Merge branch 'master' of gitlab.fbk.eu:MPBA/INF

Makefile

-#!/usr/bin/make -f
-# Makefile for running the INF pipeline
-# Author: Marco Chierici <chierici@fbk.eu>
-# Date: 2017-05-12
-#
-.PHONY: init
-
-SHELL := /bin/bash
-
-# input variables
-# shown are examples, override on command line
-FILE ?= data/AG1-G_MAV-G_498_LIT_ALL_tr.txt
-LABEL ?= data/label_498_ALL-EFS_tr.lab
-DATA1 ?= data/AG1-G_498_LIT_ALL_tr.txt
-DATA2 ?= data/MAV-G_498_LIT_ALL_tr.txt
-ENDPOINT ?= ALL-EFS
-# added MF 20170710
-THREADS ?= 4
-OUTBASE ?= /path/to/out_tmp
-
-BINDIR := .
-OUTDIR := $(OUTBASE)/$(ENDPOINT)
-
-# derived variables
-OUTPREFIX = $(notdir $(basename $(FILE)))
-LEVEL1 = $(word 1,$(subst _, ,$(OUTPREFIX)))
-LEVEL2 = $(word 2,$(subst _, ,$(OUTPREFIX)))
-
-init:
-	@mkdir -p $(OUTDIR)/rSNFi
-	@mkdir -p $(OUTDIR)/rSNF
-
-all: init $(OUTDIR)/rSNFi/$(OUTPREFIX)_MCC_scores.txt $(OUTDIR)/rSNF/$(OUTPREFIX)_MCC_scores.txt $(OUTDIR)/juxt/$(OUTPREFIX)_MCC_scores.txt
-
-
-$(OUTDIR)/juxt/$(OUTPREFIX)_RandomForest_KBest.log: $(FILE) $(LABEL)
-	python $(BINDIR)/sklearn_rf_training_fixrank.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/juxt --ranking KBest
-
-
-$(OUTDIR)/juxt/$(OUTPREFIX)_MCC_scores.txt: $(OUTDIR)/juxt/$(OUTPREFIX)_RandomForest_KBest.log $(subst _tr,_ts,$(FILE)) $(subst _tr,_ts,$(LABEL))
-	python $(BINDIR)/sklearn_rf_validation_writeperf.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/juxt --tslab $(word 3,$^)
-
-
-$(OUTDIR)/rSNF/INF_$(OUTPREFIX).txt: $(DATA1) $(DATA2) $(LABEL)
-	Rscript $(BINDIR)/snf_integration.R --d1 $(word 1,$^) --d2 $(word 2,$^) --lab $(word 3,$^) \
-		--scriptDir $(BINDIR)/SNFtools/ --clust spectral --threads "$(THREADS)" \
-		--outf $@
-
-
-$(OUTDIR)/rSNF/$(OUTPREFIX)_RandomForest_rankList.log: $(FILE) $(LABEL) $(OUTDIR)/rSNF/INF_$(OUTPREFIX).txt
-	python $(BINDIR)/sklearn_rf_training_fixrank.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/rSNF \
-		--ranking rankList --rankFeats $(word 3,$^)
-
-
-$(OUTDIR)/rSNF/$(OUTPREFIX)_MCC_scores.txt: $(OUTDIR)/rSNF/$(OUTPREFIX)_RandomForest_rankList.log $(subst _tr,_ts,$(FILE)) $(subst _tr,_ts,$(LABEL))
-	python $(BINDIR)/sklearn_rf_validation_writeperf.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/rSNF --tslab $(word 3,$^)
-
-
-$(OUTDIR)/rSNFi/intersection_$(OUTPREFIX).txt: $(OUTDIR)/juxt/$(OUTPREFIX)_RandomForest_KBest.log $(OUTDIR)/rSNF/$(OUTPREFIX)_RandomForest_rankList.log
-	python $(BINDIR)/intersect_biomarkers.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/rSNFi/venn_$(OUTPREFIX).png $@ --title1 "$(LEVEL1)" --title2 "$(LEVEL2)"
-
-
-$(OUTDIR)/rSNFi/$(OUTPREFIX).txt: $(FILE) $(OUTDIR)/rSNFi/intersection_$(OUTPREFIX).txt
-	python $(BINDIR)/extract_topfeats_onecol.py $(word 1,$^) $(word 2,$^) $@
-
-
-$(OUTDIR)/rSNFi/$(OUTPREFIX)_RandomForest_KBest.log: $(OUTDIR)/rSNFi/$(OUTPREFIX).txt $(LABEL)
-	python $(BINDIR)/sklearn_rf_training_fixrank.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/rSNFi --ranking KBest
-
-
-$(OUTDIR)/rSNFi/$(OUTPREFIX)_MCC_scores.txt: $(OUTDIR)/rSNFi/$(OUTPREFIX)_RandomForest_KBest.log $(subst _tr,_ts,$(FILE)) $(subst _tr,_ts,$(LABEL))
-	python $(BINDIR)/sklearn_rf_validation_writeperf.py $(word 1,$^) $(word 2,$^) $(OUTDIR)/rSNFi --tslab $(word 3,$^)
-

