def __yield_score_dfm(self, snp_dfm):
snp_bed_obj = BedTool(snp_dfm.to_string(index=False,
header=False,
index_names=False),
from_string=True)
for key, bed_fn in self.src_data_fn.items():
rep_bed_fn = os.path.join(self.src_data_dir, bed_fn)
rep_bed_obj = BedTool(rep_bed_fn)
# Downstream scores
closest_iu = snp_bed_obj.closest(rep_bed_obj, D='ref', iu=True)
closest_iu_dfm = pd.read_table(StringIO(str(closest_iu)),
header=None,
names=[
'snpChrom', 'snpChromStart',
'snpChromEnd', 'snpName',
'repChrom', 'repChromStart',
'repChromEnd', 'repScore',
'distance'
],
usecols=['snpName', 'repScore'])
closest_iu_dfm = closest_iu_dfm.rename(columns={
'snpName': 'name',
'repScore': 'iu_score'
})
# Upstream scores
closest_id = snp_bed_obj.closest(rep_bed_obj, D='ref', id=True)
closest_id_dfm = pd.read_table(StringIO(str(closest_id)),
header=None,
names=[
'snpChrom', 'snpChromStart',
'snpChromEnd', 'snpName',
'repChrom', 'repChromStart',
'repChromEnd', 'repScore',
'distance'
],
usecols=['snpName', 'repScore'])
closest_id_dfm = closest_id_dfm.rename(columns={
'snpName': 'name',
'repScore': 'id_score'
})
# score_dfm = pd.concat([closest_iu_dfm, closest_id_dfm], axis=1)
score_dfm = closest_iu_dfm.merge(closest_id_dfm, on='name')
score_dfm = score_dfm.assign(avg_score=(score_dfm['iu_score'] + score_dfm['id_score']) / 2.0). \
drop(['iu_score', 'id_score'], axis=1)
score_dfm = score_dfm.rename(columns={'avg_score': key})
yield score_dfm
def find_closest_genes(peaks, annotation, annotationFeature, filteredoutput,
referencePoint, filename):
"""
Find the closest gene using bedtools.closest
"""
Peaks = BedTool(peaks)
Annotation = BedTool(annotation)
Peaks = Peaks.sort()
sites = Annotation.sort()
if annotationFeature:
filteredAnnotation = __filter_annotation(filteredoutput,
annotationFeature, annotation,
referencePoint)
sites = BedTool(filteredAnnotation).sort()
elif referencePoint:
filteredAnnotation = list()
for feature in gffutils.DataIterator(annotation):
filteredAnnotation.append(str(__get_reference_coordinate(feature,
referencePoint)))
sites = BedTool(filteredAnnotation).sort()
mapped = Peaks.closest(sites, t="first")
if filename:
mapped.saveas(filename)
return(mapped)
def process(self):
all_sites = pd.read_csv(self.sites_file, usecols=['chr', 'coordinate'])
all_sites = get_winid.convert_chr_to_num(all_sites)
chrs = np.sort(all_sites['chr'].unique())
all_sites_closest = []
for chr in chrs:
print('processing sites on chr ' + str(chr))
chr_file = self.data_dir + 'chr' + chr + '.tsv'
if not os.path.exists(chr_file):
self.split_by_chr()
chr_sites = all_sites.query('chr==@chr')
chr_sites['coordinate'] = chr_sites['coordinate'].astype('i8')
chr_sites['end'] = chr_sites['coordinate'] + 1
chr_sites = BedTool([tuple(x[1]) for x in chr_sites.iterrows()])
chr_sites_closest = chr_sites.closest(chr_file,
d=True,
nonamecheck=True)
for row in chr_sites_closest:
all_sites_closest.extend(
[[row[0], row[1], row[6], row[7], row[8]]])
del chr_sites_closest
del chr_sites
gc.collect()
all_sites_closest = pd.DataFrame(all_sites_closest,
columns=[
'chr', 'coordinate',
'eigen_phred', 'eigen_pc_phred',
'distiance_to_nearest_eigen'
])
all_sites_closest = all_sites_closest.groupby(
['chr', 'coordinate']).apply(self.mean_max).reset_index()
with pd.HDFStore(self.additional_feature_file, 'a') as h5s:
h5s['Eigen'] = all_sites_closest
def feat_dist(vf, af, name):
print "inside feat_dist"
v = BedTool(vf)
a = BedTool(af)
closest = v.closest(a, D="b")
results = dict([ (r.name, int(r[len(r.fields)-1])) for r in closest ])
print "exiting feat_dist"
return Series(results, name=name)
def main():
"""
Runs Python example from the manuscript
"""
bedtools_dir = path.split(__file__)[0]
snps = BedTool(path.join(bedtools_dir, '../test/data/snps.bed.gz'))
genes = BedTool(path.join(bedtools_dir, '../test/data/hg19.gff'))
intergenic_snps = (snps - genes)
nearby = genes.closest(intergenic_snps, d=True, stream=True)
for gene in nearby:
if int(gene[-1]) < 5000:
print gene.name
def main():
"""
Runs Python example from the manuscript
"""
bedtools_dir = path.split(__file__)[0]
snps = BedTool(path.join(bedtools_dir, '../test/data/snps.bed.gz'))
genes = BedTool(path.join(bedtools_dir, '../test/data/hg19.gff'))
intergenic_snps = (snps - genes)
nearby = genes.closest(intergenic_snps, d=True, stream=True)
for gene in nearby:
if int(gene[-1]) < 5000:
print gene.name
def computeFromBed(vcfrecord,bedtoolObj,loc_simple,loc_complex):
for alt in vcfrecord.ALT:
#if not alt:
# logging.info("{} record may have an invalid VCF syntax to represent a deletion event.".format(vcfrecord))
# record_bed=BedTool('{} {} {}'.format(vcfrecord.CHROM,vcfrecord.POS-1,vcfrecord.POS + len(vcfrecord.REF)-0, from_string=True))
if len(vcfrecord.REF) <= len(alt) : #SNP or #Insertions
record_bed=BedTool('{} {} {}'.format(vcfrecord.CHROM,vcfrecord.POS-1,vcfrecord.POS), from_string=True)
