def annotateVCFs(vcflistpath=bsmutils.get_bsmdir() + '/results/calls/filtered-vcfs.tsv',
vcfdir=bsmutils.get_bsmdir() + '/results/calls/'):
vcflist = pd.read_csv(vcflistpath, sep='\t', names=['sample', 'file'], index_col='sample')
def helper(sample):
invcf = vcfdir + os.path.sep + 'filtered' + os.path.sep + vcflist.loc[sample, 'file']
targetdir = vcfdir + os.path.sep + 'annotated' + os.path.sep
val = annotateVCF(invcf=invcf, sample=sample, targetdir=targetdir)
return(val)
pp = [helper(y) for y in vcflist.index]
return(pp)
def annotateVCF(invcf=bsmutils.get_bsmdir() + '/results/calls/filtered/MSSM_106_brain.ploidy_50.filtered.vcf',
sample='MSSM_106_NeuN_pl',
targetdir=bsmutils.get_bsmdir() + '/results/calls/annotated/'):
'''
Some help would be nice
'''
script = bsmutils.get_bsmdir() + '/src/annotate-vcf-bsm'
cmd = [script, '-t', targetdir, invcf, sample]
p = subprocess.run(cmd, capture_output=True)
return(p)
def get_multi_annotations(annotlist,
vcflistpath=bsmutils.get_bsmdir() +
'/results/calls/filtered-vcfs-Chess-Walsh.tsv',
annotdirpath=bsmutils.get_bsmdir() +
'/results/2020-09-07-annotations',
na_values={},
simplecolumns=True):
vcflist = pd.read_csv(vcflistpath,
sep='\t',
names=['sample', 'file'],
index_col='sample')
samplestr = '((MSSM|PITT)_[0-9]+)_(NeuN_pl|NeuN_mn|muscle)'
def sample2indivID(sample):
return (re.sub(samplestr, 'CMC_\\1', sample))
def sample2tissue(sample):
#if re.match('.*Walsh.*', vcflistpath):
if not re.match(samplestr, sample):
return ('frontal cortex') # Walsh data
return (re.sub(samplestr, '\\3', sample)) # Chess data
def get_annot(sample, annotyp):
sampledir = annotdirpath + os.path.sep + sample
tsvpath = sampledir + os.path.sep + annotyp + '.txt'
indivID = sample2indivID(sample)
tissue = sample2tissue(sample)
na_val = na_values[annotyp] if annotyp in na_values.keys() else []
try:
annot = read_TXT_per_annotation(tsvpath,
indivID,
tissue,
simplecolumns=simplecolumns,
na_values=na_val)
annot = annotation_duplicates(annot, sep=':')
except ValueError:
annot = None
return (annot)
def do_annotyp(annotyp):
try:
annot = pd.concat([get_annot(s, annotyp) for s in vcflist.index],
axis=0)
except ValueError:
annot = None
return (annot)
annot = pd.concat([do_annotyp(a) for a in annotlist], axis=1)
return (annot)
def create_colsdict():
'''
Create input dictionary for regularize_categ_cols
'''
colsdict = {}
# order reflecting severity of effect
l = [
'Deleterious', 'Deleterious - Low Confidence', 'Tolerated',
'Tolerated - Low Confidence'
]
colsdict.update({'sift_Prediction': l})
# order reflecting increasing frequency of categories in the data set
l = ['Polymerase', 'Open Chromatin', 'Transcription Factor', 'Histone']
colsdict.update({'encode_Feature Type Class': l})
l = [
'intronic (splice_site)', 'coding', 'intronic', '5utr', '3utr',
'5upstream', '3downstream', 'non-coding intronic', 'non-coding'
]
colsdict.update({'ensembl_Predicted Function': l})
def read_categories(fpath):
with open(fpath) as f:
val = f.readlines()
val = [x.strip() for x in val]
return (val)
regbuild_epigenomes = read_categories(
bsmutils.get_bsmdir() +
'/results/2020-09-07-annotations/regbuild-epigenomes')
colsdict.update({'regbuild_Epigenome': regbuild_epigenomes})
