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Util.py
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Util.py
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'''
Copyleft Dec 17, 2015 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: airanmehr@gmail.com
'''
from __future__ import print_function
import numba
from UTILS import *
from UTILS.BED import *
from UTILS.VCF import *
from UTILS.EE import *
from UTILS.Genome import *
np.set_printoptions(linewidth=140, precision=5, suppress=True)
import os
try:
import readline
except:
pass
def googleDocURL(name,url):return '=HYPERLINK("{}","{}")'.format(url,name)
def FoldOn(y,foldOn):
x = y.copy(True)
if not (foldOn is None):
fold = x[foldOn] < 0.5
x.loc[fold, :] = 1 - x.loc[fold, :]
return x
def DataframetolaTexTable(DF, alignment=None, fname=None,shade=False):
"""
Args:
df: pandas dataframe
alignment: python list of allignment of columns; default is ['c',..]; use ['c', 'p{4in}', 'c', 'c'] for wrapping
fname: path to save latex table
Returns:
object:
Returns: latex table
"""
df=DF.copy(True)
if alignment is None: alignment = list('c' * (df.shape[1]))
sh=('',r'\rowcolor{Gray} ')[shade]
df.iloc[:,0]=df.iloc[:,0].astype(str)
df.iloc[::2,0]=sh + df.iloc[::2,0]
csv = r'\centering \begin{tabular}{' + '|'.join(alignment) + '}\n' + df.to_csv(sep='\t', index=False).replace('\t',
'\t&').replace(
'\n', '\\\\\n').replace('\\\\\n', '\\\\\hline\n', 1) + r'\end{tabular}'
csv = csv.replace("%", '\%').replace('inf',r'$\infty$')
if fname is not None:
with open(fname, 'w') as f: print >> f, csv
return csv
def burden():
f= PATH.UKBB+ 'chr21.vcf.gz.ann.gz.LOF.df'
a=pd.read_pickle(f)
a['REF_ID']=a.ID.apply(lambda x: x.split('_')[2])
a['ALT_ID'] = a.ID.apply(lambda x: x.split('_')[3])
a
def localOutliers(a, q=0.99,winSize = 2e6):
def f(xx):
window = int(winSize / 10000)
th =scan.scan3way(xx,window,f=lambda x: pd.Series(x).quantile(q))
return xx[xx >= th].loc[xx.name]
return a.loc[a.groupby(level=0).apply(f).index]
def makeCategory(df,field):
try:
df[field]=df[field].astype('category')
except:
pass
return df
class Dmel:
@staticmethod
def loadDGRP(i):
cmd = '/home/arya/bin/bcftools/bcftools view -r {}:{}-{} /pedigree2/projects/arya/Data/Dmelanogaster/DGRP/dgrp2.vcf.gz | grep -v "##"'.format(
i.CHROM, i.start, i.end)
a = execute(cmd, header=0)
a.iloc[:, 9:] = a.iloc[:, 9:].replace({'./.': '-1', '0/0': 0, '1/1': 1}).astype(int)
a.rename(columns={'#CHROM': 'CHROM'})
return a.set_index(['CHROM', 'POS'])
@staticmethod
def geneAssociations(relation=False):
"""
Args:
relation: if True returns 1 to N relation otherwise returns 1 to a set relation
Returns:
"""
# ftp://ftp.flybase.net/releases/current/precomputed_files/go/gene_association.fb.gz
g = pd.read_csv(utl.PATH.Dmel + 'gene_association.fb', sep='\t', comment='!', header=None)[
[8, 4, 1, 2]].drop_duplicates()
g.columns = ['ontology', 'go', 'fngn', 'name']
return g
@staticmethod
def geneCoordinates(assembly=5,allOrganisms=False):
# ftp://ftp.flybase.net/releases/FB2014_03/precomputed_files/genes/gene_map_table_fb_2014_03.tsv.gz
# ftp://ftp.flybase.net/releases/FB2016_04/precomputed_files/genes/gene_map_table_fb_2016_04.tsv.gz
fname=PATH.Dmel+('gene_map_table_fb_2016_04.tsv','gene_map_table_fb_2014_03.tsv')[assembly==5]
import re
try:
return pd.read_pickle(fname+'.{}.df'.format(('dmel','all')[allOrganisms]))
except:
col=4;# sequence_loc column
a=pd.read_csv(fname, sep='\t', comment='#',header=None,na_values='',keep_default_na=False).dropna(subset=[col])
df=pd.DataFrame([re.split('\..|:|\(',r[col])[:-1]+r[:2].tolist()+['\\' not in r[0]] for _,r in a.iterrows()])
df.columns=['CHROM','start','end','name','FBgn','dmel']
df[['start', 'end']] = df[['start', 'end']].astype(int)
