/
plotBoxPlot_GeneLevel.UCSCfreeze.py
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plotBoxPlot_GeneLevel.UCSCfreeze.py
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import scipy as sp
import pdb
import libs.expression
import os
import sys
from optparse import OptionParser, OptionGroup
def parse_options(argv):
'''
option parser
'''
### required input
parser = OptionParser()
required = OptionGroup(parser, 'Input')
required.add_option('-s', '--star', dest = 'fn_star', metavar = 'FILE', help = 'STAR count file')
required.add_option('-t', '--tophat', dest = 'fn_tophat', metavar = 'FILE', help = 'Tophat count file')
### optional arguments
optional = OptionGroup(parser, 'optional')
## outdir
outdir_hstr = 'Output direcory for all files'
outdir = os.path.realpath(__file__).rsplit('/',1)[0]
optional.add_option('-o', '--out_dir', dest = 'out_dir', help = outdir_hstr, default = outdir)
## genelength buffe
fnl_hstr = 'Location of file with gene length'
fnlfile = os.path.join(os.path.realpath(__file__).rsplit('/',1)[0], 'data','geneLength.tsv')
optional.add_option('-g', '--genelength', dest = 'fn_length',metavar = 'FILE', help = fnl_hstr, default = fnlfile)
## annotation file
fna_hstr = 'Annotation file [gtf]'
fnafile = os.path.join(os.path.realpath(__file__).rsplit('/',1)[0], 'data','gencode.v19.annotation.hs37d5_chr.gtf')
optional.add_option('-a', '--anno', dest = 'fn_anno', metavar = 'FILE', help = fna_hstr, default = fnafile)
parser.add_option_group(required)
parser.add_option_group(optional)
(options, args) = parser.parse_args()
return options
def getLength(fn_anno):
'''
input gtf annotation and output all exonic positions
'''
anno = sp.loadtxt(fn_anno, delimiter = '\t', dtype = 'string', usecols=[0,2,3,4,8])
chrm = anno[:,0]
anno = anno[:,1:]
anno = anno[anno[:,0] == 'exon',:] ### filter down to exons
pos = anno[:,1:3].astype('int')
gid = [x.split(';')[0] for x in anno[:,3]] ### clean gene id's
gid = sp.array([x.split(" ")[1].strip('\"') for x in gid])
ugid = sp.unique(gid)
gnL = sp.zeros(ugid.shape[0])
for i,mgid in enumerate(ugid):
if (i % 100) == 0:
sys.stdout.write("%i out of %i \n" % (i, ugid.shape[0]))
mpos = sp.hstack([range(x[0],x[1]+1) for x in pos[mgid == gid,:]]) ### positions for this frame
gnL[i] = sp.unique(mpos).shape[0]
return ugid, gnL
def getSizeFactor(fn_anno, data, gid, mode = 'sum', withXYMT = True, filterbyPC = True):
'''
input annotation, counts and gene ids
output sum of protein coding gene levels excluding sex chromosomes and mitochondria genes
'''
anno = sp.loadtxt(fn_anno, delimiter = '\t', dtype = 'string', usecols=[0,2,8])
anno = anno[anno[:,1] == 'gene', :]
if not withXYMT: ### filter xymt
anno = anno[anno[:,0] != 'MT',:]
anno = anno[anno[:,0] != 'Y',:]
anno = anno[anno[:,0] != 'X',:]
agid = [x.split(';')[0] for x in anno[:,2]] ### clean gene id's
agid = sp.array([x.split(" ")[1].strip('\"') for x in agid])
if filterbyPC: ### filter protein coding
gtpe = [x.split(';')[2] for x in anno[:,2]]
gtpe = sp.array([x.split('\"')[1].split('\"')[0] for x in gtpe])
iPC = sp.where(gtpe == 'protein_coding')[0]
agid = agid[iPC]
iGn = sp.in1d(gid, agid)
libsize = sp.sum(data[iGn,:], axis = 0)
if mode == 'uq':
libsize = sp.array([sp.percentile(x[x!=0] ,75) for x in data[iGn,:].T]) * iGn.sum()
return libsize
def main():
### get options
options = parse_options(sys.argv)
### get gene length and store locally (Should probably clean itself up)
if os.path.exists(options.fn_length):
tmp = sp.loadtxt(options.fn_length, delimiter = '\t', dtype = 'string')
gnLength = tmp[:,1].astype('float')
gnLength_gid = tmp[:,0]
else:
gnLength_gid, gnLength = getLength(options.fn_anno)
sp.savetxt(options.fn_length, sp.vstack((gnLength_gid, gnLength)).T, delimiter = '\t', fmt = '%s')
### get star data
data = sp.loadtxt(options.fn_star, delimiter = '\t', dtype = 'string')
gtid = data[0,1:]
gid = data[1:,0]
data = data[1:,1:].astype('float')
### remove htseq summary stats if available
iOK = ~sp.array([x.startswith('__') for x in gid])
gid = gid[iOK]
data = data[iOK,:]
### sort by gene id
sidx = sp.argsort(gid)
