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# Usage: python buildRmatrix.py paramter_for_buildRmatrix.txt
# Edit the variable TPM_TABLE for a different output file name.
# Watch out NA values in TPM.txt, these genes don't have any gene expression information.
#
# Purpose: make a TPM table, where each row is a gene, and each column is an experiment. The column name is RNA-seq experiment ID.
#
# 23 Dec 2016, hui, slcu
# Last modified 5 Apr 2017, hui, slcu
# Last modified 25 Oct 2019, hui, zjnu [Comments; add a variable WARN_NA to turn on/off print NA warnings.]
# Last modified 10 Oct 2020, hui, zjnu [note that if there are more than 1000 RNA-seq samples, this script requires at least 7GB memory to run.]
import os, sys, glob
from configure import TPM_FILE
TPM_TABLE = TPM_FILE
WARN_NA = False
####################################
GLB_PARAM_SYMBOL = '%%'
LCL_PARAM_SYMBOL = '%'
DATA_SYMBOL = '@'
####################################
def common_part(s):
''' s is expected to have this form: AT1G01020.1, remove .1 '''
s = s.strip()
index = s.find('.')
if index < 0: # not found, -1
return s
return s[0:index]
def make_expression_dict(fname, myid):
'''
fname -- salmon file
myid -- RNA-seq experiment ID
The retured value is a dictionary which looks like
{
'ID': RNA-seq experiment ID
'isoform':
{
'AT1G12345': [],
'AT2G12345': [],
...
}
}
Each gene ID (e.g., AT1G12345) has a number of isoforms which gives different expression levels.
'''
ID_COL = 0 # Salmon's quant.sf file, first column is gene ID
TPM_COL = 3 # Salmon's quant.sf file, fourth column is TPM
if not os.path.exists(fname):
print('ERROR [buildRmatrix.py]: file %s not exists.' % (fname))
sys.exit()
d = {'ID':myid, 'isoform':{}}
f = open(fname)
lines = f.readlines()
f.close()
for line in lines[1:]: # ignore head line, Name Length EffectiveLength TPM NumReads
line = line.strip()
lst = line.split()
gene_id = lst[ID_COL]
tpm = float(lst[TPM_COL])
common = common_part(gene_id) # gene id without .1, .2, etc.
if not common in d['isoform']:
d['isoform'][common] = [tpm]
else:
d['isoform'][common].append(tpm)
# make the dictionary smaller by using a string instead of a double-precision float number, so it requires less memory. Cut from 7.44G to 6.5G for 1003 TPM files.
for g in d['isoform']:
d['isoform'][g] = '%s' % get_max_expressed_isoform_save_space(g, d)
return d
def get_max_expressed_isoform_save_space(g, d):
''' Evolved from get_max_expressed_isoform(g, d) '''
if not g in d['isoform']:
return '-9'
lst = d['isoform'][g]
return '%4.2f' % max(lst)
def save_TPM_table(gene_lst, dict_lst, fname):
'''
gene_lst: a list of genes
dict_lst: a list of dictionaries. Each dictionary contains gene expression inforamtion. What is the detailed data structure of each dictionary?
fname: where the gene expression level matrix will be saved.
'''
dir_name = os.path.dirname(fname)
if not os.path.isdir(dir_name):
os.makedirs(dir_name)
if len(dict_lst) == 0:
print('buildRmatrix.py: dict_lst is empty. Nothing to build.')
sys.exit()
f = open(fname, 'w')
head = 'gene_id'
#print('Merge %d tables.' % (len(dict_lst)))
for d in dict_lst:
head += '\t' + d['ID'] # d['ID'] is the RNA-seq samples's SRA id
f.write('%s\n' % (head))
total_count = 0 # number of total gene expression levels
bad_count = 0 # number of NA gene expression levels. We wish this number to be far smaller than total_count.