else: #Deletions
record_bed=BedTool('{} {} {}'.format(vcfrecord.CHROM,vcfrecord.POS-1,vcfrecord.POS + len(vcfrecord.REF)-len(alt)), from_string=True)
isec=record_bed.closest(bedtoolObj,D="b")#d=True)#,wb=False)
simple=getLocation(str(isec).rstrip().split()[-1],loc_simple)
complex=getLocation(str(isec).rstrip().split()[-1],loc_complex)
updateglobalsLocation(complex)
vcfrecord.INFO["LOC"] = simple
vcfrecord.INFO["LOC_DETAIL"] = complex
return vcfrecord
def annotatePAS(DB_file, pas_generator, chromosome, strand):
if DB_file is not None:
long_bed_str = ''
if pas_generator is not None:
pas_dict = generator_to_dict(pas_generator)
_i = 1
for _pos in pas_dict:
_bl = '\t'.join(
str(e) for e in [
chromosome,
int(_pos) - 1, _pos, pas_dict[_pos], chromosome + ":" +
strand + ":" + str(_i), strand
]) + '\n'
long_bed_str += _bl
_i += 1
pas_bed = BedTool(long_bed_str, from_string=True)
pas_bed = pas_bed.sort()
anno_pas_bed = pas_bed.closest(DB_file, s=True, D='b')
annotated_pas_out = []
for _apb in anno_pas_bed:
annotated_pas_out.append(
(_apb[0], _apb[1], _apb[2], _apb[3], _apb[4], _apb[5],
_apb[9], _apb[10], _apb[12]))
return annotated_pas_out
else:
return
else:
if pas_generator is not None:
pas_out = []
pas_dict = generator_to_dict(pas_generator)
_i = 1
for _pos in pas_dict:
pas_out.append(
(chromosome, int(_pos) - 1, _pos, sub_pas[_pos],
chromosome + ":" + strand + ":" + str(_i), strand))
_i += 1
return pas_out
else:
return
def add_closest(aname, bname):
a, b = BedTool(aname), BedTool(bname)
afields = a.field_count()
c = a.closest(b, d=True)
get_name = gen_get_name(b, afields)
dbed = open(BedTool._tmp(), "w")
# keep the name and distance
seen_by_line = collections.defaultdict(list)
for feat in c:
key = "\t".join(feat[:afields])
seen_by_line[key].append([feat[-1], get_name(feat)])
for key, dist_names in seen_by_line.items():
if len(dist_names) > 0:
assert len(set([d[0] for d in dist_names])) == 1
names = ",".join(sorted(set(d[1] for d in dist_names)))
new_line = "\t".join([key] + [names] + [dist_names[0][0]])
dbed.write(new_line + "\n")
dbed.close()
d = BedTool(dbed.name)
assert len(d) == len(a)
return d
def add_closest(aname, bname):
a, b = BedTool(aname), BedTool(bname)
afields = a.field_count()
c = a.closest(b, d=True)
get_name = gen_get_name(b, afields)
dbed = open(BedTool._tmp(), "w")
# keep the name and distance
seen_by_line = collections.defaultdict(list)
for feat in c:
key = "\t".join(feat[:afields])
seen_by_line[key].append([feat[-1], get_name(feat)])
for key, dist_names in seen_by_line.iteritems():
if len(dist_names) > 0:
assert len(set([d[0] for d in dist_names])) == 1
names = ",".join(sorted(set(d[1] for d in dist_names)))
new_line = "\t".join([key] + [names] + [dist_names[0][0]])
dbed.write(new_line + "\n")
dbed.close()
d = BedTool(dbed.name)
assert len(d) == len(a)
return d
def main():
tmpdir_obj = TemporaryDirectory(dir=project_temp_dir)
tmpdir_path = Path(tmpdir_obj.name)
gencode_dir = Path(
"/icgc/dkfzlsdf/analysis/hs_ontogeny/databases/gene_annotations")
gencode19_gtf_appris_principal = (
"/icgc/dkfzlsdf/analysis/hs_ontogeny/databases/gene_annotations"
"/gencode.vM19.annotation.appris-principal.no-prefix.gtf")
gencode19_gtf = (
"/icgc/dkfzlsdf/analysis/hs_ontogeny/databases/gene_annotations"
"/gencode.vM19.annotation.no-prefix.gtf")
all_tss_area_bed = gencode_dir.joinpath('all-tss_slop-100000-5000.bed')
strand_dtype = CategoricalDtype(['+', '-'], ordered=True)
# %% compute tss area intersects, ~ 30s
gencode_df = pd.read_csv(gencode19_gtf,
sep='\t',
header=None,
comment='#',
names=[
'feat_chrom', 'source', 'feature', 'Start',
'End', 'score', 'feat_strand', 'frame',
'attribute'
],
dtype={
'feat_chrom': str,
'Start': 'i8',
'End': 'i8',
'feat_strand': strand_dtype
})
tss_n_upstream = 100_000
tss_n_downstream = 5000
transcripts = gencode_df.query('feature == "transcript"').copy()
on_plus_strand = transcripts['feat_strand'] == '+'
transcripts['TSS'] = -1
transcripts['feat_start'] = -1
transcripts['feat_end'] = -1
transcripts['feat_class'] = 'TSS_area'
transcripts = expand_gtf_attributes(transcripts)
# custom slop
transcripts.loc[on_plus_strand, 'TSS'] = transcripts.loc[on_plus_strand,
'Start']
transcripts.loc[~on_plus_strand, 'TSS'] = transcripts.loc[~on_plus_strand,
'End']
transcripts.loc[on_plus_strand,
'feat_start'] = transcripts.loc[on_plus_strand,
'Start'] - tss_n_upstream
transcripts.loc[on_plus_strand,
'feat_end'] = transcripts.loc[on_plus_strand,
'Start'] + tss_n_downstream
transcripts.loc[~on_plus_strand,
'feat_start'] = transcripts.loc[~on_plus_strand,
'End'] - tss_n_downstream
transcripts.loc[~on_plus_strand,
'feat_end'] = transcripts.loc[~on_plus_strand,
'End'] + tss_n_upstream
transcripts = transcripts.sort_values(
['feat_chrom', 'feat_start', 'feat_end', 'TSS'])
transcripts.loc[transcripts['feat_start'].lt(0), 'feat_start'] = 0
