[AlexsLemonade#229] maintain consistent column order in outlier resul…

…ts tsv

Due to filtering columns on a set() in step 01 filter_samples, the column order of intermediate .feather files and therefore outlier results file changed between script reruns. To improve reproducibility, pin column order (ie samples order) to the order of the original .Rds expression matrix.

Includes new results file which has data identical to previous, but columns in correct order.

analyses/comparative-RNASeq-analysis/01-correlation-matrix.py

@@ -69,30 +69,33 @@ def filter_samples(input_matrix,
         print_v("Skipped the QC and tumor status filters; using all samples.")
         return raw_samples
 
-    # Filter to only samples with a MEND QC status of PASS
+    # Filter to only sample ids with a MEND QC status of PASS
     # Samples not mentioned in the qc_status (ie, no qc result) will be dropped by the filter
     qc_status = get_qc_status(mend_results_path, mend_manifest_path)
     qc_pass_ids= [k for k, v in qc_status.items() if v == "PASS"]
-    qc_pass_samples = raw_samples[set(raw_samples.columns).intersection(qc_pass_ids)]
+    qc_pass_ids_in_dataset = set(raw_samples.columns).intersection(qc_pass_ids)
     print_v("QC filter: dropped {} samples that were not MEND QC PASS.".format(
-            len(raw_samples.columns) - len(qc_pass_samples.columns)))
+            len(raw_samples.columns) - len(qc_pass_ids_in_dataset)))
 
-    # Call out any samples that didn't have a QC result at all but continue analysis
+    # Call out any sample ids that had no corresponding QC file, but ok to continue even if some are present
     samples_without_qc_results = set(raw_samples.columns) - set(qc_status.keys())
     if len(samples_without_qc_results):
         print("WARNING: {} samples were dropped due to missing MEND QC results. Sample IDs: {}".format(
               len(samples_without_qc_results),
               ", ".join(samples_without_qc_results)))
 
-    # Further filter to only tumor RNA-Seq samples
+    # Further filter to only tumor RNA-Seq sample ids
     clinical = utils.read_tsv(clinical_path)
     tumor_ids = clinical[(clinical["sample_type"] == "Tumor")
                          & (clinical["composition"] == "Solid Tissue")
                          & (clinical["experimental_strategy"] == "RNA-Seq")
                         ].index
-    qc_pass_tumor_samples = qc_pass_samples[set(qc_pass_samples.columns).intersection(tumor_ids)]
+    qc_pass_tumor_ids_in_dataset = qc_pass_ids_in_dataset.intersection(tumor_ids)
     print_v("Tumor filter: dropped {} samples that were not solid tumor RNA-Seq.".format(
-            len(qc_pass_samples.columns) - len(qc_pass_tumor_samples.columns)))
+            len(qc_pass_ids_in_dataset) - len(qc_pass_tumor_ids_in_dataset)))
+
+    # And apply filtered IDs to the original dataset, retaining the original column order
+    qc_pass_tumor_samples = raw_samples[[sid for sid in raw_samples.columns if sid in qc_pass_tumor_ids_in_dataset]]
     return qc_pass_tumor_samples
 
 def prepare_expression(raw_samples,