def main():
usage = 'usage: %prog [options] <fpkm1_file> <fpkm2_file>'
parser = OptionParser(usage)
parser.add_option('-g', dest='gtf')
parser.add_option('-o', dest='out_dir', default='.')
parser.add_option('-p', dest='pseudocount', default=0.125, type='float')
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide two diff files')
else:
fpkm1_file = args[0]
fpkm2_file = args[1]
cuff1 = cufflinks.fpkm_tracking(fpkm1_file)
cuff2 = cufflinks.fpkm_tracking(fpkm2_file)
gtf_genes = set()
if options.gtf:
gtf_genes = gff.gtf_gene_set(options.gtf)
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
for sample in cuff1.experiments:
# scatter plot fpkm
df = {'fpkm1': [], 'fpkm2': []}
for i in range(len(cuff1.genes)):
if len(gtf_genes) == 0 or cuff1.genes[i] in gtf_genes:
fpkm1 = cuff1.gene_expr_exp(i, sample)
fpkm2 = cuff2.gene_expr_exp(i, sample)
if not math.isnan(fpkm1) and not math.isnan(fpkm2):
df['fpkm1'].append(math.log(options.pseudocount + fpkm1,
2))
df['fpkm2'].append(math.log(options.pseudocount + fpkm2,
2))
r_script = '%s/fpkm_fpkm_scatter.r' % os.environ['RDIR']
out_pdf = '%s/%s_scatter.pdf' % (options.out_dir, sample)
ggplot.plot(r_script, df, [out_pdf])
# compute correlation
cor, p = spearmanr(df['fpkm1'], df['fpkm2'])
report_out = open('%s/%s_report.txt' % (options.out_dir, sample), 'w')
print >> report_out, 'Spearman correlation: %f (%e)' % (cor, p)
report_out.close()
def main():
usage = 'usage: %prog [options] <fpkm1_file> <fpkm2_file>'
parser = OptionParser(usage)
parser.add_option('-g', dest='gtf')
parser.add_option('-o', dest='out_dir', default='.')
parser.add_option('-p', dest='pseudocount', default=0.125, type='float')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide two diff files')
else:
fpkm1_file = args[0]
fpkm2_file = args[1]
cuff1 = cufflinks.fpkm_tracking(fpkm1_file)
cuff2 = cufflinks.fpkm_tracking(fpkm2_file)
gtf_genes = set()
if options.gtf:
gtf_genes = gff.gtf_gene_set(options.gtf)
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
for sample in cuff1.experiments:
# scatter plot fpkm
df = {'fpkm1':[], 'fpkm2':[]}
for i in range(len(cuff1.genes)):
if len(gtf_genes) == 0 or cuff1.genes[i] in gtf_genes:
fpkm1 = cuff1.gene_expr_exp(i, sample)
fpkm2 = cuff2.gene_expr_exp(i, sample)
if not math.isnan(fpkm1) and not math.isnan(fpkm2):
df['fpkm1'].append(math.log(options.pseudocount+fpkm1,2))
df['fpkm2'].append(math.log(options.pseudocount+fpkm2,2))
r_script = '%s/fpkm_fpkm_scatter.r' % os.environ['RDIR']
out_pdf = '%s/%s_scatter.pdf' % (options.out_dir, sample)
ggplot.plot(r_script, df, [out_pdf])
# compute correlation
cor, p = spearmanr(df['fpkm1'], df['fpkm2'])
report_out = open('%s/%s_report.txt' % (options.out_dir,sample), 'w')
print >> report_out, 'Spearman correlation: %f (%e)' % (cor, p)
report_out.close()
def main():
usage = 'usage: %prog [options] <peaks gff> <diff>'
parser = OptionParser(usage)
parser.add_option('-c', dest='clip_fpkm_file', help='Control FPKM tracking file')
parser.add_option('-g', dest='ref_gtf', default='%s/gencode.v18.annotation.gtf'%os.environ['GENCODE'])
parser.add_option('--ggplot', dest='ggplot_script', default='%s/peaks_diff_compare.r'%os.environ['RDIR'], help='Script to make plots with [Default: %default]')
parser.add_option('-m', dest='max_stat', default=10, type='float', help='Max cuffdiff stat [Default: %default]')
parser.add_option('-o', dest='output_pre', default='', help='Output prefix [Default: %default]')
parser.add_option('-r', dest='rbp', default='RBP', help='RBP name [Default: %default]')