transcripts_cols = [
'feat_chrom', 'feat_start', 'feat_end', 'TSS', 'feat_strand',
'feat_class', 'gene_name', 'gene_id', 'transcript_id',
'appris_principal_score'
]
transcripts[transcripts_cols].to_csv(all_tss_area_bed,
sep='\t',
header=False,
index=False)
all_tss_area_bt = BedTool(str(all_tss_area_bed))
merged_dmrs_bt = BedTool(str(merged_dmrs_bed))
tss_intersect_bt = merged_dmrs_bt.intersect(all_tss_area_bt,
wa=True,
wb=True)
tss_intersect_df = pd.read_csv(
tss_intersect_bt.fn,
sep='\t',
names=['Chromosome', 'Start', 'End', 'region_id'] + transcripts_cols)
tss_intersect_df['perc_feature'] = np.nan
tss_intersect_df['perc_region'] = np.nan
tss_intersect_df['distance'] = -1e8
tss_intersect_df['center'] = tss_intersect_df.eval(
'Start + (End - Start)/2')
tss_intersect_df['feat_center'] = np.nan
tss_intersect_df['has_center'] = False
tss_intersect_df['distance'] = tss_intersect_df.eval('center - TSS')
assert tss_intersect_df['distance'].ne(-1e8).all()
# tss_intersect_df.loc[tss_intersect_df.eval('Start <= TSS <= End'), 'distance'] = 0
# tss_intersect_df.loc[tss_intersect_df.eval('End < TSS'), 'distance'] = tss_intersect_df.eval('End - TSS')
# tss_intersect_df.loc[tss_intersect_df.eval('Start > TSS'), 'distance'] = tss_intersect_df.eval('Start - TSS')
full_cols = [
'Chromosome', 'Start', 'End', 'region_id', 'center', 'feat_class',
'perc_feature', 'perc_region', 'distance', 'has_center', 'gene_name',
'gene_id', 'transcript_id', 'appris_principal_score', 'feat_chrom',
'feat_start', 'feat_end', 'feat_center', 'feat_strand'
]
tss_intersect_df_full = tss_intersect_df[full_cols]
# %% compute exon, intron overlap, ~45s
transcript_parts = gencode_df.loc[
~gencode_df['feature'].isin(['gene', 'start_codon', 'stop_codon']), :]
transcript_parts_fp = tmpdir_path.joinpath('transcript_parths.gtf')
transcript_parts.to_csv(transcript_parts_fp,
sep='\t',
header=False,
index=False)
transcript_parts_bt = BedTool(str(transcript_parts_fp))
transcript_parts_anno = merged_dmrs_bt.intersect(transcript_parts_bt,
wa=True,
wb=True)
transcript_parts_anno.head()
transcript_parts_df = pd.read_csv(transcript_parts_anno.fn,
sep='\t',
header=None)
transcript_parts_df.columns = [
'Chromosome', 'Start', 'End', 'region_id'
] + [
'feat_chrom', 'source', 'feat_class', 'feat_start', 'feat_end',
'score', 'feat_strand', 'frame', 'attribute'
]
start = transcript_parts_df.eval('Start - feat_start').where(
lambda ser: ser.gt(0), 0)
feat_size = transcript_parts_df.eval('feat_end - feat_start')
end = transcript_parts_df.eval('End - feat_start').where(
lambda ser: ser.lt(feat_size), feat_size)
overlap_size = end - start
region_size = transcript_parts_df.eval('End - Start')
transcript_parts_df['center'] = transcript_parts_df.eval(
'Start + (End - Start)/2')
transcript_parts_df['feat_center'] = transcript_parts_df.eval(
'feat_start + (feat_end - feat_start)/2')
transcript_parts_df['distance'] = transcript_parts_df.eval(
'center - feat_center')
transcript_parts_df['has_center'] = transcript_parts_df['distance'].lt(
feat_size / 2)
transcript_parts_df['perc_feature'] = overlap_size / feat_size
transcript_parts_df['perc_region'] = overlap_size / region_size
transcript_parts_df['distance'] = np.nan
transcript_parts_df = expand_gtf_attributes(transcript_parts_df)
transcript_parts_df_full = transcript_parts_df[full_cols]
# %% classify into proximal and distal cis regulatory regions
promoter_anno = tss_intersect_df_full.copy()
is_proximal_promoter = promoter_anno.eval('-5000 <= distance <= 1000')
is_distant_cis_regulatory_domain = promoter_anno.eval(
'-20000 <= distance < -5000')
promoter_anno['feat_class'] = np.nan
promoter_anno.loc[is_proximal_promoter, 'feat_class'] = 'Promoter'
promoter_anno.loc[is_proximal_promoter, 'has_center'] = True
promoter_anno.loc[is_distant_cis_regulatory_domain, 'feat_class'] = 'UCRD'
promoter_anno.loc[is_distant_cis_regulatory_domain, 'has_center'] = True
promoter_anno = promoter_anno.loc[~promoter_anno['feat_class'].isna(), :]
# %% concatenate and type casts
full_annos = (pd.concat([promoter_anno, transcript_parts_df_full],
axis=0).sort_values(['Chromosome', 'Start',
'End']))
precedence = pd.Series([
'start_codon',
'stop_codon',
'Promoter',
'UTR',
'exon',
'CDS',
'UCRD',
'transcript',
])
# %% Filter according to precedence
def filter_annotations(group_df):
highest_class = precedence.iloc[precedence.isin(
group_df['feat_class']).idxmax()]
class_df = group_df.query('feat_class == @highest_class').sort_values(
['appris_principal_score', 'perc_region'])
# TODO: sort by appris score, then by overlap
if highest_class == 'transcript':
class_df['feat_class'] = 'intron'
if class_df['gene_name'].nunique() == 1:
return class_df.iloc[[0], :]
else:
return class_df.groupby('gene_name', as_index=False).nth(0)
center_annos = full_annos.loc[full_annos['has_center'], :]