README.md

-### Integrative Network Fusion (INF)
+# Integrative Network Fusion (INF)
 ![INF pipeline ](figs/INF_pipeline.jpeg)
 
-**Setup**
+## Setup
 ```bash
 git clone https://gitlab.fbk.eu/MPBA/INF
 cd INF
@@ -9,19 +9,44 @@ conda env create -f env.yml -n inf
 conda activate inf
 ```
 
+### Additional dependencies
+
+#### R dependencies
 To install the R dependencies (not in conda channels), run the following command via the R prompt:
 ```bash
 install.packages("TunePareto")
 ```
 
-To install `mlpy`, follow the instructions [here](https://gitlab.fbk.eu/MPBA/mlpy). 
+#### MLPY
+To install `mlpy` follow this instructions:
+
+`mlpy` package is required for some operations included in the DAP procedure.
+
+The `mlpy` package available on PyPI is outdated and not working on OSX platforms.
 
+These are the steps to follow:
 
+Let `<ANACONDA>` be your anaconda path (e.g., `/home/user/anaconda3`).
+
+Adjust these environmental variables:
+```bash
+export LD_LIBRARY_PATH=<ANACONDA>/envs/<ENV>/lib:${LD_LIBRARY_PATH}
+export CPATH=<ANACONDA>/envs/<ENV>/include:${CPATH}
+```
+
+and then install `mlpy` from GitLab:
+```bash
+pip install git+https://gitlab.fbk.eu/MPBA/mlpy.git
+```
+
+#### Other Python dependencies
 To install `bootstrapped`:
 ```bash
 pip install bootstrapped
 ```
 
+## Usage
+
 **Input files**
 
 * omics layer 1 data: samples x features, tab-separated, with row & column names

extract_topfeats.py

@@ -12,7 +12,7 @@ def extract_feats(datafile, rankedfile, nfeat, outfile):
     # feats abundances (no names of samples, no header)
     # data_ab = data_ab.astype(np.float)
 
-    rank = np.loadtxt(rankedfile, delimiter = '\t', skiprows = 1, dtype = str)
+    rank = np.loadtxt(rankedfile, delimiter='\t', skiprows=1, dtype=str)
     # number of features in the list
     nf_list = rank.shape
     if len(nf_list) > 1:
@@ -20,9 +20,7 @@ def extract_feats(datafile, rankedfile, nfeat, outfile):
         top_feats = feats[0:nfeat]
     else:
         top_feats = rank[1]
-
-
-    #print top_feats.shape
+    # print top_feats.shape
     # extract top features from table with abundances of all features
     idx = []
     if len(nf_list) == 1:
@@ -35,16 +33,15 @@ def extract_feats(datafile, rankedfile, nfeat, outfile):
                 print('###### MISSING %s ######' % top_feats[i])
 
     # considering samples names in the new table
-    sel_feats=[features[i] for i in idx]
+    sel_feats = [features[i] for i in idx]
 
     # write new table
     with open(outfile, 'w') as outw:
-        writer = csv.writer(outw, delimiter = '\t', lineterminator = '\n')
+        writer = csv.writer(outw, delimiter='\t', lineterminator='\n')
         # header
-        writer.writerow(['Samples']+sel_feats)
+        writer.writerow(['Samples'] + sel_feats)
         for i in range(0, len(samples)):
-            writer.writerow([samples[i]]+data_ab[i,idx].tolist())
-
+            writer.writerow([samples[i]] + data_ab[i, idx].tolist())
 
 
 if __name__ == "__main__":

extract_topfeats_onecol.py

@@ -14,30 +14,30 @@ __date__    = '15 December 2016'
 
