def main():
usage = 'usage: %prog [options] <gtf> <diff>'
parser = OptionParser(usage)
parser.add_option('-o', dest='out_dir', default='te_diff', help='Output directory [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tpf.gff'%os.environ['MASK'])
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide .gtf and .diff files')
else:
gtf_file = args[0]
diff_file = args[1]
# hash genes -> TEs
gene_tes = te.hash_genes_repeats(gtf_file, options.te_gff, gene_key='transcript_id', add_star=True, stranded=True)
# create a fake family for unrepetitive genes
for line in open(gtf_file):
a = line.split('\t')
gene_id = gff.gtf_kv(a[8])['transcript_id']
if not gene_id in gene_tes:
gene_tes[gene_id] = set([('-','-','*')])
# get diffs stats
gene_diffs, te_diffs = get_diff_stats(diff_file, gene_tes)
# clean plot directory
if os.path.isdir(options.out_dir):
shutil.rmtree(options.out_dir)
os.mkdir(options.out_dir)
# stats
table_lines, pvals = compute_stats(te_diffs, gene_diffs, options.out_dir)
# perform multiple hypothesis correction
qvals = fdr.ben_hoch(pvals)
table_out = open('%s/table.txt' % options.out_dir, 'w')
for i in range(len(table_lines)):
print >> table_out, '%s %10.2e' % (table_lines[i],qvals[i])
table_out.close()
def main():
usage = 'usage: %prog [options] <gtf> <diff>'
parser = OptionParser(usage)
parser.add_option('-o', dest='out_dir', default='te_diff_regress', help='Output directory to print regression summaries [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tp.gff'%os.environ['MASK'])
parser.add_option('-m', dest='max_stat', default=None, type='float', help='Maximum stat for plotting [Default: %default]')
parser.add_option('-s', dest='spread_factor', default=None, type='float', help='Allow multiplicative factor between the shortest and longest transcripts, used to filter [Default: %default]')
parser.add_option('-l', dest='spread_lower', default=None, type='float', help='Allow multiplicative factor between median and shortest transcripts [Defafult: %default]')
parser.add_option('-u', dest='spread_upper', default=None, type='float', help='Allow multiplicative factor between median and longest transcripts [Defafult: %default]')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide .gtf and .diff files')
else:
ref_gtf = args[0]
diff_file = args[1]
# clean output directory
if os.path.isdir(options.out_dir):
shutil.rmtree(options.out_dir)
os.mkdir(options.out_dir)
if options.spread_factor or options.spread_lower or options.spread_upper:
# filter for similar length
if options.spread_factor:
options.spread_lower = math.sqrt(options.spread_factor)
options.spread_upper = math.sqrt(options.spread_factor)
spread_gtf = '%s/spread_factor.gtf' % options.out_dir
gff.length_filter(ref_gtf, spread_gtf, options.spread_lower, options.spread_lower, verbose=True)
ref_gtf = spread_gtf
# hash genes -> TEs
gene_tes = te.hash_genes_repeats(ref_gtf, options.te_gff, gene_key='transcript_id', add_star=True, stranded=True)
# hash diffs stats
gene_diffs = cuffdiff.hash_diff(diff_file, stat='fold', max_stat=options.max_stat, sample_first='input')
table_lines = []
pvals = []
for spair in gene_diffs:
sample1, sample2 = spair
# construct data frame
gene_list = list(set(gene_tes.keys()) & set(gene_diffs[spair].keys()))
df = pd.DataFrame({'diff': [gene_diffs[spair][gene_id] for gene_id in gene_list]})
covariate_str = ''
for fam in regression_tes:
te_key = '%s_fwd' % fam.replace('/','_').replace('-','')
df[te_key] = [1.0*(('*',fam,'+') in gene_tes.get(gene_id,[])) for gene_id in gene_list]
if len(covariate_str) == 0:
covariate_str = te_key
else:
covariate_str += ' + %s' % te_key
te_key = '%s_rev' % fam.replace('/','_').replace('-','')