# filter, takes ~
# could maybe be sped up by removing more introns, perhaps with better intron annotation?
t1 = time.time()
filtered_annos = center_annos.groupby(
'region_id', group_keys=False).apply(filter_annotations)
print(time.time() - t1)
# cores = 24
# filtered_annos_l = Parallel(cores)(delayed(filter_annotations)(group_df) for unused_name, group_df in grouped)
# filtered_annos = pd.concat(filtered_annos_l, axis=0)
filtered_annos.to_pickle(
results_dir.joinpath('filtered-annos_no-intergenic.p'))
# filtered_annos = pd.read_pickle(results_dir.joinpath('filtered-annos_no-intergenic.p'))
ids_annotated_regions = filtered_annos['region_id'].unique()
merged_dmrs_df = pd.read_pickle(merged_dmrs_p)
intergenic_regions = merged_dmrs_df.loc[
~merged_dmrs_df['region_id'].isin(ids_annotated_regions), :].copy()
intergenic_regions['feat_class'] = 'intergenic'
# error: chromosome dtypes are different
all_regions_annotated = (pd.concat([filtered_annos, intergenic_regions],
sort=False,
axis=0))
all_regions_annotated['Chromosome'] = all_regions_annotated[
'Chromosome'].astype(str)
all_regions_annotated.sort_values(['Chromosome', 'Start', 'End'],
inplace=True)
assert (
all_regions_annotated['region_id'].unique() == np.arange(53231)).all()
all_regions_annotated['region_id'].value_counts().value_counts()
all_regions_annotated['feat_class'].value_counts()
all_regions_annotated.to_pickle(gencode_anno_p)
all_regions_annotated.to_csv(gencode_anno_tsv,
sep='\t',
header=True,
index=False)
filtered_annos['feat_class'].value_counts()
gencode_df_w_attributes = expand_gtf_attributes(gencode_df)
principal_transcripts = gencode_df_w_attributes.query(
'appris_principal_score > 0 and feature == "transcript"').copy()
# get TSS
tss_on_plus_strand = principal_transcripts['feat_strand'].eq('+')
principal_transcripts.loc[
tss_on_plus_strand,
'End'] = principal_transcripts.loc[tss_on_plus_strand, 'Start'] + 1
principal_transcripts.loc[
~tss_on_plus_strand,
'Start'] = principal_transcripts.loc[~tss_on_plus_strand, 'End'] - 1
principal_transcripts = principal_transcripts.sort_values(
['feat_chrom', 'Start', 'End'])
principal_transcripts_fp = tmpdir_path / 'principal-transcripts.gtf'
principal_transcripts.iloc[:, 0:9].to_csv(principal_transcripts_fp,
sep='\t',
header=False,
index=False)
# pybedtools.featurefuncs.TSS has a bug
gtf_princ_tss_bt = BedTool(str(principal_transcripts_fp))
closest_tss_bt = merged_dmrs_bt.closest(gtf_princ_tss_bt,
D='b',
fu=True,
t='first')
closest_tss_df = pd.read_csv(closest_tss_bt.fn, sep='\t', header=None)
distances = closest_tss_df.iloc[:, -1]
distances = distances.loc[(distances < 100_000) & (distances > -100_000)]
import matplotlib as mpl
from matplotlib.axes import Axes # for autocompletion in pycharm
from matplotlib.figure import Figure # for autocompletion in pycharm
mpl.use('Agg') # import before pyplot import!
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
fig, ax = plt.subplots(1, 1)
sns.distplot(distances.values, bins=1000, kde=False, ax=ax)
fig.savefig(results_dir.joinpath('tss-distance-dist.png'))
fig.savefig(results_dir.joinpath('tss-distance-dist.pdf'))
def get_transition_matrix_cluster(h3k27ac, h3k4me3, h3k27me3, diff_dict,
cluster_index):
intervals = BedTool(
"/srv/scratch/annashch/dmso/dp_gp/dpgp_diff_peaks_fold/" +
str(cluster_index) + ".bed")
intersection_h3k27ac = intervals.closest(h3k27ac, wao=True)
intersection_h3k4me3 = intervals.closest(h3k4me3, wao=True)
intersection_h3k27me3 = intervals.closest(h3k27me3, wao=True)
results = dict()
for i in range(len(intervals)):
if i % 100 == 0:
print(i)
cur_interval = tuple(intersection_h3k27ac[i][0:3])
results[cur_interval] = dict()
results[cur_interval]['dmso'] = [0, 0, 0] #h3k27ac, h3k4me3, h3k27me3
results[cur_interval]['control'] = [0, 0, 0]
#determine which of 6 possible mark combinations is represented for DMSO & Control
if intersection_h3k27ac[i][3].startswith('c'):
#is the h3k27ac mark differential or stable?