df = df.sort_values(['CHROM', 'start'])
df.index=range(df.shape[0])
df.to_pickle(fname+'.all.df')
df=df[df.dmel]
df.index=range(df.shape[0])
df.to_pickle(fname+'.dmel.df')
return df[df.dmel]
@staticmethod
def getEuChromatin(scores):
def filter(x):
try:
return scores.loc[x.name][(x.loc[x.name].start <= scores.loc[x.name].index.values) & (
x.loc[x.name].end >= scores.loc[x.name].index.values)]
except:
pass
return Dmel.getEuChromatinCoordinates().groupby(level=0).apply(filter)
@staticmethod
def getEuChromatinCoordinates():
"""http://petrov.stanford.edu/cgi-bin/recombination-rates_updateR5.pl"""
a = pd.Series(
"""X : 1.22 .. 21.21
2L : 0.53 .. 18.87
2R : 1.87 .. 20.86
3L : 0.75 .. 19.02
3R : 2.58 .. 27.44 """.split('\n'))
return pd.DataFrame(a.apply(lambda x: [x.split(':')[0]] + x.split(':')[1].split('..')).tolist(),
columns=['CHROM', 'start', 'end']).applymap(str.strip).set_index('CHROM').astype(
float) * 1e6
@staticmethod
def getChromLen(ver=5):
return pd.read_csv(home + 'storage/Data/Dmelanogaster/refrence/dmel{}.fasta.fai'.format(ver), sep='\t',
header=None).replace({'dmel_mitochondrion_genome': 'M'}).rename(
columns={0: 'CHROM'}).set_index(['CHROM'])[1].rename("length")
loadSNPID = lambda: pd.read_csv(home+'storage/Data/Dmelanogaster/dm5.vcf', sep='\t', usecols=range(5),
header=None, comment='#', names=['CHROM', 'POS', 'ID', 'REF', 'ALT']).set_index(
['CHROM', 'POS'])
def loadGenes(Intervals=True):
a=pd.read_csv(PATH.data+'Human/WNG_1000GP_Phase3/gene_info.csv')[['chrom','pop','gene','POS_hg19']].rename(columns={'chrom':'CHROM','POS_hg19':'POS'})
a.CHROM=a.CHROM.apply(lambda x: INT(x[3:]))
a=a.set_index('pop')
if Intervals:
a['start']=a.POS-2e6
a['end']=a.POS+2e6
a['name']=a.gene
return a
def normalizeIHS(a,field=None):
if field is None:
field=a.columns[-1]
m=a.set_index('x')[field].groupby(level=0).mean().sort_index()
s=a.set_index('x')[field].groupby(level=0).std().sort_index()
return (a[field]-m.loc[a.x].values)/s.loc[a.x].values
def loadGap(assempbly=19,pad=0):
gap = pd.read_csv(PATH.data + 'Human/gaps/hg{}.gap'.format(assempbly), sep='\t')[
['chrom', 'chromStart', 'chromEnd']].rename(
columns={'chrom': 'CHROM', 'chromStart': 'start', 'chromEnd': 'end'}).reset_index()
gap.start -= pad;
gap.end += pad;
gap.loc[gap.start < 0, 'start'] = 0
gap.CHROM = gap.CHROM.apply(lambda x: INT(x[3:]));
gap=gap.set_index('CHROM')
return gap
def loadFst(fname):
a=pd.read_csv(fname,sep='\t');
a.CHROM=a.CHROM.replace({'22':22});a=a.set_index(['CHROM','POS']).sort_index().iloc[:,0].rename('Fst')
a=a.loc[range(1,23)]
return a[a>0]
def lite1d(a,q=0.9,cutoff=None):
return a[a>a.quantile(q)]
def MultiIndex(df):
return pd.MultiIndex.from_tuples(df.apply(lambda x: tuple(x),1).tolist(),names=df.columns)
def tmpFileName(x=None):
if x is None:
import uuid
tmpPath = home + 'storage/tmp'
os.system('mkdir -p ' + tmpPath)
return '/home/arya/storage/tmp/' + str(uuid.uuid4())
else:
os.system('rm -f {}*'.format(x))
def IBS(i,path='/home/arya/POP/HA/', onlyRefPopSNPs=False):
bcf = '/home/arya/bin/bcftools/bcftools'
plink = '/home/arya/bin/plink'
uid = tmpFileName()
VCF = path + 'chr{}.vcf.gz'.format(i.CHROM)
vcf = uid + '.vcf.gz'
ibs = uid
if onlyRefPopSNPs:
freqs = gz.Freq(i)
if freqs is not None:
pos = uid + '.vcf'
polymorphix(freqs.KGZ).reset_index().iloc[:, :2].to_csv(pos, sep='\t', header=None, index=False)
execute('{} view -R {} {} | {} -T {} -Oz -o {}'.format(bcf, BED.str(i), VCF, bcf, pos, vcf),False)
else:
execute('{} view -r {} {} -Oz -o {}'.format(bcf, BED.str(i), VCF, vcf),False)
from subprocess import Popen, PIPE, STDOUT, call
cmd = '{} --vcf {} --cluster --matrix --out {}'.format(plink, vcf, ibs)
execute(cmd,False)