parser.add_option('-s', dest='single_gene_loci', default=False, action='store_true', help='Only use single gene loci [Default: %default]')
parser.add_option('-t', dest='test_stat', default=False, action='store_true', help='Use test statistic rather than fold change [Default: %default]')
parser.add_option('--sample1', dest='sample1', help='Sample_1 name in cuffdiff')
parser.add_option('--sample2', dest='sample2', help='Sample_2 name in cuffdiff')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide peaks GFF and .diff file')
else:
peaks_gff = args[0]
diff_file = args[1]
##################################################
# process GTF
##################################################
if options.single_gene_loci:
single_gtf_fd, single_gtf_file = filter_single(options.ref_gtf)
options.ref_gtf = single_gtf_file
gtf_genes = gff.gtf_gene_set(options.ref_gtf)
##################################################
# collect CLIP peak bound genes
##################################################
peak_genes = set()
p = subprocess.Popen('intersectBed -s -u -a %s -b %s' % (options.ref_gtf, peaks_gff), shell=True, stdout=subprocess.PIPE)
for line in p.stdout:
peak_genes.add(gff.gtf_kv(line.split('\t')[8])['gene_id'])
p.communicate()
# find expressed genes in peak calls
silent_genes = set()
if options.clip_fpkm_file:
silent_genes = find_silent(options.clip_fpkm_file)
##################################################
# collect RIP stats
##################################################
if options.test_stat:
rip_fold, rip_bound = ripseq.hash_rip(diff_file, just_ok = True, use_fold=False, max_stat=options.max_stat, one_rbp=True)
else:
rip_fold, rip_bound = ripseq.hash_rip(diff_file, use_fold=True, max_stat=options.max_stat, one_rbp=True)
rip_fold = ripseq.hash_rip_fold(diff_file, min_fpkm=0.125, max_fold=10, one_rbp=True)
##################################################
# plot bound and unbound distributions
##################################################
# construct data frame
df_dict = {'Gene':[], 'CLIP':[], 'RIP':[]}
for gene_id in rip_fold:
if gene_id in gtf_genes and (len(silent_genes) == 0 or gene_id not in silent_genes):
df_dict['Gene'].append(gene_id)
df_dict['RIP'].append(rip_fold[gene_id])
if gene_id in peak_genes:
df_dict['CLIP'].append('Bound')
else:
df_dict['CLIP'].append('Unbound')
ggplot.plot(options.ggplot_script, df_dict, [options.output_pre, options.rbp, options.test_stat])
##################################################
# compute stats on bound and unbound distributions
##################################################
bound_fold = [df_dict['RIP'][i] for i in range(len(df_dict['RIP'])) if df_dict['CLIP'][i] == 'Bound']
unbound_fold = [df_dict['RIP'][i] for i in range(len(df_dict['RIP'])) if df_dict['CLIP'][i] == 'Unbound']
# perform statistical test
z, p = stats.mannwhitneyu(bound_fold, unbound_fold)
stats_out = open('%s_stats.txt' % options.output_pre, 'w')
cols = (options.rbp, len(bound_fold), stats.mean(bound_fold), len(unbound_fold), stats.mean(unbound_fold), z, p)
print >> stats_out, '%-10s %5d %6.2f %5d %6.2f %6.2f %9.2e' % cols
stats_out.close()
##################################################
# plot venn diagram
##################################################
rip_genes = set([df_dict['Gene'][i] for i in range(len(df_dict['Gene'])) if rip_bound.get(df_dict['Gene'][i],False)])
clip_only = len(peak_genes - rip_genes)
rip_only = len(rip_genes - peak_genes)
both = len(peak_genes & rip_genes)
if options.clip_fpkm_file:
print >> sys.stderr, 'Ignoring silent genes for hypergeometric test'