overlap = tuple(intersection_h3k27ac[i][3:6])
#is the peak up in dmso?
if overlap in diff_dict['h3k27ac_up']:
results[cur_interval]['dmso'][0] = 1
#is the peak up in control?
elif overlap in diff_dict['h3k27ac_down']:
results[cur_interval]['control'][0] = 1
else:
results[cur_interval]['dmso'][0] = 1
results[cur_interval]['control'][0] = 1
if intersection_h3k4me3[i][3].startswith('c'):
#is the h3k4me3 mark differential or stable?
overlap = tuple(intersection_h3k4me3[i][3:6])
#is the peak up in dmso?
if overlap in diff_dict['h3k4me3_up']:
results[cur_interval]['dmso'][1] = 1
#is the peak up in control?
elif overlap in diff_dict['h3k4me3_down']:
results[cur_interval]['control'][1] = 1
else:
results[cur_interval]['dmso'][1] = 1
results[cur_interval]['control'][1] = 1
if intersection_h3k27me3[i][3].startswith('c'):
#is the h3k27me3 mark differential or stable?
overlap = tuple(intersection_h3k27me3[i][3:6])
#is the peak up in dmso?
if overlap in diff_dict['h3k27me3_up']:
results[cur_interval]['dmso'][2] = 1
#is the peak up in control?
elif overlap in diff_dict['h3k27me3_down']:
results[cur_interval]['control'][2] = 1
else:
results[cur_interval]['dmso'][2] = 1
results[cur_interval]['control'][2] = 1
print("completed interval labels")
#aggregate results into matrix
transition_mat = dict()
start_states = set([])
end_states = set([])
for interval in results:
start_state = tuple(results[interval]['control'])
end_state = tuple(results[interval]['dmso'])
start_states.add(start_state)
end_states.add(end_state)
if start_state not in transition_mat:
transition_mat[start_state] = dict()
if end_state not in transition_mat[start_state]:
transition_mat[start_state][end_state] = 1
else:
transition_mat[start_state][end_state] += 1
outf = open("chipseq_transition_matrix_" + str(cluster_index) + ".txt",
'w')
start_states = list(start_states)
end_states = list(end_states)
outf.write('\t' + '\t'.join([str(i) for i in end_states]) + '\n')
for s in start_states:
outf.write(str(s))
for e in end_states:
if e in transition_mat[s]:
outf.write('\t' + str(transition_mat[s][e]))
else:
outf.write('\t0')
outf.write('\n')
def proximal(path1,
path2,
window_min,
window_max,
upstream=False,
downstream=False,
bins=None):
"""
This is the main function of pairwise_asymmetries.py
Uses pybedtools closest function to find proximal coordinates
Then calculates asymmetry through orientation function for proximal pairs
# the flags it uses from here https://bedtools.readthedocs.io/en/latest/content/tools/closest.html
if bins==True then return not the counts but the lists of counts to bin them
"""
# Finds the occurrences within the proximity limits and saves their pairwise orientation.
DataL1 = BedTool(path1).sort()
DataL2 = BedTool(path2).sort()
if upstream == downstream and upstream == True:
closest = DataL1.closest(DataL2, D='ref')
elif upstream is True:
closest = DataL1.closest(DataL2, D='ref', id=False, iu=True)
elif downstream is True:
closest = DataL1.closest(DataL2, D='ref', iu=False, id=True)
else:
closest = DataL1.closest(DataL2, D='ref')
closest_df = closest.to_dataframe()
Strand1_init = list(closest_df.iloc[:, 5])
Strand2_init = list(closest_df.iloc[:, 11])
Distance_init = [i for i in list(closest_df.iloc[:, -1])]
Distance1_temp, Strand1, Strand2 = zip(*(
(dist, strand1, strand2) for dist, strand1, strand2 in zip(
Distance_init, Strand1_init, Strand2_init)
if abs(dist) <= window_max and abs(dist) >= window_min and dist >= 0))
Distance2_temp, Strand1_temp, Strand2_temp = zip(
*((dist, strand2, strand1) for dist, strand1, strand2 in zip(
Distance_init, Strand1_init, Strand2_init)
if abs(dist) <= window_max and abs(dist) >= window_min and dist < 0))
Distance = list(Distance1_temp) + list(Distance2_temp)
Strand1 = list(Strand1) + list(Strand1_temp)
Strand2 = list(Strand2) + list(Strand2_temp)
p_p, m_m, p_m, m_p, same_strand, opposite_strand, convergent, divergent = orientation(
Strand1, Strand2)
# Calculate the distance distributions for all orientations
Distances_orientations = get_distance_orientations(Distance, Strand1,
Strand2, window_min,
window_max)
p_pL_bin = []
m_mL_bin = [] # Same orientation
p_mL_bin = []
m_pL_bin = [] # Opposite orientation
same_strandL_bin = []
opposite_strandL_bin = [] # Combined same / opposite orientations
convergentL_bin = []
divergentL_bin = []
Bins = []
if bins is not None:
