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)

# intersect with self and compute overlap sets

#p = subprocess.Popen('intersectBed -sorted -wo -s -a %s -b %s' % (ref_gtf, ref_gtf), shell=True, stdout=subprocess.PIPE)

p = subprocess.Popen('intersectBed -wo -s -a %s -b %s' % (ref_gtf, ref_gtf), shell=True, stdout=subprocess.PIPE)

# computer overlaps

gene_overlaps = {}

for line in p.stdout:

a = line.split('\t')

gid1 = gff.gtf_kv(a[8])['gene_id']

gid2 = gff.gtf_kv(a[17])['gene_id']

if gid1 != gid2:

gene_overlaps.setdefault(gid1,set()).add(gid2)

gene_overlaps.setdefault(gid2,set()).add(gid1)

p.communicate()

# filter overlapping genes out

single_gtf_fd, single_gtf_file = tempfile.mkstemp()

single_gtf_out = open(single_gtf_file, 'w')

for line in open(ref_gtf):

a = line.split('\t')

gene_id = gff.gtf_kv(a[8])['gene_id']

if gene_id not in gene_overlaps:

print >> single_gtf_out, line,

single_gtf_out.close()

# get fpkms (possibly from an isoform file)

gene_fpkms = {}

control_fpkm_in = open(clip_fpkm_file)

control_fpkm_in.readline()

for line in control_fpkm_in:

a = line.split('\t')

gene_id = a[3]

fpkm = float(a[9])

gene_fpkms[gene_id] = gene_fpkms.get(gene_id,0) + fpkm

control_fpkm_in.close()

silent_genes = set()

for gene_id in gene_fpkms:

if gene_fpkms[gene_id] < silent_fpkm:

silent_genes.add(gene_id)