# k is x
# K is n
# N is M
# n is N
# hypergeom.sf(x, M, n, N, loc=0)
p1 = hypergeom.sf(both-1, len(gtf_genes), len(peak_genes), len(rip_genes))
p2 = hypergeom.sf(both-1, len(gtf_genes), len(rip_genes), len(peak_genes))
hyper_out = open('%s_hyper.txt' % options.output_pre, 'w')
cols = (p1, p2, both, clip_only, rip_only, len(peak_genes), len(rip_genes), len(gtf_genes))
print >> hyper_out, '%7.2e %7.2e %5d %5d %5d %5d %5d %5d' % cols
hyper_out.close()
if clip_only > 0 and rip_only > 0:
plt.figure()
# venn_diag = venn2(subsets=(clip_only, rip_only, both), set_labels=['CLIP', 'fRIP'], set_colors=['#e41a1c', '#377eb8'])
# venn_diag = venn2(subsets=(clip_only, rip_only, both), set_labels=['CLIP', 'fRIP'], set_colors=['#e41a1c', '#1ae47d'])
venn_diag = venn2(subsets=(clip_only, rip_only, both), set_labels=['CLIP', 'fRIP'], set_colors=['#e41a1c', '#A1A838'])
plt.savefig('%s_venn.pdf' % options.output_pre)
##################################################
# clean
##################################################
if options.single_gene_loci:
os.close(single_gtf_fd)
os.remove(single_gtf_file)
def main():
usage = 'usage: %prog [options] <peaks gff> <diff>'
parser = OptionParser(usage)
parser.add_option('-c',
dest='clip_fpkm_file',
help='Control FPKM tracking file')
parser.add_option('-g',
dest='ref_gtf',
default='%s/gencode.v18.annotation.gtf' %
os.environ['GENCODE'])
parser.add_option('--ggplot',
dest='ggplot_script',
default='%s/peaks_diff_compare.r' % os.environ['RDIR'],
help='Script to make plots with [Default: %default]')
parser.add_option('-m',
dest='max_stat',
default=10,
type='float',
help='Max cuffdiff stat [Default: %default]')
parser.add_option('-o',
dest='output_pre',
default='',
help='Output prefix [Default: %default]')
parser.add_option('-r',
dest='rbp',
default='RBP',
help='RBP name [Default: %default]')
parser.add_option('-s',
dest='single_gene_loci',
default=False,
action='store_true',
help='Only use single gene loci [Default: %default]')
parser.add_option(
'-t',
dest='test_stat',
default=False,
action='store_true',
help='Use test statistic rather than fold change [Default: %default]')
parser.add_option('--sample1',
dest='sample1',
help='Sample_1 name in cuffdiff')
parser.add_option('--sample2',
dest='sample2',
help='Sample_2 name in cuffdiff')
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide peaks GFF and .diff file')
else:
peaks_gff = args[0]
diff_file = args[1]
##################################################
# process GTF
##################################################
if options.single_gene_loci:
single_gtf_fd, single_gtf_file = filter_single(options.ref_gtf)
options.ref_gtf = single_gtf_file
gtf_genes = gff.gtf_gene_set(options.ref_gtf)
##################################################
# collect CLIP peak bound genes
##################################################
peak_genes = set()
p = subprocess.Popen('intersectBed -s -u -a %s -b %s' %
(options.ref_gtf, peaks_gff),
shell=True,
stdout=subprocess.PIPE)
for line in p.stdout:
peak_genes.add(gff.gtf_kv(line.split('\t')[8])['gene_id'])
p.communicate()
# find expressed genes in peak calls
silent_genes = set()
if options.clip_fpkm_file:
silent_genes = find_silent(options.clip_fpkm_file)
##################################################
# collect RIP stats
##################################################
if options.test_stat:
rip_fold, rip_bound = ripseq.hash_rip(diff_file,
just_ok=True,
use_fold=False,
max_stat=options.max_stat,
one_rbp=True)
else:
rip_fold, rip_bound = ripseq.hash_rip(diff_file,