missed_genes = {}
for g in gene_lst:
s = g
for d in dict_lst:
if g in d['isoform']:
v = d['isoform'][g]
else:
v = '-9'
total_count += 1
if v != '-9':
s += '\t' + v
else:
if WARN_NA:
print('WARNING [buildRmatrix.py]: %s not in %s.' % (g, d['ID']))
s += '\t' + 'NA'
bad_count += 1
missed_genes[g] = 1
f.write('%s\n' % (s))
f.close()
if 1.0 * bad_count / total_count > 0.0:
print('WARNING [buildRmatrix.py]: %s contains NA values!\n%d out of %d gene expression levels (%4.1f percent) are NAs.\n%d gene IDs are in your gene list but not in the results output by Salmon.' % (fname, bad_count, total_count, 100.0* bad_count/total_count, len(missed_genes)))
def get_dict_list(d):
''' A list of dictionaries, each element for one RNA-seq data '''
dlst = []
for myid in d['ID_LIST']:
if myid in d:
fname = d[myid]['LOCATION']
d2 = make_expression_dict(fname, myid)
dlst.append(d2)
return dlst
def get_gene_list(fname):
f = open(fname)
lst = []
for line in f:
line = line.strip()
if line != '':
l = line.split()[0]
lst.append(l)
f.close()
return lst
def get_key_value(s):
lst = s.split('=')
k, v = lst[0], lst[1]
return (k, v)
def get_value(s, delimit):
index = s.find(delimit)
if index < 0:
sys.exit()
return s[index+1:].strip()
def make_data_dict(fname):
'''
fname - parameter_for_buildRmatrix.txt
Return a dictionary which looks like
{
'ID_LIST': [],
'SRR1':
{
'LOCATION': path to the salmon quant file, e.g., /home/lanhui/brain/Data/R/Mapped/public/SRR953400_quant.txt
}
}
'''
d = {'ID_LIST':[]} # ID_LIST is a list of RNA-seq experiment IDs
f = open(fname)
lines = f.readlines()
f.close()
for line in lines:
line = line.strip()
if line == '' or line.startswith('#'):
continue
if line.startswith(DATA_SYMBOL):
s = line[line.rfind(DATA_SYMBOL[-1])+1:]
s = s.strip()
if s in d:
print('Warning [buildRmatrix.py]: ID %s is duplicated.' % (s))
sys.exit()
d[s] = {'DATA_NAME':'', 'DATA_FORMAT':'', 'DESCRIPTION':'', 'LOCATION':'', 'NOTE':''}
d['ID_LIST'].append(s)
if line.startswith('DESCRIPTION:'):
d[s]['DESCRIPTION'] = get_value(line, ':')
elif line.startswith('DATA_FORMAT:'):
d[s]['DATA_NAME'] = get_value(line, ':')
elif line.startswith('DATA_FORMAT:'):
d[s]['DATA_FORMAT'] = get_value(line, ':')
elif line.startswith('LOCATION:'):
d[s]['LOCATION'] = get_value(line, ':')
elif line.startswith('NOTE:'):
d[s]['NOTE'] = get_value(line, ':')
elif line.startswith(LCL_PARAM_SYMBOL) and not line.startswith(GLB_PARAM_SYMBOL):
make_local_parameter(d[s]['PARAM'], line)
return d
def make_global_param_dict(fname):
f = open(fname)
d = {'GENE_LIST':''} # change
for line in f:
line = line.strip()
if line.startswith(GLB_PARAM_SYMBOL):
s = line[line.rfind(GLB_PARAM_SYMBOL[-1])+1:]
lst = s.split('\t') # separate items by TAB
for x in lst:
if x != '':
k, v = get_key_value(x)
d[k] = v
f.close()
return d
## main
param_file = sys.argv[1]
global_param_dict = make_global_param_dict(param_file)
data_dict = make_data_dict(param_file)
TPM_TABLE = os.path.abspath(TPM_TABLE)
save_TPM_table(get_gene_list(global_param_dict['GENE_LIST']), get_dict_list(data_dict), TPM_TABLE)
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