def mode_2(exp_matrix):
#remember value of bedgraph, ugly way
value = {}
for regions in exp_matrix.get_regionsets():
for region in regions:
value[(region.chrom, region.initial, region.final)] = region.data
for region in exp_matrix.get_regionsets():
f = open("region_" + str(region.name) + ".data", 'w')
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, gene_peaks_mapping = region_set.filter_by_gene_association(region.fileName, None, gene_file, genome_file, threshDist=2000)
for k in gene_peaks_mapping.keys():
chr, raw_positions = k.split(':')
start, end = map(lambda x: int(x), raw_positions.split('-'))
#if peak is not assigned, an empty string occurs
if "" in gene_peaks_mapping[k]:
gene_peaks_mapping[k].remove("")
list = 'NA' if not gene_peaks_mapping[k] else ','.join(gene_peaks_mapping[k])
print(chr, start, end, value[(chr, start, end)], list, sep='\t', file = f)
f.close()
def mode_3(exp_matrix):
#remember value of bedgraph, ugly way
score = {}
for regions in exp_matrix.get_regionsets():
for region in regions:
score[(region.chrom + ':' + str(region.initial) + '-' + str(region.final))] = region.data
for region in exp_matrix.get_regionsets():
f = open("region_" + str(region.name) + ".data", 'w')
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, gene_peaks_mapping = region_set.filter_by_gene_association(region.fileName, None, gene_file, genome_file, threshDist=2000)
avg_score = {} #score per peak
genes = {}
for peak, gene_list in gene_peaks_mapping.items():
for gen in gene_list: #reverse mapping peak -> gene to gene -> peak
if not gen:
continue
genes[gen] = genes.get(gen, set())
genes[gen].add(peak)
avg_score[gen] = avg_score.get(gen, [])
avg_score[gen].append(score[peak]) #join all scores of peaks assigned to a gen
for gen in genes.keys():
avg = sum(map(lambda x: float(x), avg_score[gen]))/ float(len(avg_score[gen]))
print(gen, avg, ", ".join(str(t) for t in genes[gen]), sep='\t', file = f)
f.close()
def mode_3(exp_matrix):
#remember value of bedgraph, ugly way
score = {}
for regions in exp_matrix.get_regionsets():
for region in regions:
score[(region.chrom + ':' + str(region.initial) + '-' + str(region.final))] = region.data
for region in exp_matrix.get_regionsets():
f = open("region_" + str(region.name) + ".data", 'w')
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, gene_peaks_mapping = region_set.filter_by_gene_association(region.fileName, None, gene_file, genome_file, thresh_dist=2000)
avg_score = {} #score per peak
genes = {}
for peak, gene_list in list(gene_peaks_mapping.items()):
for gen in gene_list: #reverse mapping peak -> gene to gene -> peak
if not gen:
continue
genes[gen] = genes.get(gen, set())
genes[gen].add(peak)
avg_score[gen] = avg_score.get(gen, [])
avg_score[gen].append(score[peak]) #join all scores of peaks assigned to a gen
for gen in list(genes.keys()):
avg = sum([float(x) for x in avg_score[gen]])/ float(len(avg_score[gen]))
print(gen, avg, ", ".join(str(t) for t in genes[gen]), sep='\t', file = f)
f.close()
def mode_2(exp_matrix):
#remember value of bedgraph, ugly way
value = {}
for regions in exp_matrix.get_regionsets():
for region in regions:
value[(region.chrom, region.initial, region.final)] = region.data
for region in exp_matrix.get_regionsets():
f = open("region_" + str(region.name) + ".data", 'w')
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, gene_peaks_mapping = region_set.filter_by_gene_association(region.fileName, None, gene_file, genome_file, thresh_dist=2000)
for k in list(gene_peaks_mapping.keys()):
chr, raw_positions = k.split(':')
start, end = [int(x) for x in raw_positions.split('-')]
#if peak is not assigned, an empty string occurs
if "" in gene_peaks_mapping[k]:
gene_peaks_mapping[k].remove("")
list = 'NA' if not gene_peaks_mapping[k] else ','.join(gene_peaks_mapping[k])
print(chr, start, end, value[(chr, start, end)], list, sep='\t', file = f)
f.close()
def mode_1(exp_matrix):
for region in exp_matrix.get_regionsets():
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, _ = region_set.filter_by_gene_association(
region.fileName, None, gene_file, genome_file, thresh_dist=50000)
print('#number of mapped genes:', mappedGenes)