colsdict.update({'structvar_Type': ['complex', 'loss', 'gain']})
return (colsdict)
def load_data(picklepath=bsmutils.get_bsmdir() +
'/results/2020-09-07-annotations/annotated-calls.p'):
'''
Load annotated calls from pickle file
'''
with open(picklepath, 'rb') as f:
data = pickle.load(f)
return (data)
def readVCFs(vcflistpath=bsmutils.get_bsmdir() + '/results/calls/filtered-vcfs.tsv',
vcfdir=bsmutils.get_bsmdir() + '/results/calls/', clean=True):
'''
Reads the calls/records of several VCFs into rows of a single DataFrame
Arguments
vcflistpath: path to file listing all VCFs
vcfdir: the directory of the VCFs
clean: weather to remove redundant & degenerate columns
Value:
calls: a pandas DataFrame
'''
vcflist = pd.read_csv(vcflistpath, sep='\t', names=['sample', 'file'], index_col='sample')
vcflist['filepath'] = [vcfdir + os.sep + 'annotated' + os.sep + f for f in vcflist['file']]
l = [readVCF(y) for y in vcflist['filepath']]
calls = pd.concat(l, axis=0)
if clean:
calls = clean_calls(calls, dropna=True, dropdegenerate=True, dropredundant=True)
return(calls)
def do_annot(annotlist=annotlist,
na_values=na_values,
colsdict=create_colsdict(),
fpath=bsmutils.get_bsmdir() +
'/results/2020-09-07-annotations/annot.p',
calls=individuals.get_datasets()):
'''
Main function: read SNPnexus annotations for the full Chess and Walsh datasets
'''
if os.path.exists(fpath):
print('loading annot DataFrame from', fpath)
with open(fpath, 'rb') as f:
annot = pickle.load(f)
else:
vcflistpath = bsmutils.get_bsmdir(
) + '/results/calls/filtered-vcfs-Chess-Walsh.tsv'
annotdirpath = bsmutils.get_bsmdir(
) + '/results/2020-09-07-annotations'
annot = get_multi_annotations(annotlist, vcflistpath, annotdirpath,
na_values)
pickle.dump(annot, open(fpath, 'wb'))
return (annot)
def read_annotlist(annotpath=bsmutils.get_bsmdir() + '/tables/VCF-HC.annotations', withFORMAT=False):
'''
Reads a file containing list of annotations in VCFs into a list.
Parameters
annotpath: the path to the aforementioned file
withFORMAT: if False (default) the FORMAT fields are omitted
Value: the list of annotations
'''
with open(annotpath) as f:
l = f.readlines()
l = [y.replace('\n', '') for y in l] # remove newline characters
if not withFORMAT:
l = [y for y in l if not re.match('^FORMAT', y)]
return(l)
def read_clinical(ancestry=True):
# CMC_Human_clinical_metadata.csv
if not os.path.exists(cmc_clinical_path):
import synapseclient
syn = synapseclient.login()
wdir = bsmutils.get_bsmdir() + '/resources/'
clinical_syn = syn.get('syn2279441',
downloadLocation=wdir,
ifcollision='overwrite.local')
fpath = clinical_syn.path
else:
fpath = cmc_clinical_path
clinical = pd.read_csv(fpath, index_col='Individual ID')
if ancestry:
ancestry = pd.read_csv(cmc_ancestry_path,
sep='\t',
index_col='Individual_ID')
ancestry = ancestry.drop(columns=['Genotyping_Sample_ID', 'Cluster'])
clinical = pd.concat([clinical, ancestry], axis=1)
#clinical.columns = pd.MultiIndex.from_product([['Clinical'], calls.columns], names=['Source', 'Annotation'])
return (clinical)
targetdir=bsmutils.get_bsmdir() + '/results/calls/annotated/'):
'''
Some help would be nice
'''
script = bsmutils.get_bsmdir() + '/src/annotate-vcf-bsm'
