/
checkBias_2.0.py
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/
checkBias_2.0.py
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import scipy as sp
import scipy.stats as spst
import numpy as np
import sys
import os
import pdb
import glob
import fnmatch
from optparse import OptionParser, OptionGroup
from libs.annotation import *
from libs.counts import *
from libs.viz import *
from libs.bam import *
import logging
### may not be necessary
def parse_options(argv):
parser = OptionParser()
sampleinput = OptionGroup(parser, 'Input')
sampleinput.add_option('-f', '--file', dest='fn_exonq', metavar= 'FILE', help='Exon quantification file from firehose', default = '-')
sampleinput.add_option('-b', '--bam_dir', dest = 'dir_bam', metavar = 'FILE', help = 'Directory of bam files', default = '-')
sampleinput.add_option('-t', '--tab_cnt', dest = 'dir_cnt', metavar = 'FILE', help = 'Directory of tab delimited count files' , default = '-')
sampleinput.add_option('-a', '--fn_anno', dest = 'fn_anno', metavar = 'FILE', help = 'Annotation', default = '-')
sampleinput.add_option('-n', '--fn_bam', dest = 'fn_bam', metavar = 'FIlE', help = 'Specifies bam file for counting only', default = '-')
#### optional arguments
optional = OptionGroup(parser, 'Options')
optional.add_option('-q','--quant', dest = 'qmode', metavar = 'STRING', help = 'What type of quantification to use [rpkm,raw]', default = 'raw')
optional.add_option('-l', '--length', dest = 'length', metavar = 'STRING', help = 'Length filter [uq,mq,lq]', default = 'uq')
optional.add_option('-w', '--whitelist', dest = 'fn_white', metavar = 'FILE', help = 'White list with ids', default = '-')
optional.add_option('-o', '--fnout', dest = 'fn_out', metavar = 'FILE', help = 'prefix for output', default = 'out')
optional.add_option('-g', '--log', dest = 'fn_log', metavar = 'FILE', help = 'Log file', default = 'out.log')
optional.add_option('-p', '--plot', dest = 'doPlot', action = "store_true", help = 'Plot figures', default=False)
optional.add_option('-v', '--verbose', dest = 'isVerbose', action = "store_true", help = 'Set Logger To Verbose', default=False)
optional.add_option('-c', '--pseudocount', dest = 'doPseudo', action = "store_true", help = 'Add Pseudocounts to ratio', default=False)
optional.add_option('-m', '--fn_anno_tmp', dest = 'fn_anno_tmp', metavar = 'FILE', help = 'Temp file for storing anno info', default = os.path.join(os.path.realpath(__file__).rsplit('/',1)[:-1][0] ,'anno.tmp'))
optional.add_option('-i', '--genelist', dest = 'fn_genes', metavar = 'FILE', help = 'file with genenames to use', default = '-')
optional.add_option('-s', '--fn_sample_ratio', dest = 'fn_sample_ratio', metavar = 'FILE', help = 'Sample Ratios in relation to yours', default = os.path.join(os.path.realpath(__file__).rsplit('/',1)[:-1][0] ,'data','sampleRatios/TCGA_sample_a_ratio_uq.tsv'))
optional.add_option('-d', '--mask-filter', dest = 'filt', help = 'Mask all readcounts below this integer', default = '0')
parser.add_option_group(sampleinput)
parser.add_option_group(optional)
(options, args) = parser.parse_args()
if len(argv) < 2:
parser.print_help()
sys.exit(2)
if sp.sum(int(options.fn_exonq != '-') + int(options.dir_bam != '-') + int(options.dir_cnt != '-') + int(options.fn_bam != '-')) != 1:
print "Please specify exactly one type of input file(s) (e.g.: Exon quantification, Bam Files or Tab delimited count files)"
parser.print_help()
sys.exit(2)
return options
def whitelisting(options, header, data):