# Performs the same analysis for each bin.
Bins = binner(window_min, window_max, bins)
for index, bin_i in enumerate(Bins):
Strand1Bin = []
Strand2Bin = []
min_bin, max_bin = bin_i
for k in range(len(Distance)):
if Distance[k] >= min_bin and Distance[k] < max_bin:
Strand1Bin.append(Strand1[k])
Strand2Bin.append(Strand2[k])
p_p_bin, m_m_bin, p_m_bin, m_p_bin, same_strand_bin, opposite_strand_bin, convergent_bin, divergent_bin = orientation(
Strand1Bin, Strand2Bin)
p_pL_bin.append(p_p_bin)
m_mL_bin.append(m_m_bin) # Same orientation, per bin
p_mL_bin.append(p_m_bin)
m_pL_bin.append(m_p_bin) # Opposite orientation per bin
same_strandL_bin.append(same_strand_bin)
opposite_strandL_bin.append(opposite_strand_bin)
convergentL_bin.append(convergent_bin)
divergentL_bin.append(divergent_bin)
return (Distances_orientations, p_p, m_m, p_m, m_p, same_strand,
opposite_strand, convergent,
divergent), (Bins, p_pL_bin, m_mL_bin, p_mL_bin, m_pL_bin,
same_strandL_bin, opposite_strandL_bin,
convergentL_bin, divergentL_bin)
#!/usr/bin/python
"""
Example from the manuscript to print the names of genes that are <5000 bp away
from intergenic SNPs. See sh_ms_example.sh for the shell script equivalent.
"""
from pybedtools import BedTool
snps = BedTool('../test/data/snps.bed.gz')
genes = BedTool('../test/data/hg19.gff')
intergenic_snps = (snps - genes)
nearby = genes.closest(intergenic_snps, d=True, stream=True)
for gene in nearby:
if int(gene[-1]) < 5000:
print gene.name
grch38gff='/home/drew/Desktop/IPyNB-Variant-Analysis/data/cuffcmp.combined.gtf'
#snps = BedTool('snps.bed.gz') # [1]
genes = BedTool(grch38gff) # [1]
# In[ ]:
get_ipython().run_cell_magic('bash', '', 'ln -P /home/drew/Desktop/IPyNB-Variant-Analysis/data\nln -P /media/drew/easystore/ReferenceGenomes/GCA_000001405.15_GRCh38_no_alt_analysis_set/\nln -P /media/drew/easystore/ReferenceGenomes/GRCh38/')
# In[ ]:
intergenic_snps = snps.subtract(genes) # [2]
nearby = genes.closest(intergenic_snps, d=True, stream=True) # [2, 3]
for gene in nearby: # [4]
if int(gene[-1]) < 5000: # [4]
print gene.name # [4]
# In[ ]:
get_ipython().run_cell_magic('bash', '', 'cd /media/drew/easystore/GoodCell-Resources/AnalysisBaseDir/GSA_Data/2018_07\npwd\nls -l */*vcf')
# In[ ]:
def run(chipdir, refseq, filedir, DMSO, CA):
TSS = (-200, 1000)
a = BedTool(chipdir)
b = a.closest(refseq, d=True)
b.cut([9, 10, 11, 12, 13, 14, 21]).saveas(filedir + "/SRF_closest.bed")
d = dict()
with open(filedir + "/SRF_closest.bed") as F:
for line in F:
line = line.strip().split()
chrom, start, stop = line[0:3]
d[chrom + "\t" + start + "\t" + stop + "\t"] = "\t".join(line[3:])
outfile = open(filedir + "/SRF_closest.rmdup.bed", "w")
for key in d:
if "." not in key.split():
outfile.write(key + d[key] + "\n")
outfile.close()
# os.system("sort -k1,1 -k2,2n " + filedir + "/SRF_closest.rmdup.bed > " + filedir + "/SRF_closest.rmdup.sorted.bed")
a = BedTool(filedir + "/SRF_closest.rmdup.bed")
a.sort().saveas(filedir + "/SRF_closest.rmdup.sorted.bed")
outfile = open(filedir + "/SRF.TSS.bed", "w")
outfile2 = open(filedir + "/SRF.gene.bed", "w")
with open(filedir + "/SRF_closest.rmdup.sorted.bed") as F:
for line in F:
chrom, start, stop, gene, number, strand, distance = line.strip().split()
if int(stop) - int(start) > 2000 and int(distance) > 10000:
if strand is "+":
outfile.write(chrom + "\t" + str(int(start) + TSS[0]) + "\t" + str(int(start) + TSS[1]) + "\n")
outfile2.write(chrom + "\t" + str(int(start) + TSS[1]) + "\t" + stop + "\n")
else:
outfile.write(chrom + "\t" + str(int(stop) - TSS[1]) + "\t" + str(int(stop) - TSS[0]) + "\n")
outfile2.write(chrom + "\t" + start + "\t" + str(int(stop) - TSS[1]) + "\n")
outfile.close()
outfile2.close()
a = BedTool(filedir + "/SRF.TSS.bed")
a.sort().saveas(filedir + "/SRF.TSS.bed")
a = BedTool(filedir + "/SRF.gene.bed")
a.sort().saveas(filedir + "/SRF.gene.bed")
TSS = filedir + "/SRF.TSS.bed"
genes = filedir + "/SRF.gene.bed"
os.system("bedtools map -a " + genes + " -b " + DMSO + " -c 4 -o sum > " + filedir + "/DMSO.genes.bed")
os.system("bedtools map -a " + TSS + " -b " + DMSO + " -c 4 -o sum > " + filedir + "/DMSO.TSS.bed")