 #### Extract features from a given dataset ####
 
+
 def extract_feats(datafile, rankedfile, outfile):
-    #print locals()
+    # print locals()
     # table with feats abundances
     data = np.loadtxt(datafile, delimiter='\t', dtype=str)
     # feats abundances (no names of samples, no header)
-    data_ab = data[1:,1:].astype(np.float)
+    data_ab = data[1:, 1:].astype(np.float)
 
     rank = np.loadtxt(rankedfile, delimiter='\t', skiprows=1, dtype=str)
     # number of features in the list
     nf_list = rank.shape
     if len(nf_list) > 1:
         feats = rank[:, 0]
-        top_feats = feats #[0:nfeat]
+        top_feats = feats  # [0:nfeat]
     else:
         top_feats = rank
 
 
-
     # extract top features from table with abundances of all features
     idx = []
     nfeat = len(top_feats)
     for i in range(nfeat):
-        if top_feats[i] in data[0,:].tolist():
-            idx.append(data[0,:].tolist().index(top_feats[i]))
+        if top_feats[i] in data[0, :].tolist():
+            idx.append(data[0, :].tolist().index(top_feats[i]))
         else:
             print(top_feats[i])
 
@@ -48,8 +48,8 @@ def extract_feats(datafile, rankedfile, outfile):
     # write new table
     with open(outfile, 'w') as outw:
         writer = csv.writer(outw, delimiter='\t', lineterminator='\n')
-        for i in range(len(sel_feats[:,0])):
-            writer.writerow(sel_feats[i,:])
+        for i in range(len(sel_feats[:, 0])):
+            writer.writerow(sel_feats[i, :])
 
 
 if __name__ == "__main__":

input_output.py

 import numpy as np
 import pandas as pd
 
+
 def load_data(filename):
     df = pd.read_csv(filename, sep='\t', header=0, index_col=0)
     var_names = df.columns.tolist()
@@ -8,14 +9,18 @@ def load_data(filename):
     data = df.values.astype(dtype=np.float)
     return sample_names, var_names, data
 
+
 def save_split(x, y, sample_names, var_names, basename):
     """
     x, y: output of train_test_split
     sample_names var_names: lists with samples and feature names (will be the DataFrame row and column names)
     """
     x_df = pd.DataFrame(x, index=sample_names, columns=var_names)
-    x_df.to_csv(f"{basename}.txt", sep='\t', index=True, header=True, index_label="sampleID")
+    x_df.to_csv(
+        f"{basename}.txt", sep='\t', index=True, header=True, index_label="sampleID"
+    )
 
     y_df = pd.DataFrame(y, index=sample_names, columns=['label'])
-    y_df.to_csv(f"{basename}.lab", sep='\t', index=True, header=True, index_label="sampleID")
-
+    y_df.to_csv(
+        f"{basename}.lab", sep='\t', index=True, header=True, index_label="sampleID"
+    )

intersect_biomarkers.py

@@ -18,21 +18,59 @@ import numpy as np
 
 matplotlib.use('Agg')
 
-parser = argparse.ArgumentParser(description='Find the intersection between feature lists and produce Venn diagrams.')
-parser.add_argument('CONFIGFILE1', type=str, help='Training experiment configuration file 1 (with info about number of top discriminant features)')
-parser.add_argument('CONFIGFILE2', type=str, help='Training experiment configuration file 2 (with info about number of top discriminant features)')
-parser.add_argument('OUTLIST', type=str, help='Output file for intersected feature list.')
-parser.add_argument('OUTFILE', type=str, nargs='?', help='Output file for Venn diagram plot.')
-
-parser.add_argument('--title1', type=str, default='List_1', nargs='?', help='Name for first diagram (default: %(default)s)')
-parser.add_argument('--title2', type=str, default='List_2', nargs='?', help='Name for second diagram (default: %(default)s)')
-parser.add_argument('--configFile3', type=str, default='NO', nargs='?', help='Third configuration file - optional (default: %(default)s)')
-parser.add_argument('--title3', type=str, default='List_3', nargs='?', help='Name for third diagram (default: %(default)s)')
+parser = argparse.ArgumentParser(
+    description='Find the intersection between feature lists and produce Venn diagrams.'
+)
+parser.add_argument(
+    'CONFIGFILE1',
+    type=str,
+    help='Training experiment configuration file 1 (with info about number of top discriminant features)',
+)
+parser.add_argument(
+    'CONFIGFILE2',
+    type=str,
+    help='Training experiment configuration file 2 (with info about number of top discriminant features)',
+)
+parser.add_argument(
+    'OUTLIST', type=str, help='Output file for intersected feature list.'
+)
+parser.add_argument(
+    'OUTFILE', type=str, nargs='?', help='Output file for Venn diagram plot.'
+)
+
+parser.add_argument(
+    '--title1',
+    type=str,
+    default='List_1',
+    nargs='?',
+    help='Name for first diagram (default: %(default)s)',
+)
+parser.add_argument(
+    '--title2',
+    type=str,
+    default='List_2',
+    nargs='?',
+    help='Name for second diagram (default: %(default)s)',
+)
+parser.add_argument(
+    '--configFile3',
+    type=str,
+    default='NO',
+    nargs='?',
+    help='Third configuration file - optional (default: %(default)s)',
+)
+parser.add_argument(
+    '--title3',
+    type=str,
+    default='List_3',
+    nargs='?',
+    help='Name for third diagram (default: %(default)s)',
+)
 
