def mode_2(exp_matrix,thresh):
#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_old(region.fileName, None, gene_file, genome_file, threshDist=thresh)
print(mappedGenes)
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, thresh, type_file):
#remember value of bedgraph, ugly way
score = {}
for regions in exp_matrix.get_regionsets():
for region in regions:
if type_file=="ODIN":
aux=(region.data).split("\t")
aux=aux[-1].split(";")
score[(region.chrom + ':' + str(region.initial) + '-' + str(region.final))] = float(region.data[-1])
if type_file=="THOR":
aux=(region.data).split(";")
score[(region.chrom + ':' + str(region.initial) + '-' + str(region.final))] = float(aux[-1])
else:
score[(region.chrom + ':' + str(region.initial) + '-' + str(region.final))] = region.data
for i, region in enumerate(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_old(region.fileName, None, gene_file, genome_file, threshDist=thresh)
avg_score = {} #score per peak
genes = {}
print('Consider row %s of exp. matrix, number of mapped genes is %s' %(i, mappedGenes), file=sys.stderr)
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():
if options.metric == 'mean':
avg = np.mean(avg_score[gen])
elif options.metric == 'max':
avg = np.max(avg_score[gen])
print(gen, avg, ", ".join(str(t) for t in genes[gen]), sep='\t', file = f)
f.close()
# experimental_matrix_file = "/home/manuel/workspace/cluster_p/THOR/exp/exp23_macs2_payton/1"
# gene_exp = "/home/manuel/workspace/cluster_p/allhoff/project_THOR/data/payton/gene_expression/CCmean.data"
# annotation_path = "/home/manuel/workspace/cluster_h/rgtdata/hg19/"
# outputdir = "/home/manuel/test/"
exps = ExperimentalMatrix()
exps.read(experimental_matrix_file)
regionsets = exps.get_regionsets()
genome_file = annotation_path + "/chrom.sizes"
gene_file = annotation_path + "/association_file.bed"
genes = GeneSet("Expression")
genes.read_expression(gene_exp)
for region in regionsets:
bedNew = GenomicRegionSet("")
[degenes, de_peak_genes, mappedGenes, totalPeaks, regionsToGenes] \
= bedNew.filter_by_gene_association_old(region.fileName, genes.genes, gene_file, genome_file)
[ct, labels] = averageExpression(region, genes, regionsToGenes)
aux = region.fileName.split("/")
fileName = aux[-1]
fileName = fileName.split(".")
output(genes.cond, labels, ct, outputdir + "/" + fileName[0] + ".txt")
annotation_path = args[2]
outputdir = args[3]
# experimental_matrix_file = "/home/manuel/workspace/cluster_p/THOR/exp/exp23_macs2_payton/1"
# gene_exp = "/home/manuel/workspace/cluster_p/allhoff/project_THOR/data/payton/gene_expression/CCmean.data"
# annotation_path = "/home/manuel/workspace/cluster_h/rgtdata/hg19/"
# outputdir = "/home/manuel/test/"
exps = ExperimentalMatrix()
exps.read(experimental_matrix_file)
regionsets = exps.get_regionsets()
genome_file = annotation_path + "/chrom.sizes"
gene_file = annotation_path + "/association_file.bed"
genes = GeneSet("Expression")
genes.read_expression(gene_exp)
for region in regionsets:
bedNew = GenomicRegionSet("")
[degenes, de_peak_genes, mappedGenes, totalPeaks, regionsToGenes] \
= bedNew.filter_by_gene_association_old(region.fileName, genes.genes, gene_file, genome_file, threshDist=options.dist)
[ct, labels] = averageExpression(region, genes, regionsToGenes)
fileName = path.splitext(path.basename(region.fileName))[0]
output(genes.cond, labels, ct, path.join(outputdir, fileName + ".txt"))
def mode_1(exp_matrix,thresh):
for region in exp_matrix.get_regionsets():
region_set = GenomicRegionSet("")
_, _, mappedGenes, _, _ = region_set.filter_by_gene_association_old(region.fileName, None, gene_file, genome_file, threshDist=thresh)
print('#number of mapped genes:', mappedGenes)
print(region.name+"\t"+("\t".join(region_set.genes)))
def mode_4(exp_matrix,thresh,type_file,geneexp_file):
#remember value of bedgraph, ugly way
gene_set = GeneSet("")
gene_set.read_expression(geneexp_file)
score = {}
for regions in exp_matrix.get_regionsets():
for region in regions:
if type_file=="ODIN":
aux=(region.data).split("\t")
aux=aux[-1].split(";")
score[(region.chrom + ':' + str(region.initial) + '-' + str(region.final))] = aux[-1]
else:
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_old(region.fileName, gene_set.genes, gene_file, genome_file, threshDist=thresh)
print(mappedGenes)
#region.filter_by_gene_association(organism=organism,threshDist=thresh)
# _, _, mappedGenes, _, gene_peaks_mapping
avg_score = {} #score per peak
genes = {}
print(region)
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
print(avg_score)
for gen in gene_set.genes:
try:
avg = sum(map(lambda x: float(x), avg_score[gen]))/ float(len(avg_score[gen]))
peaks = ", ".join(str(t) for t in genes[gen])
siz=avg*len(avg_score[gen])
except:
avg = 0.0
siz=0
peaks = "_"
try:
print(gen, "\t".join([str(t) for t in gene_set.values[gen.upper()]]), avg, siz,peaks , sep='\t', file = f)
except:
pass
f.close()
annotation_path = args[2]
outputdir = args[3]
# experimental_matrix_file = "/home/manuel/workspace/cluster_p/THOR/exp/exp23_macs2_payton/1"
# gene_exp = "/home/manuel/workspace/cluster_p/allhoff/project_THOR/data/payton/gene_expression/CCmean.data"
# annotation_path = "/home/manuel/workspace/cluster_h/rgtdata/hg19/"
# outputdir = "/home/manuel/test/"
exps = ExperimentalMatrix()
exps.read(experimental_matrix_file)
regionsets = exps.get_regionsets()
genome_file = annotation_path + "/chrom.sizes"
gene_file = annotation_path + "/association_file.bed"
genes = GeneSet("Expression")
genes.read_expression(gene_exp)
for region in regionsets:
bedNew = GenomicRegionSet("")
[degenes, de_peak_genes, mappedGenes, totalPeaks, regionsToGenes] \
= bedNew.filter_by_gene_association_old(region.fileName, genes.genes, gene_file, genome_file, threshDist=options.dist)
[ct, labels] = averageExpression(region, genes, regionsToGenes)
fileName = path.splitext(path.basename(region.fileName))[0]
output(genes.cond, labels, ct, path.join(outputdir, fileName + ".txt"))