | W38619,10 2228 | W38620,15 2229 | W38622,4 2230 | W38623,5 2231 | W38624,8 2232 | W38625,13 2233 | W38626,7 2234 | W38627,14 2235 | W38628,14 2236 | W38629,2 2237 | W38630,6 2238 | W38631,2 2239 | W38632,13 2240 | W38633,7 2241 | W38634,16 2242 | W38636,14 2243 | W38637,3 2244 | W38638,16 2245 | W38639,19 2246 | W38640,10 2247 | W38643,12 2248 | W38644,10 2249 | W38645,13 2250 | W38646,1 2251 | W38647,16 2252 | W38648,6 2253 | W38649,14 2254 | W38650,2 2255 | W38651,14 2256 | W38652,10 2257 | W38653,14 2258 | W38654,16 2259 | W38655,9 2260 | W38656,3 2261 | W38657,3 2262 | W38659,14 2263 | W38660,2 2264 | W38662,12 2265 | W38663,9 2266 | W38664,1 2267 | W38666,3 2268 | W38668,5 2269 | W38669,2 2270 | W38670,8 2271 | W38672,2 2272 | W38673,16 2273 | W38674,5 2274 | W38675,10 2275 | W38676,13 2276 | W38677,16 2277 | W38678,14 2278 | W38679,2 2279 | W38680,5 2280 | W38681,15 2281 | W38682,7 2282 | W38683,15 2283 | W38684,5 2284 | W38685,6 2285 | W38686,5 2286 | W38687,2 2287 | W38688,3 2288 | W38689,15 2289 | W38690,7 2290 | W38691,3 2291 | W38692,15 2292 | W38693,16 2293 | W38694,6 2294 | W38695,7 2295 | W38697,6 2296 | W38698,6 2297 | W38699,10 2298 | W38700,4 2299 | W38701,13 2300 | W38702,14 2301 | W38703,13 2302 | W38704,3 2303 | W38705,6 2304 | W38707,8 2305 | W38709,14 2306 | W38710,2 2307 | W38711,9 2308 | W38712,6 2309 | W38713,15 2310 | W38714,13 2311 | W38715,3 2312 | W38716,16 2313 | W38717,2 2314 | W38718,9 2315 | W38719,14 2316 | W38720,3 2317 | W38721,14 2318 | W38722,14 2319 | W38723,16 2320 | W38724,14 2321 | W38726,9 2322 | W38727,3 2323 | W38728,13 2324 | W38729,10 2325 | W38730,14 2326 | W38732,3 2327 | W38733,6 2328 | W38734,14 2329 | W38735,4 2330 | W38736,3 2331 | W38737,2 2332 | W38738,2 2333 | W38739,14 2334 | W38740,2 2335 | W38741,8 2336 | W38742,10 2337 | W38744,10 2338 | W38745,14 2339 | W38747,10 2340 | W38748,16 2341 | W38750,10 2342 | W38751,10 2343 | W38752,13 2344 | W38753,16 2345 | W38754,2 2346 | W38755,16 2347 | W38756,2 2348 | W38757,14 2349 | W38758,11 2350 | W38759,14 2351 | W38760,11 2352 | W38761,14 2353 | W38765,13 2354 | W38767,2 2355 | W38768,2 2356 | W38769,16 2357 | W38770,2 2358 | W38771,2 2359 | W38772,14 2360 | W38773,5 2361 | W38774,2 2362 | W38775,7 2363 | W38776,9 2364 | W38777,2 2365 | W38778,10 2366 | W38779,7 2367 | W38781,6 2368 | W38782,15 2369 | W38783,5 2370 | W38784,5 2371 | W38785,15 2372 | W38786,14 2373 | W38788,8 2374 | W38789,14 2375 | W38790,15 2376 | W38791,4 2377 | W38793,14 2378 | W38794,13 2379 | W38795,6 2380 | W38796,4 2381 | W38797,13 2382 | W38798,4 2383 | W38799,9 2384 | W38800,3 2385 | W38801,8 2386 | W38802,8 2387 | W38803,15 2388 | W38804,2 2389 | W38805,12 2390 | W38806,3 2391 | W38807,15 2392 | W38808,15 2393 | W38809,9 2394 | W38810,3 2395 | W38811,10 2396 | W38812,15 2397 | W38813,16 2398 | W38814,4 2399 | W38815,3 2400 | W38816,14 2401 | W38818,3 2402 | W38819,16 2403 | W38820,6 2404 | W38821,5 2405 | W38822,13 2406 | W38823,13 2407 | W38824,11 2408 | W38825,13 2409 | W38826,8 2410 | W38827,2 2411 | W38828,13 2412 | W38829,13 2413 | W38830,8 2414 | W38831,8 2415 | W38832,15 2416 | W38833,14 2417 | W38834,14 2418 | W38835,19 2419 | W38836,6 2420 | W38837,5 2421 | W38838,15 2422 | W38839,7 2423 | W38840,15 2424 | W38841,15 2425 | W38843,6 2426 | W38845,12 2427 | W38846,2 2428 | W38847,13 2429 | W38848,3 2430 | W38849,4 2431 | W38850,14 2432 | W38851,10 2433 | W38852,16 2434 | W38853,15 2435 | W38854,6 2436 | W38855,13 2437 | W38856,19 2438 | W38858,15 2439 | W38859,6 