use_fold=True,
max_stat=options.max_stat,
one_rbp=True)
rip_fold = ripseq.hash_rip_fold(diff_file,
min_fpkm=0.125,
max_fold=10,
one_rbp=True)
# TEMP: print bound genes
# genes_out = open('%s_genes.txt' % options.output_pre, 'w')
# for gene_id in rip_bound:
# if rip_bound[gene_id]:
# print >> genes_out, gene_id, rip_fold[gene_id]
# genes_out.close()
##################################################
# plot bound and unbound distributions
##################################################
# construct data frame
df_dict = {'Gene': [], 'CLIP': [], 'RIP': []}
for gene_id in rip_fold:
if gene_id in gtf_genes and (len(silent_genes) == 0
or gene_id not in silent_genes):
df_dict['Gene'].append(gene_id)
df_dict['RIP'].append(rip_fold[gene_id])
if gene_id in peak_genes:
df_dict['CLIP'].append('Bound')
else:
df_dict['CLIP'].append('Unbound')
ggplot.plot(options.ggplot_script, df_dict,
[options.output_pre, options.rbp, options.test_stat])
##################################################
# compute stats on bound and unbound distributions
##################################################
bound_fold = [
df_dict['RIP'][i] for i in range(len(df_dict['RIP']))
if df_dict['CLIP'][i] == 'Bound'
]
unbound_fold = [
df_dict['RIP'][i] for i in range(len(df_dict['RIP']))
if df_dict['CLIP'][i] == 'Unbound'
]
# perform statistical test
z, p = stats.mannwhitneyu(bound_fold, unbound_fold)
stats_out = open('%s_stats.txt' % options.output_pre, 'w')
cols = (options.rbp, len(bound_fold), stats.mean(bound_fold),
len(unbound_fold), stats.mean(unbound_fold), z, p)
print >> stats_out, '%-10s %5d %6.2f %5d %6.2f %6.2f %9.2e' % cols
stats_out.close()
##################################################
# plot venn diagram
##################################################
rip_genes = set([
df_dict['Gene'][i] for i in range(len(df_dict['Gene']))
if rip_bound.get(df_dict['Gene'][i], False)
])
clip_only = len(peak_genes - rip_genes)
rip_only = len(rip_genes - peak_genes)
both = len(peak_genes & rip_genes)
if options.clip_fpkm_file:
print >> sys.stderr, 'Ignoring silent genes for hypergeometric test'
# k is x
# K is n
# N is M
# n is N
# hypergeom.sf(x, M, n, N, loc=0)
p1 = hypergeom.sf(both - 1, len(gtf_genes), len(peak_genes),
len(rip_genes))
p2 = hypergeom.sf(both - 1, len(gtf_genes), len(rip_genes),
len(peak_genes))
hyper_out = open('%s_hyper.txt' % options.output_pre, 'w')
cols = (p1, p2, both, clip_only, rip_only, len(peak_genes), len(rip_genes),
len(gtf_genes))
print >> hyper_out, '%7.2e %7.2e %5d %5d %5d %5d %5d %5d' % cols
hyper_out.close()
if clip_only > 0 and rip_only > 0:
plt.figure()
# venn_diag = venn2(subsets=(clip_only, rip_only, both), set_labels=['CLIP', 'fRIP'], set_colors=['#e41a1c', '#377eb8'])
# venn_diag = venn2(subsets=(clip_only, rip_only, both), set_labels=['CLIP', 'fRIP'], set_colors=['#e41a1c', '#1ae47d'])
venn_diag = venn2(subsets=(clip_only, rip_only, both),
set_labels=['CLIP', 'fRIP'],
set_colors=['#e41a1c', '#A1A838'])
plt.savefig('%s_venn.pdf' % options.output_pre)
##################################################
# clean
##################################################
if options.single_gene_loci:
os.close(single_gtf_fd)
os.remove(single_gtf_file)
def main():
usage = 'usage: %prog [options] <diff1_file> <diff2_file>'
parser = OptionParser(usage)
parser.add_option('-s', dest='stat', default='test_stat')
parser.add_option('-g', dest='genes_gtf', default=None)
parser.add_option('-m', dest='min_fpkm', default=0, type='float')
parser.add_option('-o', dest='out_dir', default='.')