 __author__ = 'Alessandro Zandona'
 __date__ = '15 December 2016'
 
-if len(sys.argv)==1:
+if len(sys.argv) == 1:
     parser.print_help()
     sys.exit(1)
 
@@ -77,7 +115,7 @@ feats2 = fl_2[:NFEATS, 1]
 # Convert lists into sets
 feats2_set = set(feats2)
 
-if (configfile3 != 'NO'):
+if configfile3 != 'NO':
     config.read(configfile3)
     if not config.has_section('INPUT'):
         print("%s is not a valid configuration file." % CONFIGFILE2)
@@ -96,53 +134,60 @@ if (configfile3 != 'NO'):
 
 # Intersection between lists
 f1f2 = feats1_set.intersection(feats2_set)
-if (configfile3 != 'NO'):
+if configfile3 != 'NO':
     f1f3 = feats1_set.intersection(feats3_set)
     f2f3 = feats2_set.intersection(feats3_set)
 
 # associate to each common feature the position in each lists
-#outFile_f1f2=os.path.join(os.path.dirname(OUTFILE),'Intersection_%s_%s.txt' %(title1,title2))
-#outw=open(outFile_f1f2, 'w')
+# outFile_f1f2=os.path.join(os.path.dirname(OUTFILE),'Intersection_%s_%s.txt' %(title1,title2))
+# outw=open(outFile_f1f2, 'w')
 with open(OUTLIST, 'w') as outw:
-   writer = csv.writer(outw, delimiter = '\t', lineterminator = '\n')
-   writer.writerow(['Feature', 'Position in %s' %title1, 'Postition in %s' %title2])
+    writer = csv.writer(outw, delimiter='\t', lineterminator='\n')
+    writer.writerow(['Feature', 'Position in %s' % title1, 'Postition in %s' % title2])
     for i in range(len(list(f1f2))):
         # current feature in intersection
         interF = list(f1f2)[i]
         # position of current feature in first list
-      idx_list1 = np.where(feats1==interF)[0][0]
+        idx_list1 = np.where(feats1 == interF)[0][0]
         # position of current feature in second list
-      idx_list2 = np.where(feats2==interF)[0][0]
-      writer.writerow([list(f1f2)[i], idx_list1+1, idx_list2+1])
+        idx_list2 = np.where(feats2 == interF)[0][0]
+        writer.writerow([list(f1f2)[i], idx_list1 + 1, idx_list2 + 1])
 
-if (configfile3 != 'NO'):
+if configfile3 != 'NO':
     # associate to each common feature the position in each lists
-   outFile_f1f3=os.path.join(os.path.dirname(OUTFILE),'Intersection_%s_%s.txt' %(title1,title3))
+    outFile_f1f3 = os.path.join(
+        os.path.dirname(OUTFILE), 'Intersection_%s_%s.txt' % (title1, title3)
+    )
     with open(outFile_f1f3, 'w') as outw:
-      writer = csv.writer(outw, delimiter = '\t', lineterminator = '\n')
-      writer.writerow(['Feature', 'Position in %s '%title1, 'Postition in %s ' %title3])
+        writer = csv.writer(outw, delimiter='\t', lineterminator='\n')
+        writer.writerow(
+            ['Feature', 'Position in %s ' % title1, 'Postition in %s ' % title3]
+        )
         for i in range(len(list(f1f3))):
             # current feature in intersection
             interF = list(f1f3)[i]
             # position of current feature in first list
-         idx_list1 = np.where(feats1==interF)[0][0]
+            idx_list1 = np.where(feats1 == interF)[0][0]
             # position of current feature in second list
-         idx_list3 = np.where(feats3==interF)[0][0]
-         writer.writerow([list(f1f3)[i], idx_list1+1, idx_list3+1])
-   
+            idx_list3 = np.where(feats3 == interF)[0][0]
+            writer.writerow([list(f1f3)[i], idx_list1 + 1, idx_list3 + 1])
 