df[te_key] = [1.0*(('*',fam,'-') in gene_tes.get(gene_id,[])) for gene_id in gene_list]
covariate_str += ' + %s' % te_key
# regress
mod = smf.ols(formula='diff ~ %s' % covariate_str, data=df).fit()
# output model
mod_out = open('%s/%s-%s.txt' % (options.out_dir, sample1, sample2), 'w')
print >> mod_out, mod.summary()
mod_out.close()
# save table lines
for fam in regression_tes:
te_key = '%s_fwd' % fam.replace('/','_').replace('-','')
cols = (fam, '+', sample1, sample2, sum(df[te_key]), mod.params[te_key], mod.tvalues[te_key], mod.pvalues[te_key]/0.5)
table_lines.append('%-17s %1s %-10s %-10s %6d %8.3f %8.3f %10.2e' % cols)
pvals.append(cols[-1])
te_key = '%s_rev' % fam.replace('/','_').replace('-','')
cols = (fam, '-', sample1, sample2, sum(df[te_key]), mod.params[te_key], mod.tvalues[te_key], mod.pvalues[te_key]/0.5)
table_lines.append('%-17s %1s %-10s %-10s %6d %8.3f %8.3f %10.2e' % cols)
pvals.append(cols[-1])
# perform multiple hypothesis correction
qvals = fdr.ben_hoch(pvals)
table_out = open('%s/table.txt' % options.out_dir, 'w')
for i in range(len(table_lines)):
print >> table_out, '%s %10.2e' % (table_lines[i],qvals[i])
table_out.close()
if options.spread_factor or options.spread_lower or options.spread_upper:
os.remove(spread_gtf)
def main():
usage = 'usage: %prog [options] <gtf> <diff>'
parser = OptionParser(usage)
parser.add_option('-o', dest='out_dir', default='te_diff_regress', help='Output directory to print regression summaries [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tpf.gff'%os.environ['MASK'])
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide .gtf and .diff files')
else:
gtf_file = args[0]
diff_file = args[1]
# hash genes -> TEs
gene_tes = te.hash_genes_repeats(gtf_file, options.te_gff, gene_key='transcript_id', add_star=True, stranded=True)
# hash diffs stats
gene_diffs = hash_diff(diff_file)
# clean output directory
if os.path.isdir(options.out_dir):
shutil.rmtree(options.out_dir)
os.mkdir(options.out_dir)
table_lines = []
pvals = []
for spair in gene_diffs:
sample1, sample2 = spair
# construct data frame
gene_list = list(set(gene_tes.keys()) & set(gene_diffs[spair].keys()))
df = pd.DataFrame({'diff': [gene_diffs[spair][gene_id] for gene_id in gene_list]})
covariate_str = ''
for fam in regression_tes:
te_key = '%s_fwd' % fam.replace('/','_').replace('-','')
df[te_key] = [1.0*(('*',fam,'+') in gene_tes.get(gene_id,[])) for gene_id in gene_list]
if len(covariate_str) == 0:
covariate_str = te_key
else:
covariate_str += ' + %s' % te_key
te_key = '%s_rev' % fam.replace('/','_').replace('-','')
df[te_key] = [1.0*(('*',fam,'-') in gene_tes.get(gene_id,[])) for gene_id in gene_list]
covariate_str += ' + %s' % te_key
# regress
mod = smf.ols(formula='diff ~ %s' % covariate_str, data=df).fit()
# output model
mod_out = open('%s/%s-%s.txt' % (options.out_dir, sample1, sample2), 'w')
print >> mod_out, mod.summary()
mod_out.close()
# save table lines
for fam in regression_tes:
te_key = '%s_fwd' % fam.replace('/','_').replace('-','')
cols = (fam, '+', sample1, sample2, sum(df[te_key]), mod.params[te_key], mod.tvalues[te_key], mod.pvalues[te_key]/0.5)
table_lines.append('%-17s %1s %-10s %-10s %6d %8.3f %8.3f %10.2e' % cols)
pvals.append(cols[-1])
te_key = '%s_rev' % fam.replace('/','_').replace('-','')
cols = (fam, '-', sample1, sample2, sum(df[te_key]), mod.params[te_key], mod.tvalues[te_key], mod.pvalues[te_key]/0.5)
table_lines.append('%-17s %1s %-10s %-10s %6d %8.3f %8.3f %10.2e' % cols)
pvals.append(cols[-1])
# perform multiple hypothesis correction
qvals = fdr.ben_hoch(pvals)
table_out = open('%s/table.txt' % options.out_dir, 'w')
for i in range(len(table_lines)):
print >> table_out, '%s %10.2e' % (table_lines[i],qvals[i])
table_out.close()