print(region.name + "\t" + ("\t".join(region_set.genes)))
allgenes.append(r.name)
allgenes=list(set(allgenes))
genesets=exps.get_genesets()
if len(sys.argv) > 3:
back=True
backGroundPeaks = sys.argv[3]
backBed=GenomicRegionSet("BACK")
backBed.read_bed(backGroundPeaks)
backBed=GenomicRegionSet("BACK")
backBed.read_bed(backGroundPeaks)
backUP=GenomicRegionSet("BACKUP")
[back_de_genes,back_de_peak_genes, back_mappedGenes, back_totalPeaks] = backUP.filter_by_gene_association(backGroundPeaks,genesets[0],geneFile,genomeFile)
prop_back=back_mappedGenes/float(len(allgenes))
for g in genesets:
for region in exps.get_regionsets():
bed = GenomicRegionSet("")
[degenes,de_peak_genes, mappedGenes, totalPeaks] = bed.filter_by_gene_association(region.fileName,g,geneFile,genomeFile)
#print degenes
#print bed.genes
a=de_peak_genes
b=degenes-de_peak_genes
c=back_mappedGenes-de_peak_genes
d=len(allgenes)-b-c-a
prop_de=de_peak_genes/float(degenes)
p= pvalue(a,b,c,d)
print region.name,g.name,a,b,c,d,degenes,mappedGenes,len(allgenes),prop_de,prop_back,prop_de/prop_back,p.right_tail,p.left_tail
def mode_1(exp_matrix):
for region in exp_matrix.get_regionsets():
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, _ = region_set.filter_by_gene_association(region.fileName, None, gene_file, genome_file, threshDist=50000)
print('#number of mapped genes:', mappedGenes)
print(region.name+"\t"+("\t".join(region_set.genes)))
allgenes = list(set(allgenes))
genesets = exps.get_genesets()
if len(sys.argv) > 3:
back = True
backGroundPeaks = sys.argv[3]
backBed = GenomicRegionSet("BACK")
backBed.read_bed(backGroundPeaks)
backBed = GenomicRegionSet("BACK")
backBed.read_bed(backGroundPeaks)
backUP = GenomicRegionSet("BACKUP")
[back_de_genes, back_de_peak_genes, back_mappedGenes,
back_totalPeaks] = backUP.filter_by_gene_association(backGroundPeaks,
genesets[0], geneFile,
genomeFile)
prop_back = back_mappedGenes / float(len(allgenes))
for g in genesets:
for region in exps.get_regionsets():
bed = GenomicRegionSet("")
[degenes, de_peak_genes, mappedGenes,
totalPeaks] = bed.filter_by_gene_association(region.fileName, g,
geneFile, genomeFile)
#print degenes
#print bed.genes
a = de_peak_genes
b = degenes - de_peak_genes
c = back_mappedGenes - de_peak_genes
d = len(allgenes) - b - c - a
print len(backBed), len(region)
br = backBed.random_subregions(len(region))
else:
br = region.random_regions('hg19',
total_size=len(region),
overlap_result=True,
overlap_input=True)
br.write_bed(str(j) + "random.bed")
for g in genesets:
#print region,g
bed = GenomicRegionSet("")
[degenes, de_peak_genes, mappedGenes, totalPeaks,
bla] = bed.filter_by_gene_association(region.fileName,
g.genes,
geneFile,
genomeFile,
threshDist=distance)
randomRes = []
#backBed=GenomicRegionSet("BACK")
#backBed.read_bed(backGroundPeaks)
for j, n in enumerate(range(randomize)):
backUP = GenomicRegionSet("BACKUP")
[
back_de_genes, back_de_peak_genes, back_mappedGenes,
back_totalPeaks, bla
] = backUP.filter_by_gene_association(str(j) + "random.bed",
g.genes,
geneFile,
genomeFile,
threshDist=distance)
print genesets
for region in exps.get_regionsets():
for j,n in enumerate(range(randomize)):
if backGroundPeaks:
print len(backBed), len(region)
br=backBed.random_subregions(len(region))
else:
br=region.random_regions('hg19',total_size=len(region),overlap_result=True, overlap_input=True)
br.write_bed(str(j)+"random.bed")
for g in genesets:
#print region,g
bed = GenomicRegionSet("")
[degenes,de_peak_genes, mappedGenes, totalPeaks,bla] = bed.filter_by_gene_association(region.fileName,g.genes,geneFile,genomeFile,threshDist=distance)
randomRes=[]
#backBed=GenomicRegionSet("BACK")
#backBed.read_bed(backGroundPeaks)
for j,n in enumerate(range(randomize)):
backUP=GenomicRegionSet("BACKUP")
[back_de_genes,back_de_peak_genes, back_mappedGenes, back_totalPeaks,bla] = backUP.filter_by_gene_association(str(j)+"random.bed",g.genes,geneFile,genomeFile,threshDist=distance)
randomRes.append(back_de_peak_genes)
#print str(j)+"random.bed"
randomRes=numpy.array(randomRes)
#print randomRes
a=de_peak_genes
m=numpy.mean(randomRes)
s=numpy.std(randomRes)
z=(a-m)/s
prop_de=de_peak_genes/float(degenes)