whitelist = sp.loadtxt(options.fn_white, delimiter = '\t', dtype = 'string')
midx_m = sp.in1d(header, whitelist)
tags = sp.array([x.split('-')[3] for x in header])
midx_n = np.core.defchararray.startswith(tags, '1')
header = header[midx_m | midx_n]
data = data[:, midx_m | midx_n]
return header, data
def calculateBias(exonTgene, data, exonpos):
mycounts = sp.zeros((exonTgene.shape[0], data.shape[1], 2))
myLength = sp.zeros(exonTgene.shape[0])
for i,rec in enumerate(exonTgene):
istart = exonpos == rec[0]
iend = exonpos == rec[1]
if sp.sum(istart) == 0:
continue
if sp.sum(iend) == 0:
continue
if exonpos[istart][0].split(':')[-1] == '-':
tmp = istart
istart = iend
iend = tmp
startcc = sp.array(exonpos[istart][0].split(':')[1].split('-')).astype('int')
endcc = sp.array(exonpos[iend][0].split(':')[1].split('-')).astype('int')
startlen = startcc[1] - startcc[0]
endlen = endcc[1] - endcc[0]
myLength[i] = float(rec[4])
mycounts[i,:,0] = data[istart,:]
mycounts[i,:,1] = data[iend,:]
return mycounts, myLength
def main():
### Parse options
options = parse_options(sys.argv)
filt = int(options.filt)
#### set up logger
logging.basicConfig(filename = options.fn_log, level = 0, format = '%(asctime)s - %(levelname)s - %(message)s')
log = logging.getLogger()
if options.isVerbose:
consoleHandler = logging.StreamHandler()
log.addHandler(consoleHandler)
### Read annotation from file
logging.info("Reading Annotation from file")
exonTgene = getAnnotationTable(options)
### Read in or generate quantifications
logging.info("Reading in quantifications")
if options.fn_exonq != '-': ### TODO: change this into raw by principle and then generate rpkm which match firebrowse
exonpos, header, data = readExpData(options.fn_exonq, options.qmode)
iOK = ~sp.array([x.startswith('chrM') for x in exonpos])
exonpos = exonpos[iOK]
data = data[iOK,:]
sidx = sp.argsort(exonpos)
exonpos = exonpos[sidx]
data = data[sidx,:]
elif options.dir_cnt != '-':
exonpos, header, data = readExpDataBam(options.dir_cnt) ### move this over to file lists rather than dirs
data[data < filt] = sp.nan
if options.qmode == 'rpkm':
data = (data * 1E9) / sp.sum(data , axis = 0)
exonl = sp.array([int(x.split(':')[1].split('-')[1]) - int(x.split(':')[1].split('-')[0]) + 1 for x in exonpos])
data /= sp.tile(exonl[:, sp.newaxis], data.shape[1])
elif options.dir_bam != '-':
bam_list = glob.glob(os.path.join(options.dir_bam, '*.bam'))
header = bam_list ### change this TODO
data = get_counts_from_multiple_bam(bam_list, exonTgene) ### REMOVE
exonpos = exonTgene[:, :2].ravel('C')
elif options.fn_bam != '-':
print "WARNING: Running only gene counts"
#exonTable = getFullAnnotationTable()
exonTable = sp.sort(exonTgene[:,[0,1]].ravel())
data = get_counts_from_single_bam(options.fn_bam,exonTable)
sp.savetxt(options.fn_out+'counts.tsv', sp.vstack((exonTable,data[::2])).T, delimiter = '\t', fmt = '%s')
sys.exit(0)
### normalize counts by exon length
logging.info("Normalize counts by exon length")
if (options.fn_exonq == '-') | (options.qmode == 'raw'):
exonl = sp.array([int(x.split(':')[1].split('-')[1]) - int(x.split(':')[1].split('-')[0]) + 1 for x in exonpos])
data /= sp.tile(exonl[:, sp.newaxis], data.shape[1])
#data /= sp.hstack([exonl[:, sp.newaxis] for x in xrange(data.shape[0] / exonl.shape[0])]).ravel(1)
### Subset to whitelist
if options.fn_white != '-':
logging.info("Subsetting to whitelist")
header, data = whitelisting(options, header, data)
### Calculate 3'/5' Bias
logging.info("Calculate Bias")
mycounts, myLength = calculateBias(exonTgene, data, exonpos)