parser.add_option('-p', dest='pseudocount', default=0.125, type='float')
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide two diff files')
else:
diff1_file = args[0]
diff2_file = args[1]
gtf_genes = None
if options.genes_gtf:
gtf_genes = gff.gtf_gene_set(options.genes_gtf)
diff1_stats = cuffdiff.hash_stat(diff1_file,
stat=options.stat,
min_fpkm=options.min_fpkm,
pseudocount=options.pseudocount,
gene_set=gtf_genes)
diff1_sig = cuffdiff.hash_sig(diff1_file, gene_set=gtf_genes)
diff2_stats = cuffdiff.hash_stat(diff2_file,
stat=options.stat,
min_fpkm=options.min_fpkm,
pseudocount=options.pseudocount,
gene_set=gtf_genes)
diff2_sig = cuffdiff.hash_sig(diff2_file, gene_set=gtf_genes)
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
for diff_key in diff1_stats:
sample1, sample2 = diff_key
gene_stats1 = diff1_stats[diff_key]
gene_sig1 = diff1_sig[diff_key]
gene_stats2 = diff2_stats[diff_key]
gene_sig2 = diff2_sig[diff_key]
report_out = open(
'%s/%s-%s_report.txt' % (options.out_dir, sample1, sample2), 'w')
# compare numbers of genes quantified
common_genes = set(gene_stats1.keys()) & set(gene_stats2.keys())
print >> report_out, 'Genes quantified'
print >> report_out, '%s\t%d' % (diff1_file, len(gene_stats1))
print >> report_out, '%s\t%d' % (diff2_file, len(gene_stats2))
print >> report_out, 'Common\t%d' % len(common_genes)
print >> report_out, ''
up1 = set([gene_id for gene_id in gene_sig1 if gene_sig1[gene_id]])
up2 = set([gene_id for gene_id in gene_sig2 if gene_sig2[gene_id]])
print >> report_out, 'Genes upregulated'
print >> report_out, '%s\t%d' % (diff1_file, len(up1))
print >> report_out, '%s\t%d' % (diff2_file, len(up2))
print >> report_out, 'Common\t%d' % len(up1 & up2)
print >> report_out, ''
down1 = set(
[gene_id for gene_id in gene_sig1 if not gene_sig1[gene_id]])
down2 = set(
[gene_id for gene_id in gene_sig2 if not gene_sig2[gene_id]])
print >> report_out, 'Genes downregulated'
print >> report_out, '%s\t%d' % (diff1_file, len(down1))
print >> report_out, '%s\t%d' % (diff2_file, len(down2))
print >> report_out, 'Common\t%d' % len(down1 & down2)
print >> report_out, ''
# scatter plot test stat
df = {'diff1': [], 'diff2': []}
for gene_id in common_genes:
df['diff1'].append(gene_stats1[gene_id])
df['diff2'].append(gene_stats2[gene_id])
r_script = '%s/diff_diff_scatter.r' % os.environ['RDIR']
out_pdf = '%s/%s-%s_scatter.pdf' % (options.out_dir, sample1, sample2)
ggplot.plot(r_script, df, [out_pdf], df_file='%s.df' % out_pdf[:-4])
# compute correlation
cor, p = spearmanr(df['diff1'], df['diff2'])
print >> report_out, 'Spearman correlation: %f (%f)' % (cor, p)
cor, p = pearsonr(df['diff1'], df['diff2'])
print >> report_out, 'Pearson correlation: %f (%f)' % (cor, p)
report_out.close()
# plot test_stat versus test_stat difference
df = {'minus': [], 'avg': []}
for gene_id in common_genes:
df['minus'].append(gene_stats1[gene_id] - gene_stats2[gene_id])
df['avg'].append(0.5 * gene_stats1[gene_id] +
0.5 * gene_stats2[gene_id])
r_script = '%s/diff_diff_ma.r' % os.environ['RDIR']
out_pdf = '%s/%s-%s_ma.pdf' % (options.out_dir, sample1, sample2)
ggplot.plot(r_script, df, [out_pdf])
def main():
usage = 'usage: %prog [options] <diff1_file> <diff2_file>'
parser = OptionParser(usage)
parser.add_option('-s', dest='stat', default='test_stat')
parser.add_option('-g', dest='genes_gtf', default=None)
parser.add_option('-m', dest='min_fpkm', default=0, type='float')
parser.add_option('-o', dest='out_dir', default='.')