gid = gid[sidx]
data = data[sidx,:]
### sort by gtid
sidx = sp.argsort(gtid)
gtid = gtid[sidx]
data = data[:,sidx]
### keep copy for later and sort gene length by gene id so that id's match
### in case length is ordered differently
data_star = data.copy()
gtid_star = gtid.copy()
sidx = sp.argsort(gnLength_gid)
gnLength_gid = gnLength_gid[sidx]
gnLength = gnLength[sidx]
### get total count FPKM
libSize = getSizeFactor(options.fn_anno, data, gid, withXYMT = True, filterbyPC = False)
### store size factor total count star
myExp = libs.expression.ExpressionData(Y = data, GID = gid, GTID = gtid)
myExp.libsizenorm(trafo='fpkm', totalcounts = libSize, gelen =gnLength)
myExp.libsizeplots(fn_base = os.path.join(options.out_dir,'postnorm_star_gene_fpkm'), fmt = 'pdf', figsize = [100,5])
myExp.store(os.path.join(options.out_dir, 'star_fpkm.tsv.gz'))
### get uq FPKM
libSize = getSizeFactor(options.fn_anno, data, gid, mode = 'uq', withXYMT = False, filterbyPC = True) ###'uq'
myExp = libs.expression.ExpressionData(Y = data, GID = gid, GTID = gtid)
myExp.libsizenorm(trafo='fpkm', totalcounts = libSize, gelen =gnLength)
myExp.libsizeplots(fn_base = os.path.join(options.out_dir,'postnorm_star_gene_fpkm_uq'), fmt = 'pdf', figsize = [100,5])
myExp.store(os.path.join(options.out_dir, 'star_fpkm_uq.tsv.gz'))
data = sp.loadtxt(options.fn_tophat, delimiter = '\t', dtype = 'string')
gtid = data[0,1:]
gid = data[1:,0]
gid = sp.array([x.strip('\"') for x in gid])
data = data[1:,1:].astype('float')
iOK = ~sp.array([x.startswith('__') for x in gid])
gid = gid[iOK]
data = data[iOK,:]
sidx = sp.argsort(gid)
gid = gid[sidx]
data = data[sidx,:]
sidx = sp.argsort(gtid)
gtid = gtid[sidx]
data = data[:,sidx]
if gtid_star.shape[0] != gtid.shape[0]:
midx = sp.in1d(gtid, gtid_star)
gtid = gtid[midx]
data = data[:,midx]
### get size factor TOTAL COUNT based on PROTEin CODinG only
libSize = getSizeFactor(options.fn_anno, data, gid, withXYMT = True, filterbyPC = False)
myExp = libs.expression.ExpressionData(Y = data, GID = gid, GTID = gtid)
myExp.libsizenorm(trafo='fpkm', totalcounts = libSize, gelen =gnLength)
myExp.libsizeplots(fn_base = os.path.join(options.out_dir,'postnorm_th_gene_fpkm'), fmt = 'pdf', figsize = [100,5])
myExp.store(os.path.join(options.out_dir, 'th_fpkm.tsv.gz'))
libSize = getSizeFactor(options.fn_anno, data, gid, mode = 'uq', withXYMT = False, filterbyPC = True)
myExp = libs.expression.ExpressionData(Y = data, GID = gid, GTID = gtid)
myExp.libsizenorm(trafo='fpkm', totalcounts = libSize, gelen =gnLength)
myExp.libsizeplots(fn_base = os.path.join(options.out_dir,'postnorm_th_gene_fpkm_uq'), fmt = 'pdf', figsize = [100,5])
myExp.store(os.path.join(options.out_dir, 'th_fpkm_uq.tsv.gz'))
### find overlap
instar = sp.in1d(gtid, gtid_star)
intophat = sp.in1d(gtid_star, gtid)
assert instar.sum() == intophat.sum(), 'Samples do not match'
assert instar.sum() == gtid.shape[0], 'Partial mismatch of ids'
assert intophat.sum() == gtid_star.shape[0], 'Partial mismatch of ids'
### subset to intersection
data = data[:,instar]
data_star = data_star[:,intophat]
### fix labels
gtid = gtid[instar]
gtid_star = gtid_star[intophat]
### done matching
data = data + data_star
libSize = getSizeFactor(options.fn_anno, data, gid, withXYMT = True, filterbyPC = False)
myExp = libs.expression.ExpressionData(Y = data, GID = gid, GTID = gtid)
myExp.libsizenorm(trafo='fpkm', totalcounts = libSize, gelen =gnLength)
myExp.libsizeplots(fn_base = os.path.join(options.out_dir,'postnorm_joint_gene_fpkm'), fmt = 'pdf', figsize = [100,5])
myExp.store(os.path.join(options.out_dir, 'joint_fpkm.tsv.gz'))
libSize = getSizeFactor(options.fn_anno, data, gid, mode = 'uq', withXYMT = False, filterbyPC = True)
myExp = libs.expression.ExpressionData(Y = data, GID = gid, GTID = gtid)
myExp.libsizenorm(trafo='fpkm', totalcounts = libSize, gelen =gnLength)
myExp.libsizeplots(fn_base = os.path.join(options.out_dir,'postnorm_joint_gene_fpkm_uq'), fmt = 'pdf', figsize = [100,5])
myExp.store(os.path.join(options.out_dir, 'joint_fpkm_uq.tsv.gz'))
if __name__ == "__main__":
main()