Added comments

Author: jessemzhang

Trapnell_pipeline/Trapnell_wrapper.py

@@ -3,13 +3,15 @@
 import sys
 import numpy as np
 
+# Parse the options from command line
 try:
     opts, args = getopt.getopt(sys.argv[1:],"i:n:k:t:",["idir=","njobs=","hacked-kallisto-path=","reference-transcriptome="])
 except getopt.GetoptError:
     print ("getopterrror")
     print ('usage is : \n python Trapnell_wrapper.py -i input_SRA_dir -k path-to-hacked-kallisto -t path-to-human-reference-transcriptome [-n number-of-processes-to-use]')
     sys.exit(1)
-    
+
+# The four options used in this pipeline are    
 SRA_dir=''
 num_proc=1
 kallipso_path=''
@@ -25,23 +27,25 @@
     elif opt in ("-t","--reference-transcriptome"):
         ref_transcriptome=arg
 
-        
+# Error message if function not called correctly        
 if (not SRA_dir) or (not kallipso_path) or (not ref_transcriptome):
     print ('usage is : \n python Trapnell_wrapper.py -i input_SRA_dir -k path-to-hacked-kallisto -t path-to-human-reference-transcriptome [-n number-of-processes-to-use]')
     sys.exit(1)
 
+# Execute process_SRA, which converts SRA to fastq
 print('Extracting reads from SRAs...')
 os.system('mkdir -p ./reads/')
 os.system('rm -f ./reads/*')
 os.system('python process_SRA.py -i '+SRA_dir+' -o ./reads/ -n '+str(num_proc))
 
+# This removes files that Trapnell didn't use. (Specific for analysis of this dataset)
 print('Removing files that Trapnell throws...')
 relevant_fls=np.loadtxt('Files_to_keep.txt',dtype=str)
 fls_to_remove=[cells for cells in os.listdir('./reads/') if cells.split('_')[0] not in relevant_fls ]
 for fl in fls_to_remove:
     os.system('rm '+'./reads/'+fl)
 
-
+# Use kallisto to build index. should return same results as hacked kallisto
 print('Generating the Kallisto index (with hacked kallisto)...')
 os.system('mkdir -p ./kallisto_index')
 os.system('rm -f ./kallisto_index/*')
@@ -52,15 +56,16 @@
 num_ec = sum(1 for line in open('./kallisto_index/Trapnell_index.idx_ecmap.txt'))
 print(num_ec)
 
+# Use hacked kallisto to generate TCCs (output is .class file that contains counts of ECs)
 print('Generating TCC (with hacked kallisto)...')
 os.system('mkdir -p ./transcript_compatibility_counts/')
 os.system('rm -f ./transcript_compatibility_counts/*')
 os.system('python get_pseudoalignments_paired_end.py -i ./reads/ -o ./transcript_compatibility_counts/ -k '+kallipso_path+ ' -t '+ index_path +' -n '+ str(num_proc))
 
+# Process the .class file into a matrix. normalize such that each row sums to 1 
 print('Generating TCC distribution...')
-
 os.system('python get_tcc_dist.py -i ./transcript_compatibility_counts/ -m '+str(num_ec)+' -t ./Trapnell_TCC.dat -d ./Trapnell_TCC_distribution.dat')
 
+# Process the TCC distribution matrix to get pairwise distributions
 print('Generating pairwise distances...')
-
 os.system('python get_pairwise_distances.py ./Trapnell_TCC_distribution.dat ./Trapnell_TCC_pairwise_distance.dat '+str(num_proc))