### subset to high expression ### TODO: need to change this for clarity here....
logging.info("Make sure I got only reasonably expressed genes")
if (options.fn_genes == '-') & (options.fn_exonq != '-'): ### assuming that i do not have rpkm and not pre-selected genes anyways
primeCov = sp.mean(mycounts[:,:,0], axis = 1) + sp.mean(mycounts[:,:,1], axis = 1)
### ensure average expression of 1 rpkm across samples
if options.length == 'uq':
iOK = (sp.mean(mycounts[:,:,0], axis = 1) > 1) & (sp.mean(mycounts[:,:,1], axis = 1) > 1)
elif options.length == 'mq':
iOK = (sp.mean(mycounts[:,:,0], axis = 1) > 1) & (sp.mean(mycounts[:,:,1], axis = 1) > 1)
elif options.length == 'lq':
iOK = (sp.mean(mycounts[:,:,0], axis = 1) > 1) & (sp.mean(mycounts[:,:,1], axis = 1) > 1)
mycounts = mycounts[iOK,:,:]
myLength = myLength[iOK]
sp.savetxt(options.fn_out+'.geneSet', exonTgene[iOK,:], fmt = '%s', delimiter = '\t')
if options.doPseudo:
logging.info("Add Pseudocount and estimate ratios")
ratio = ((mycounts[:,:,1] + 1) / (mycounts[:,:,0] + 1))
else:
logging.info("Estimate ratios")
ratio = ((mycounts[:,:,1]) / (mycounts[:,:,0]))
logging.info("Find Median")
vals = []
for i in xrange(mycounts.shape[1]):
iOK = ~(sp.isnan(mycounts[:,i,0])) & ~(sp.isnan(mycounts[:,i,1]))
tmp = ((mycounts[:,i,1] )[iOK]) / ((mycounts[:,i,0] )[iOK] )
vals.append(sp.percentile(tmp[~sp.isnan(tmp)],50))
vals = sp.array(vals)
sidx = sp.argsort(vals)
iqr = ( (sp.percentile(vals,75) - sp.percentile(vals,25) ) * 1.5)
logging.info("Tukey Filter is estimated to be %f" % (iqr+sp.percentile(vals, 75)))
print "Tukey Filter is estimated to be %f" % (iqr+sp.percentile(vals, 75))
print "Tukey Filter is estimated to be %f" % (sp.percentile(vals, 25)-iqr)
sp.savetxt('%s_sample_a_ratio_%s.tsv' % (options.fn_out,options.length), sp.vstack((header, vals.astype('string'))).T, delimiter = '\t', fmt = '%s')
ratio = ratio[:,sidx]
if options.doPlot:
logging.info("Plot all samples")
baselinedata = sp.loadtxt(options.fn_sample_ratio, delimiter = '\t', dtype = 'string')
baselinedata = baselinedata[:,1].astype('float')
basePval = sp.hstack((baselinedata, vals))
midx = sp.hstack((sp.ones(baselinedata.shape[0]), sp.zeros(vals.shape[0]))).astype('bool')
plotBias(basePval, '%s_bias_sorted_vline_%s.png' % (options.fn_out,options.length), midx)
midx = sp.hstack((sp.ones(baselinedata.shape[0]), sp.zeros(vals.shape[0]))).astype('bool')
plotBias(basePval, '%s_bias_sorted_vline_log_%s.png' % (options.fn_out,options.length), midx, logScale = True)
if __name__ == "__main__":
main()