def main():
usage = 'usage: %prog [options] <gtf> <diff>'
parser = OptionParser(usage)
parser.add_option('-m', dest='max_stat', default=None, type='float', help='Maximum stat for plotting [Default: %default]')
parser.add_option('-o', dest='out_dir', default='te_diff', help='Output directory [Default: %default]')
parser.add_option('-c', dest='scale', default=1, type='float', help='CDF plot scale [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tp.gff'%os.environ['MASK'])
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 plot 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)
ref_gtf = spread_gtf
##################################################
# hash TEs -> genes
##################################################
te_genes = te.hash_repeats_genes(ref_gtf, options.te_gff, gene_key='transcript_id', add_star=True, stranded=True)
##################################################
# hash genes -> RIP diff
##################################################
gene_diff = cuffdiff.hash_diff(diff_file, stat='fold', max_stat=options.max_stat, sample_first='input')
##################################################
# compute stats and make plots
##################################################
table_lines, pvals = compute_stats(te_genes, gene_diff, ref_gtf, options.out_dir, options.scale)
# 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.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('-m',
dest='max_stat',
default=None,
type='float',
help='Maximum stat for plotting [Default: %default]')
parser.add_option('-o',
dest='out_dir',
default='te_diff',
help='Output directory [Default: %default]')
parser.add_option('-c',
dest='scale',
default=1,
type='float',
help='CDF plot scale [Default: %default]')
parser.add_option('-t',
dest='te_gff',
default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
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 plot 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)
ref_gtf = spread_gtf
##################################################
# hash TEs -> genes
##################################################
te_genes = te.hash_repeats_genes(ref_gtf,
options.te_gff,
gene_key='transcript_id',
add_star=True,
stranded=True)
##################################################
# hash genes -> RIP diff
##################################################
gene_diff = cuffdiff.hash_diff(diff_file,
stat='fold',
max_stat=options.max_stat,
sample_first='input')
##################################################
# compute stats and make plots
##################################################
table_lines, pvals = compute_stats(te_genes, gene_diff, ref_gtf,
options.out_dir, options.scale)
# 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('-c', dest='scale', default=1, type='float', help='Plot scale [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tp.gff'%os.environ['MASK'])
parser.add_option('-r', dest='orientation', default=False, action='store_true', help='Split TEs by orientation [Default: %default]')
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]
# make output directory
if not os.path.isdir(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 -> occurence num
gene_te_num = te.hash_genes_repeats_num(ref_gtf, options.te_gff, gene_key='transcript_id', add_star=True, stranded=options.orientation)
# 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
gene_list = list(set(gene_te_num.keys()) & set(gene_diffs[spair].keys()))
for fam in count_tes:
if options.orientation:
orients = ['+','-']
else:
orients = ['+']
for orient in orients:
# hash diff values by TE count
count_diff = []
for gene_id in gene_diffs[spair]:
if options.orientation:
count = gene_te_num.get(gene_id,{}).get(('*',fam,orient), 0)
else:
count = gene_te_num.get(gene_id,{}).get(('*',fam), 0)
while count >= len(count_diff):
count_diff.append([])
count_diff[count].append(gene_diffs[spair][gene_id])
df = {'TEs':[], 'stat_low':[], 'stat_mid':[], 'stat_hi':[]}
for c in range(len(count_diff)):
if len(count_diff[c]) > 12:
stat_low, stat_mid, stat_hi = stats.quantile(count_diff[c], [.25, .5, .75])
df['TEs'].append(c)
df['stat_low'].append(stat_low)
df['stat_mid'].append(stat_mid)
df['stat_hi'].append(stat_hi)
else:
break
if len(df['TEs']) > 1:
fam_plot = fam[fam.find('/')+1:]
if options.orientation:
out_pdf = '%s/%s-%s_%s_%s.pdf' % (options.out_dir, sample1, sample2, fam_plot, orient)
out_df = '%s/%s-%s_%s_%s.df' % (options.out_dir, sample1, sample2, fam_plot, orient)
else:
out_pdf = '%s/%s-%s_%s.pdf' % (options.out_dir, sample1, sample2, fam_plot)
out_df = '%s/%s-%s_%s.df' % (options.out_dir, sample1, sample2, fam_plot)
ggplot.plot('%s/te_diff_count.r' % os.environ['RDIR'], df, [out_pdf, options.scale], df_file=out_df)
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('-c',
dest='scale',
default=1,
type='float',
help='Plot scale [Default: %default]')
parser.add_option('-t',
dest='te_gff',
default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
parser.add_option('-r',
dest='orientation',
default=False,
action='store_true',
help='Split TEs by orientation [Default: %default]')
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]
# make output directory
if not os.path.isdir(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 -> occurence num
gene_te_num = te.hash_genes_repeats_num(ref_gtf,
options.te_gff,
gene_key='transcript_id',
add_star=True,
stranded=options.orientation)
# 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
gene_list = list(
set(gene_te_num.keys()) & set(gene_diffs[spair].keys()))
for fam in count_tes:
if options.orientation:
orients = ['+', '-']
else:
orients = ['+']
for orient in orients:
# hash diff values by TE count
count_diff = []
for gene_id in gene_diffs[spair]:
if options.orientation:
count = gene_te_num.get(gene_id, {}).get(
('*', fam, orient), 0)
else:
count = gene_te_num.get(gene_id, {}).get(('*', fam), 0)
while count >= len(count_diff):
count_diff.append([])
count_diff[count].append(gene_diffs[spair][gene_id])
df = {'TEs': [], 'stat_low': [], 'stat_mid': [], 'stat_hi': []}
for c in range(len(count_diff)):
if len(count_diff[c]) > 12:
stat_low, stat_mid, stat_hi = stats.quantile(
count_diff[c], [.25, .5, .75])
df['TEs'].append(c)
df['stat_low'].append(stat_low)
df['stat_mid'].append(stat_mid)
df['stat_hi'].append(stat_hi)
else:
break
if len(df['TEs']) > 1:
fam_plot = fam[fam.find('/') + 1:]
if options.orientation:
out_pdf = '%s/%s-%s_%s_%s.pdf' % (options.out_dir,
sample1, sample2,
fam_plot, orient)
out_df = '%s/%s-%s_%s_%s.df' % (options.out_dir,
sample1, sample2,
fam_plot, orient)
else:
out_pdf = '%s/%s-%s_%s.pdf' % (
options.out_dir, sample1, sample2, fam_plot)
out_df = '%s/%s-%s_%s.df' % (options.out_dir, sample1,
sample2, fam_plot)
ggplot.plot('%s/te_diff_count.r' % os.environ['RDIR'],
df, [out_pdf, options.scale],
df_file=out_df)
if options.spread_factor or options.spread_lower or options.spread_upper:
os.remove(spread_gtf)