os.system("bedtools map -a " + genes + " -b " + CA + " -c 4 -o sum > " + filedir + "/CA.genes.bed")
os.system("bedtools map -a " + TSS + " -b " + CA + " -c 4 -o sum > " + filedir + "/CA.TSS.bed")
TRx = list()
TRy = list()
expressionlist = list()
with open(filedir + "/DMSO.genes.bed") as a, open(filedir + "/DMSO.TSS.bed") as b, open(
filedir + "/CA.genes.bed"
) as c, open(filedir + "/CA.TSS.bed") as d:
for line in a:
bline = b.readline().strip().split()[-1]
cline = c.readline().strip().split()[-1]
dline = d.readline().strip().split()[-1]
if line.strip().split()[-1] is ".":
DMSOgene = 0.0
else:
DMSOgene = float(line.strip().split()[-1])
if bline is ".":
DMSOTSS = 0.0
else:
DMSOTSS = float(bline)
if cline is ".":
CAgene = 0.0
else:
CAgene = float(cline)
if dline is ".":
CATSS = 0.0
else:
CATSS = float(dline)
if DMSOgene == 0.0:
TRx.append(0.0)
else:
TRx.append((DMSOTSS / DMSOgene))
if CAgene == 0.0:
TRy.append(0.0)
else:
TRy.append((CATSS / CAgene))
expressionlist.append((np.log2(DMSOgene) + np.log2(CAgene)) / 2.0)
F6 = plt.figure()
ax1 = F6.add_subplot(111)
xy = np.vstack([TRx, TRy])
z = gaussian_kde(xy)(xy)
ax1.scatter(TRx, TRy, c=z, edgecolor="")
# ax1.scatter(TRx2,TRy2,c='red',edgecolor="",s=expressionlist2)
ax1.set_title("Pausing Index")
ax1.set_ylabel("CA")
ax1.set_xlabel("DMSO")
ax1.get_xaxis().tick_bottom()
ax1.get_yaxis().tick_left()
# ax1.plot([0,1/slope1],[intercept1,1],color = 'r')
ax1.set_xlim([0, 20])
ax1.set_ylim([0, 20])
ax1.plot([0, 50.0], [0, 50.0], color="k")
# ax1.text(8,18, "Pearson = " + str(pearsons)[0:5])
# ax2 = F6.add_subplot(122)
# ax2.plot(np.sort(cdf),np.linspace(0,1,len(cdf)))
# ax2.plot(stats.norm.cdf(np.linspace(min(cdf),max(cdf)),0,np.var(cdf)),np.linspace(0,1,len(cdf)))
plt.savefig(figuredir + "/PausingIndex.png")
peak_background = peak_background.sort()
gene_background = gene_background.sort()
return peak_background, gene_background
import pdb
for cluster in range(1, 7):
#get the peaks & genes for the current cluster
peak_bed = BedTool(str(cluster) + ".peaks.bed")
gene_bed = BedTool(str(cluster) + ".genes.bed")
#get the background
peak_background, gene_background = get_background(cluster)
#peak to gene closest, cur cluster
peak_to_gene_foreground = [
int(str(i).strip().split('\t')[-1])
for i in peak_bed.closest(gene_bed, wao=True, d=True, t="first")
]
#gene to peak closest, cur cluster
gene_to_peak_foreground = [
int(str(i).strip().split('\t')[-1])
for i in gene_bed.closest(peak_bed, wao=True, d=True, t="first")
]
#peak to gene closest, background
peak_to_gene_background = [
int(str(i).strip().split('\t')[-1])
for i in peak_bed.closest(gene_background, wao=True, d=True, t="first")
]
#gene to peak closest, background
gene_to_peak_background = [
int(str(i).strip().split('\t')[-1])
for i in gene_bed.closest(peak_background, wao=True, d=True, t="first")
csv_writer.writerow(out)
print("\n\nFile(s) generated:\n\t", fileOut)
def chrom_format(gencode):
return BedTool([list(j.replace('chr', '') for j in i) for i in gencode])
### ANNOTATE #####
if not delly_format and gencode and len(bedpe_list) > 0:
bedpe_bed = BedTool(bedpe_list)
if not 'chr' in bedpe_list[0][0]:
gencode = chrom_format(gencode)
bedpe_gencode = bedpe_bed.closest(gencode, d=True)
bedpe_bed2 = BedTool([i[3:6] + i[:] for i in bedpe_gencode[:]])
del (bedpe_gencode)
bedpe_gencode = bedpe_bed2.closest(gencode, d=True)
bedpe_list2 = [i[3:] for i in bedpe_gencode[:]]
bedpe_header = header + [
'chrom_gene1', 'start_gene1', 'end_gene1', 'name_gene1',
'strand_gene1', 'dist_gene1', 'chrom_gene2', 'start_gene2',
'end_gene2', 'name_gene2', 'strand_gene2', 'dist_gene2', 'fusion_gene'
]
with open(fileOutAnno, 'wb') as wout:
bedpe_writer = csv.writer(wout, delimiter="\t")
max_distance = 50000
gene_list = list()
for r in bedpe_list2:
r = [i.replace('\r', '') for i in r]
def run(DMSO, Nutlin1, Nutlin3, P53, figuredir, file2dir):
D = BedTool(DMSO)
N1 = BedTool(Nutlin1)
N3 = BedTool(Nutlin3)
P = BedTool(P53).cut([0, 1, 2])
start = time.time()
w1 = (D + P).saveas(file2dir + 'Wave1.bed')
w1rand = BedTool([P[i] for i in rn.randint(0, len(P), len(w1))]).sort()
w2 = (N1 + P - D).saveas(file2dir + 'Wave2.bed')