parser.add_option('-p', dest='pseudocount', default=0.125, type='float')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide two diff files')
else:
diff1_file = args[0]
diff2_file = args[1]
gtf_genes = None
if options.genes_gtf:
gtf_genes = gff.gtf_gene_set(options.genes_gtf)
diff1_stats = cuffdiff.hash_stat(diff1_file, stat=options.stat, min_fpkm=options.min_fpkm, pseudocount=options.pseudocount, gene_set=gtf_genes)
diff1_sig = cuffdiff.hash_sig(diff1_file, gene_set=gtf_genes)
diff2_stats = cuffdiff.hash_stat(diff2_file, stat=options.stat, min_fpkm=options.min_fpkm, pseudocount=options.pseudocount, gene_set=gtf_genes)
diff2_sig = cuffdiff.hash_sig(diff2_file, gene_set=gtf_genes)
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
for diff_key in diff1_stats:
sample1, sample2 = diff_key
gene_stats1 = diff1_stats[diff_key]
gene_sig1 = diff1_sig[diff_key]
gene_stats2 = diff2_stats[diff_key]
gene_sig2 = diff2_sig[diff_key]
report_out = open('%s/%s-%s_report.txt' % (options.out_dir,sample1,sample2), 'w')
# compare numbers of genes quantified
common_genes = set(gene_stats1.keys()) & set(gene_stats2.keys())
print >> report_out, 'Genes quantified'
print >> report_out, '%s\t%d' % (diff1_file,len(gene_stats1))
print >> report_out, '%s\t%d' % (diff2_file,len(gene_stats2))
print >> report_out, 'Common\t%d' % len(common_genes)
print >> report_out, ''
up1 = set([gene_id for gene_id in gene_sig1 if gene_sig1[gene_id]])
up2 = set([gene_id for gene_id in gene_sig2 if gene_sig2[gene_id]])
print >> report_out, 'Genes upregulated'
print >> report_out, '%s\t%d' % (diff1_file,len(up1))
print >> report_out, '%s\t%d' % (diff2_file,len(up2))
print >> report_out, 'Common\t%d' % len(up1 & up2)
print >> report_out, ''
down1 = set([gene_id for gene_id in gene_sig1 if not gene_sig1[gene_id]])
down2 = set([gene_id for gene_id in gene_sig2 if not gene_sig2[gene_id]])
print >> report_out, 'Genes downregulated'
print >> report_out, '%s\t%d' % (diff1_file,len(down1))
print >> report_out, '%s\t%d' % (diff2_file,len(down2))
print >> report_out, 'Common\t%d' % len(down1 & down2)
print >> report_out, ''
# scatter plot test stat
df = {'diff1':[], 'diff2':[]}
for gene_id in common_genes:
df['diff1'].append(gene_stats1[gene_id])
df['diff2'].append(gene_stats2[gene_id])
r_script = '%s/diff_diff_scatter.r' % os.environ['RDIR']
out_pdf = '%s/%s-%s_scatter.pdf' % (options.out_dir, sample1, sample2)
ggplot.plot(r_script, df, [out_pdf], df_file='%s.df'%out_pdf[:-4])
# compute correlation
cor, p = spearmanr(df['diff1'], df['diff2'])
print >> report_out, 'Spearman correlation: %f (%f)' % (cor,p)
cor, p = pearsonr(df['diff1'], df['diff2'])
print >> report_out, 'Pearson correlation: %f (%f)' % (cor,p)
report_out.close()
# plot test_stat versus test_stat difference
df = {'minus':[], 'avg':[]}
for gene_id in common_genes:
df['minus'].append(gene_stats1[gene_id] - gene_stats2[gene_id])
df['avg'].append(0.5*gene_stats1[gene_id] + 0.5*gene_stats2[gene_id])
r_script = '%s/diff_diff_ma.r' % os.environ['RDIR']
out_pdf = '%s/%s-%s_ma.pdf' % (options.out_dir, sample1, sample2)
ggplot.plot(r_script, df, [out_pdf])
