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prep_TCC_matrix.py
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prep_TCC_matrix.py
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import os
import sys, gc
#json_path=os.path.abspath(sys.argv[1])
#if not os.path.isfile(json_path):
# print("ERROR: Please provide path to a valid config.json file...")
# print(sys.argv[1])
# exit(1)
#import json
#with open(json_path) as json_file:
# parameter = json.load(json_file)
#
#print("Number of threads", parameter["NUM_THREADS"])
# Load dataset
import numpy as np
from scipy.sparse import coo_matrix
from sklearn.preprocessing import normalize
import sys
import argparse
# Get arguments
parser = argparse.ArgumentParser()
parser.add_argument('-E', '--inputMatrixec', required=True, help='Input Matrix.ec')
parser.add_argument('-T', '--inputMatrixtsv', required=True, help='Input Matrix.tsv')
parser.add_argument('-O', '--outputDir', required=True, help='Output Dir')
parser.add_argument('-I', '--indexFasta', required=True, help='Provide the index fasta file used to generate the kallisto .idx')
parser.add_argument('-G', '--GenesOrTranscripts', required=False, help='enter "geneIDs" or "transcriptIDs" to return a rownames as gene or transcript IDs')
#/mnt/isilon/davidson_lab/ranum/Tools/Kallisto_Index/Mus_musculus.GRCm38.cdna.all.fa
args = parser.parse_args()
#matrix.ec file
#ecfile_dir = parameter["kallisto"]["TCC_output"]+"matrix.ec"
#tsvfile_dir = parameter["iskallisto"]["TCC_output"]+"matrix.tsv"
exfile_dir = args.inputMatrixec
tsvfile_dir = args.inputMatrixtsv
output_dir = args.outputDir
#exfile_dir = /mnt/isilon/davidson_lab/ranum/Data/SPLiT-Seq/AlignTest/SPLiT-Seq_demultiplexing/kallisto_output/matrix.ec
#tsvfile_dir = /mnt/isilon/davidson_lab/ranum/Data/SPLiT-Seq/AlignTest/SPLiT-Seq_demultiplexing/kallisto_output/matrix.tsv
print("Loading index fasta..")
if (args.GenesOrTranscripts == "transcriptIDs"):
print("Extracting transcript IDs..")
transcriptID_Dict = {}
with open(args.indexFasta, "r") as infile:
line_ct = 0
id_ct = 0
for line in infile:
if (">" in line):
split1 = line.split(" ")
split2 = split1[0].split(">")
transcriptID = split2[1]
#print(str(id_ct) + " " + transcriptID)
transcriptID_Dict[id_ct] = transcriptID
id_ct = id_ct + 1
line_ct = line_ct + 1
if (args.GenesOrTranscripts == "geneIDs"):
print("Extracting gene IDs..")
geneID_Dict = {}
with open(args.indexFasta, "r") as infile:
line_ct = 0
id_ct = 0
for line in infile:
if (">" in line):
split1 = line.split(" ")
split2 = split1[3].split(":")
geneID = split2[1]
#print(str(id_ct) + " " + transcriptID)
geneID_Dict[id_ct] = geneID
id_ct = id_ct + 1
line_ct = line_ct + 1
print("Loading input matrix..")
matrixTSV_List = []
with open(args.inputMatrixtsv, "r") as infile2:
for line in infile2:
split1 = line.split("\t")
transcriptNum = split1[0]
matrixTSV_List.append(int(transcriptNum))
print("Loading in equivalence classes..")
equivalenceClass_Dict = {}
with open(args.inputMatrixec, "r") as infile3:
for line in infile3:
split1 = line.split("\t")
split2 = split1[1].rstrip()
split3 = split2.split(",")
equivalenceClass_Dict[int(split1[0])] = split3
print("Loading TCCs..")
COOinput = np.loadtxt( tsvfile_dir, delimiter='\t' , dtype=int)
rows,cols,data = COOinput.T
nonzero_ec = np.unique(rows)
map_rows = { val:ind for ind,val in enumerate( nonzero_ec ) }
map_cols = { val:ind for ind,val in enumerate( np.unique(cols) ) }
TCCmatrix = coo_matrix( (data.astype(float),( [map_rows[r] for r in rows], [map_cols[c] for c in cols]) ) )
NUM_OF_CELLS = TCCmatrix.shape[1]
print("NUM_OF_CELLS =", NUM_OF_CELLS)
T = TCCmatrix.tocsr()
T_norm = normalize(T, norm='l1', axis=0)
T_normT = T_norm.transpose()
del TCCmatrix;
_ = gc.collect()
# Pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
from scipy.spatial.distance import *
from scipy.stats import entropy
def L1_distance(p,q):
return cityblock(p,q).sum()
# def jensen_shannon(p, q):
# m=0.5*p+0.5*q
# p = np.transpose(p[p > 0])
# q = np.transpose(q[q > 0])
# m = np.transpose(m[m > 0])
# return np.sqrt(entropy(m)-0.5*entropy(q)-0.5*entropy(p))
#num_of_threads = parameter["NUM_THREADS"]
num_of_threads = 5
print("Calculating pairwise L1 distances... ( num_threads =",num_of_threads,")")
# D_js = pairwise_distances(T_normT,metric=jensen_shannon,n_jobs=num_of_threads)
D_l1 = pairwise_distances(T_normT,metric=L1_distance,n_jobs=num_of_threads)
print("writing data...")
#Save data
T = T.todense()
#D_l1 = D_l1.todense()
#nonzero_ex = nonzero_ex.todense()
with open(output_dir+"2_rowNames.txt", 'w') as f:
for row in map_rows:
print(row, file=f)
#with open(output_dir+"3_colNames.txt", 'w') as f:
# for row in map_cols:
# print(row, file=f)
if (args.GenesOrTranscripts == "transcriptIDs"):
with open(output_dir+"3_transcriptIDs.txt", 'w') as f:
for row in map_rows:
EQgenelist = equivalenceClass_Dict[row]
Plist = []
for EQkey in EQgenelist:
Plist.append(transcriptID_Dict[int(EQkey)])
print('[%s]' % ', '.join(map(str, Plist)), file=f)
#print('[%s]' % ', '.join(map(str, EQgenelist)))
if (args.GenesOrTranscripts == "geneIDs"):
with open(output_dir+"3_geneIDs.txt", 'w') as f:
for row in map_rows:
EQgenelist = equivalenceClass_Dict[row]
Plist = []
for EQkey in EQgenelist:
Plist.append(geneID_Dict[int(EQkey)])
print('[%s]' % ', '.join(map(str, Plist)), file=f)
#print('[%s]' % ', '.join(map(str, EQgenelist)))
with open(output_dir+"1_expressionMatrix.txt", 'wb') as f:
np.savetxt(f,T, delimiter="\t")
#with open(output_dir+"2_colNames.txt", 'wb') as f:
#with open(output_dir+"pwise_dist_L1.dat", 'wb') as f:
# np.savetxt(f,D_l1, delimiter="\t")
#with open(output_dir+"nonzero_ec.dat", 'wb') as f:
# np.savetxt(f,nonzero_ec, delimiter="\t")
print("DONE.")