cmd = [script, '-t', targetdir, invcf, sample]
p = subprocess.run(cmd, capture_output=True)
return(p)
def annotateVCFs(vcflistpath=bsmutils.get_bsmdir() + '/results/calls/filtered-vcfs.tsv',
vcfdir=bsmutils.get_bsmdir() + '/results/calls/'):
vcflist = pd.read_csv(vcflistpath, sep='\t', names=['sample', 'file'], index_col='sample')
def helper(sample):
invcf = vcfdir + os.path.sep + 'filtered' + os.path.sep + vcflist.loc[sample, 'file']
targetdir = vcfdir + os.path.sep + 'annotated' + os.path.sep
val = annotateVCF(invcf=invcf, sample=sample, targetdir=targetdir)
return(val)
pp = [helper(y) for y in vcflist.index]
return(pp)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dir', help='main VCF directory (bsm/results/calls/)',
default=bsmutils.get_bsmdir() + '/results/calls/')
parser.add_argument('-l', '--vcflist', help='list of samples and VCF files (bsm/results/calls/filtered-vcfs.tsv)',
default=bsmutils.get_bsmdir() + '/results/calls/filtered-vcfs.tsv')
args = parser.parse_args()
annotateVCFs(vcflistpath=args.vcflist, vcfdir=args.dir)
readVCFs(vcflistpath=args.vcflist, vcfdir=args.dir)
import scipy.stats
import numpy as np
import pandas as pd
import os.path
from bsmcalls import readVCF
from bsmcalls import preprocessing
import bsmutils
cmc_clinical_synid = 'syn2279441'
cmc_clinical_path = bsmutils.get_bsmdir(
) + '/resources/CMC_Human_clinical_metadata.csv'
cmc_ancestry_path = bsmutils.get_bsmdir(
) + '/resources/cmc-ancestry/CMC_MSSM-Penn-Pitt_DNA_GENOTYPE_ANCESTRY_GemTools.tsv'
walsh_gsub_path = bsmutils.get_bsmdir(
) + '/resources/walsh-manifests/genomics_subject02_template_WalshParkASD-corr.csv'
walsh_vcfs_path = bsmutils.get_bsmdir(
) + '/results/calls/filtered-vcfs-Walsh.tsv'
chess_vcfs_path = bsmutils.get_bsmdir() + '/results/calls/filtered-vcfs.tsv'
v1 = [
'AF', 'ALT', 'BaseQRankSum', 'DP', 'FILTER/PASS', 'FS', 'GWASpval', 'REF',
'ReadPosRankSum', 'SOR', 'VQSLOD', 'chromatinState_DLPFC', 'culprit',
'szdbCNVcount'
]
v2 = ['Dx', 'AntipsychAtyp', 'AntipsychTyp', 'Institution', 'EV.3']
def read_clinical(ancestry=True):
# CMC_Human_clinical_metadata.csv
if not os.path.exists(cmc_clinical_path):
import synapseclient
def get_geneset(df=pd.read_csv(
bsmutils.get_bsmdir() + '/resources/CLOZUK/supp-table-4.csv', skiprows=7),
col='Gene(s) tagged'):
val = df['Gene(s) tagged'].str.split(', ').dropna().sum()
geneset = set(val)
return (geneset)
import pandas as pd
import numpy as np
import bsmutils
roadmap_rna_bname = bsmutils.get_bsmdir(
) + '/resources/roadmap-epigenomics/rna/expression/57epigenomes.'
proteinatlas_rna_bname = bsmutils.get_bsmdir(
) + '/resources/proteinatlas/expression/tissue_category_rna_brain_'
def read_roadmap_rna(kind='RPKM',
sampledict={
'E071': 'BRN.HIPP.MID',
'E082': 'BRN.FET.F'
},
suffix=False):
if suffix:
sampledict = dict(
zip(sampledict.keys(),
[x + '_' + kind for x in sampledict.values()]))
fpath = roadmap_rna_bname + kind + '.pc'
df = pd.read_csv(fpath, sep='\t', index_col=0, usecols=sampledict.keys())
df = df.rename(sampledict, axis=1)
return (df)
def read_roadmap_rna_RPKM_N(sampledict={
'E071': 'BRN.HIPP.MID',
'E082': 'BRN.FET.F'
}):
l = [read_roadmap_rna(k, sampledict, suffix=True) for k in ['RPKM', 'N']]