-   outFile_f2f3=os.path.join(os.path.dirname(OUTFILE),'Intersection_%s_%s.txt' %(title2,title3))
+    outFile_f2f3 = os.path.join(
+        os.path.dirname(OUTFILE), 'Intersection_%s_%s.txt' % (title2, title3)
+    )
     with open(outFile_f2f3, 'w') as outw:
-      writer = csv.writer(outw, delimiter = '\t', lineterminator = '\n')
-      writer.writerow(['Feature', 'Position in %s '%title2, 'Postition in %s ' %title3])
+        writer = csv.writer(outw, delimiter='\t', lineterminator='\n')
+        writer.writerow(
+            ['Feature', 'Position in %s ' % title2, 'Postition in %s ' % title3]
+        )
         for i in range(len(list(f2f3))):
             # current feature in intersection
             interF = list(f2f3)[i]
             # position of current feature in first list
-         idx_list2 = np.where(feats2==interF)[0][0]
+            idx_list2 = np.where(feats2 == interF)[0][0]
             # position of current feature in second list
-         idx_list3 = np.where(feats3==interF)[0][0]
-         writer.writerow([list(f2f3)[i], idx_list2+1, idx_list3+1])
+            idx_list3 = np.where(feats3 == interF)[0][0]
+            writer.writerow([list(f2f3)[i], idx_list2 + 1, idx_list3 + 1])
 
 
 # # plot Venn diagrams

list_distances.py

+import argparse
+from itertools import combinations
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from mlpy import canberra_stability
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--resultsdir', type=str, help='Results folder')
+parser.add_argument('--dataset', type=str, help='Dataset name')
+parser.add_argument('--target', type=str, help='Clinical endpoint')
+parser.add_argument(
+    '--model', type=str, default='randomForest', help='Model (default: %(default)s)'
+)
+parser.add_argument(
+    '--nf_min', type=int, default=10, help='Min #feat (default: %(default)s)'
+)
+parser.add_argument(
+    '--nf_max', type=int, default=50, help='Max #feat (default: %(default)s)'
+)
+parser.add_argument(
+    '--nf_step',
+    type=int,
+    default=10,
+    help='Increase by these many feat (default: %(default)s)',
+)
+parser.add_argument('--nf_rsnf', type=int, nargs='+', help='One or more #feat for rSNF')
+parser.add_argument('--layers', type=str, nargs='+', help='')
+args = parser.parse_args()
+
+RESULTSDIR = args.resultsdir  # top-level results directory
+DATASET = args.dataset  # 'tcga_breast'
+TARGET = args.target  # 'ER'
+MODEL = args.model
+NF_MIN = args.nf_min
+NF_MAX = args.nf_max
+NF_STEP = args.nf_step
+NF_RSNF = args.nf_rsnf
+LAYERS = args.layers
+N_LAYERS = len(LAYERS)
+MODE = 'rSNF'
+
+assert (
+    Path(RESULTSDIR, DATASET).expanduser().exists()
+), f"{RESULTSDIR}/{DATASET} not found"
+
+assert (
+    Path(RESULTSDIR, f"{DATASET}_SNFdap").expanduser().exists()
+), f"{RESULTSDIR}/{DATASET}_SNFdap not found"
+
+for k in range(2, N_LAYERS + 1):
+    for comb in combinations(LAYERS, k):
+        layers_concat = '_'.join(comb)
+        bordas = []
+        for datatype in [DATASET, f'{DATASET}_SNFdap']:
+            bordaf = f'{RESULTSDIR}/{datatype}/{TARGET}/{MODEL}/Borda_splits_50-60_{MODE}_{layers_concat}.txt'
+            bordas.append(pd.read_csv(bordaf, sep='\t', index_col=None))
+        # prepare ranks for canberra_stability
+        ranks = (
+            pd.concat(
+                [
+                    np.argsort(bordas[0]['FEATURE_ID']),
+                    np.argsort(bordas[1]['FEATURE_ID']),
+                ],
+                axis=1,
+            )
+            .transpose()
+            .values
+        )
+        for nf in np.arange(NF_MIN, NF_MAX + NF_STEP, NF_STEP):
+            cs = canberra_stability(ranks, nf)
+            print(f'{MODE} - {layers_concat} - stability({nf}) = {cs:.3f}')
+        # additional steps for NF_RSNF
+        print()
+        for nf in NF_RSNF:
+            cs = canberra_stability(ranks, nf)
+            print(f'{MODE} - {layers_concat} - stability({nf}) = {cs:.3f}')
+        print()
+        print()

performance.py

 from __future__ import division
+
 import numpy as np
 
 __author__ = 'Davide Albanese'
 
+