2440 | W38860,2 2441 | W38861,15 2442 | W38862,3 2443 | W38863,13 2444 | W38864,6 2445 | W38865,13 2446 | W38866,9 2447 | W38867,10 2448 | W38868,10 2449 | W38869,14 2450 | W38870,2 2451 | W38871,6 2452 | W38872,10 2453 | W38873,7 2454 | W38874,6 2455 | W38875,14 2456 | W38876,2 2457 | W38877,2 2458 | W38878,7 2459 | W38879,5 2460 | W38880,13 2461 | W38881,13 2462 | W38882,14 2463 | W38883,3 2464 | W38884,13 2465 | W38886,4 2466 | W38887,3 2467 | W38889,2 2468 | W38890,15 2469 | W38891,7 2470 | W38892,1 2471 | W38893,15 2472 | W38894,2 2473 | W38895,13 2474 | W38896,8 2475 | W38897,9 2476 | W38898,19 2477 | W38899,11 2478 | W38900,8 2479 | W38901,17 2480 | W38902,13 2481 | W38903,13 2482 | W38904,13 2483 | W38905,3 2484 | W38906,12 2485 | W38907,16 2486 | W38908,10 2487 | W38909,15 2488 | W38910,7 2489 | W38911,13 2490 | W38912,13 2491 | W38913,14 2492 | W38914,16 2493 | W38915,3 2494 | W38916,7 2495 | W38917,13 2496 | W38918,13 2497 | W38919,3 2498 | W38920,1 2499 | W38921,14 2500 | W38922,15 2501 | W38923,10 2502 | W38924,15 2503 | W38925,4 2504 | W38926,9 2505 | W38927,14 2506 | W38928,9 2507 | W38929,13 2508 | W38930,10 2509 | W38931,9 2510 | W38932,5 2511 | W38933,2 2512 | W38934,7 2513 | W38935,13 2514 | W38937,6 2515 | W38938,7 2516 | W38939,10 2517 | W38940,7 2518 | W38941,10 2519 | W38942,3 2520 | W38943,5 2521 | W38944,10 2522 | W38945,6 2523 | W38946,19 2524 | W38947,8 2525 | W38948,4 2526 | W38949,14 2527 | W38950,16 2528 | W38951,14 2529 | W38952,10 2530 | W38953,10 2531 | W38954,13 2532 | W38955,7 2533 | W38956,15 2534 | W38957,10 2535 | W38958,15 2536 | W38959,15 2537 | W38960,14 2538 | W38961,5 2539 | W38962,13 2540 | W38963,9 2541 | W38964,7 2542 | W38965,14 2543 | W38966,6 2544 | W38967,2 2545 | W38968,8 2546 | W38969,7 2547 | W38970,5 2548 | W38971,7 2549 | W38972,13 2550 | W38973,7 2551 | W38974,5 2552 | W38975,8 2553 | W38976,2 2554 | W38977,2 2555 | W38978,4 2556 | W38979,14 2557 | W38980,15 2558 | W38981,13 2559 | W38982,3 2560 | W38983,2 2561 | W38985,5 2562 | W38987,13 2563 | W38989,3 2564 | W38990,6 2565 | W38991,8 2566 | W38992,6 2567 | W38993,15 2568 | W38994,2 2569 | W38996,5 2570 | W38997,6 2571 | W38998,9 2572 | W38999,8 2573 | W39000,15 2574 | W39001,13 2575 | W39002,10 2576 | W39003,15 2577 | W39004,15 2578 | W39005,2 2579 | W39006,13 2580 | W39007,14 2581 | W39008,9 2582 | W39009,2 2583 | W39010,3 2584 | W39011,17 2585 | W39012,16 2586 | W39013,13 2587 | W39015,6 2588 | W39017,13 2589 | W39018,10 2590 | W39019,13 2591 | W39020,13 2592 | W39021,2 2593 | W39022,6 2594 | W39023,15 2595 | W39024,14 2596 | W39025,14 2597 | W39026,13 2598 | W39027,14 2599 | W39028,6 2600 | W39029,13 2601 | W39030,17 2602 | W39031,2 2603 | W39032,6 2604 | W39033,7 2605 | W39034,10 2606 | W39035,10 2607 | W39036,6 2608 | W39037,15 2609 | W39038,15 2610 | W39039,6 2611 | W39040,4 2612 | W39041,8 2613 | W39042,6 2614 | W39043,13 2615 | W39044,13 2616 | W39045,5 2617 | W39047,10 2618 | W39048,10 2619 | W39050,10 2620 | W39051,2 2621 | W39052,11 2622 | W39053,16 2623 | W39054,14 2624 | W39055,5 2625 | W39056,10 2626 | W39057,6 2627 | W39058,2 2628 | W39059,10 2629 | W39060,15 2630 | W39061,16 2631 | W39062,6 2632 | W39063,19 2633 | W39064,10 2634 | W39065,15 2635 | W39066,13 2636 | W39067,5 2637 | W39068,7 2638 | W39069,4 2639 | W39070,7 2640 | W39071,3 2641 | W39072,19 2642 | W39073,13 2643 | W39074,13 2644 | W39075,13 2645 | W39076,3 2646 | W39077,1 2647 | W39078,15 2648 | W39079,8 2649 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