w2rand = BedTool([P[i] for i in rn.randint(0, len(P), len(w2))]).sort()
w3 = (N3 + P - N1 - D).saveas(file2dir + 'Wave3.bed')
w3rand = BedTool([P[i] for i in rn.randint(0, len(P), len(w3))]).sort()
# w1 = D+P
# print rn.shuffle(list(P))[:len(w1)]
# w1rand = BedTool(rn.shuffle(P)[:len(w1)]).sort()
# w2 = N1+P-D
# w2rand = BedTool(rn.shuffle(P)[:len(w2)]).sort()
# w3 = N3+P-N1-D
# w3rand = BedTool(rn.shuffle(P)[:len(w3)]).sort()
end = time.time()
print(end - start)
a = w2.closest(w1, d=True)
b = w2rand.closest(w1rand, d=True)
c = w3.closest(w2, d=True)
d = w3rand.closest(w2rand, d=True)
w21 = list()
w2r1r = list()
w32 = list()
w3r2r = list()
for x in a:
try:
w21.append(math.log(float(x[-1]), 10))
except:
w21.append(0)
for x in b:
try:
w2r1r.append(math.log(float(x[-1]), 10))
except:
w2r1r.append(0)
for x in c:
try:
w32.append(math.log(float(x[-1]), 10))
except:
w32.append(0)
for x in d:
try:
w3r2r.append(math.log(float(x[-1]), 10))
except:
w3r2r.append(0)
print len(w21), len(w2r1r), len(w32), len(w3r2r)
# w21 = [math.log(float(x[-1])) for x in a if float(x[-1]) != 0]
# w2r1r = [math.log(float(x[-1])) for x in b if float(x[-1]) != 0]
# w32 = [math.log(float(x[-1])) for x in c if float(x[-1]) != 0]
# w3r2r = [math.log(float(x[-1])) for x in d if float(x[-1]) != 0]
# print stats.ks_2samp(w21, w2r1r)
# print stats.ks_2samp(w32, w3r2r)
F = plt.figure()
ax1 = F.add_subplot(221)
ax1.set_title('Wave2 to Wave1 (pval: ' +
str(stats.ks_2samp(w21, w2r1r)[1]) + ')')
ax1.set_ylabel('Count')
ax1.set_xlabel('Log 10 Distance (bp)')
ax1.hist(w21, bins=np.arange(0, 8 + 0.2, 0.2), alpha=0.5, color='green')
# ax1.set_xlim([0,500000])
# ax1.set_ylim([0,600])
# ax1.hist(w21,bins=np.arange(0, 18 + 0.2, 0.2))
# ax1.set_xscale('log')
# ax2.F.add_subplot(222)
# ax2.set_title('Wave2rand to Wave1rand')
# ax2.set_ylabel('Count')
# ax2.set_xlabel('Distance (bp)')
ax1.hist(w2r1r, bins=np.arange(0, 8 + 0.2, 0.2), alpha=0.5, color='red')
ax1.legend(['Observed', 'Expected'], loc='upper left')
# ax2.set_xlim([0,500000])
# ax2.set_ylim([0,600])
# ax1.hist(w2r1r,bins=np.arange(0, 18 + 0.2, 0.2))
# ax2.set_xscale('log')
ax2 = F.add_subplot(222)
ax2.set_title('Cumulative distribution function')
ax2.set_ylabel('CDF')
ax2.set_xlabel('Log 10 Distance (bp)')
# Use the histogram function to bin the data
counts, bin_edges = np.histogram(w21,
bins=np.arange(0, 18 + 0.2, 0.2),
normed=True)
counts_r, bin_edges_r = np.histogram(w2r1r,
bins=np.arange(0, 18 + 0.2, 0.2),
normed=True)
# Now find the cdf
cdf = np.cumsum(counts)
cdf_r = np.cumsum(counts_r)
# And finally plot the cdf
plt.plot(bin_edges[1:], cdf, color='green')
plt.plot(bin_edges_r[1:], cdf_r, color='red')
ax2.legend(['Observed', 'Expected'], loc='upper left')
ax3 = F.add_subplot(223)
ax3.set_title('Wave3 to Wave2 (pval: ' +
str(stats.ks_2samp(w32, w3r2r)[1]) + ')')
ax3.set_ylabel('Count')
ax3.set_xlabel('Log 10 Distance (bp)')
ax3.hist(w32, bins=np.arange(0, 8 + 0.2, 0.2), alpha=0.5, color='green')
# ax3.set_xlim([0,500000])
# ax3.set_ylim([0,3500])
# ax3.hist(w32,bins=np.arange(0, 18 + 0.2, 0.2))
# ax3.set_xscale('log')
ax4 = F.add_subplot(224)
ax4.set_title('Cumulative distribution function')
ax4.set_ylabel('CDF')
ax4.set_xlabel('Log 10 Distance (bp)')
# Use the histogram function to bin the data
counts, bin_edges = np.histogram(w32,
bins=np.arange(0, 18 + 0.2, 0.2),
normed=True)
counts_r, bin_edges_r = np.histogram(w3r2r,
bins=np.arange(0, 18 + 0.2, 0.2),
normed=True)
# Now find the cdf
cdf = np.cumsum(counts)
cdf_r = np.cumsum(counts_r)
# And finally plot the cdf
plt.plot(bin_edges[1:], cdf, color='green')
plt.plot(bin_edges_r[1:], cdf_r, color='red')
ax4.legend(['Observed', 'Expected'], loc='upper left')
# ax4 = F.add_subplot(224)
# ax4.set_title('Wave3rand to Wave2rand')
# ax4.set_ylabel('Count')
# ax4.set_xlabel('Distance (bp)')
ax3.hist(w3r2r, bins=np.arange(0, 8 + 0.2, 0.2), alpha=0.5, color='red')
ax3.legend(['Observed', 'Expected'], loc='upper left')
# ax4.set_xlim([0,500000])
# ax4.set_ylim([0,3500])
# ax3.hist(w3r2r,bins=np.arange(0, 18 + 0.2, 0.2))
# ax4.set_xscale('log')
plt.savefig(figuredir + 'Cluster_analysis.png', dpi=1200)