# with open(os.devnull, 'w') as FNULL:
# call(cmd.split(), stdout=FNULL, stderr=FNULL)
names = pd.read_csv(ibs + '.mibs.id', sep='\t', header=None)[0].values
c = pd.read_csv(ibs + '.mibs', sep=' ', header=None).T.dropna().T
c.index = names;
c.columns = names
tmpFileName(uid)
return augmentIndex(c,path=path)
def augmentIndex(c,axes=[0,1],path='/home/arya/POP/HA/'):
pop = VCF.loadPanel(path+'panel').iloc[:, :-1].set_index('sample')
one=lambda i: MultiIndex(pop.loc[i].reset_index().rename(columns={'index': 'sample','super_pop':'sup'})[['sup','pop','sample']])
if 0 in axes:c.index= one(map(str,c.index))
if 1 in axes:c.columns = one(c.columns)
return c.sort_index(axis=0).sort_index(axis=1)
def slice(A,pops=None,axes=[0,1],maxn = int(1e6),I=None):
sort = lambda x: x.sort_index(0).sort_index(1)
m=sort(A)
f=lambda x: x.reset_index([0, 1], drop=True)
if I is not None:
if axes == [0]:
return f(m).loc[I.astype(str)]
return f(f(m).loc[I.astype(str)].T).T[I.astype(str)]
I = []
for p in pops:I += VCF.ID(p,maxn=maxn).astype(str).tolist()
if axes==[0]:
return sort(m.loc[pd.IndexSlice[:, :, I], :])
return sort(m.loc[pd.IndexSlice[:, :, I], pd.IndexSlice[:, :, I]])
def rankLogQ(a,positiveTail=True):
return (a.dropna().rank(ascending=not positiveTail)/a.dropna().size).apply(np.log10).abs()
def rankPercentile(a,measure=100,n=None):
if n is not None:
rq=(a.rank() + (n - a.shape[0])) / n
else:
rq=a.rank() / a.shape[0]
return (rq * measure).apply(np.ceil).astype(int)
def significantLog(a,q=0.05):
return a[a>abs(np.log10(q))]
def nxScan(a,w=50,step=5,name=None,minn=0,f=np.mean):
if name is None:
if a.name is not None:name=a.name
else:name='stat'
# print name
x=scan.Genome(a, f={name: f, 'n': len},winSize=w*1000,nsteps=step)
return x[x.n > minn]
def ihsScan(a,minn=0,topQuantile=0.05):
# print a
# if a[a >= a.quantile(0.999)].value_counts().size>1:
return nxScan(a[a>=a.quantile(0.95)],f=np.mean, minn=minn)
# else:return nxScan(a[a>=a.quantile(0.995)],f=np.mean, minn=minn)
return nxScan(significantLog(rankLogQ(a,positiveTail=positiveTail),q=topQuantile),minn=minn)
def UCSC(i):
return 'https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr11%3A14125000-14225000&hgsid=604049611_XYcrNVjMubaqAEYhMU3mOa75y1nA'
def saveSingletons(CHROM,f='/home/arya/POP/HA/GT/chr{}.df'):
f=f.format(CHROM)
a=pd.read_pickle(f)
super=lambda X: X.loc[:,map(lambda x: not isinstance(x,tuple), X.columns)]
a=super(a.iloc[:,1:])
b=a>0
a[b.sum(1)==1].reset_index()[['CHROM','POS']].to_csv(f.replace('.df','.singletonPos.vcf'),sep='\t',index=False,header=False)
def triPopColor(pops):
try:return {pops[0]: 'b', pops[1]: 'r', pops[2]: 'g'}
except: return {pops[0]: 'b', pops[1]: 'r'}
def saveBegelePosAsia(chrom):
a=pd.read_pickle('/home/arya/POP/KGZU+ALL/chr{}.df'.format(chrom))[['SAS','EAS']].reset_index(['ID','REF','ALT'],drop=True).loc[chrom]
b=pd.read_csv('/home/arya/storage/Data/Human/Beagle/hg19/chr{}.pos.vcf'.format(22),sep='\t',header=None)[1]
a=a.loc[b]
a=a[((a==0).sum(1)<2)&((a==1).sum(1)<2)].reset_index()
a['CHROM']=chrom
a[['CHROM','POS']].to_csv('/home/arya/POP/ASIA/chr{}.pos.vcf'.format(chrom),sep='\t',header=None,index=False)
def pbsi(i,pops):
def load(p1, p2):
import os
fname = '/home/arya/scan/Fst/{}.{}.gz'
if os.path.isfile(fname.format(p1, p2)):
a = gz.load(f=fname.format(p1, p2), i=i)
else:
a = gz.load(f=fname.format(p2, p1), i=i)
return a.dropna().rename(p1 + '\nvs\n' + p2).loc[i.CHROM]
HS = load(pops[0], pops[1])
HL = load(pops[0], pops[2])
SL = load(pops[1], pops[2])
dedup= lambda x: x[~x.index.duplicated()]
a = pd.concat(map(dedup,[HS, HL, SL]), 1)
a[a < 0] = 0
a=(1-a[a.iloc[:,0]>0].dropna()).apply(np.log).abs()
ff = lambda x: (x.iloc[:, 0] + x.iloc[:, 1] - x.iloc[:, 2]).rename('PBS') # .rolling(50,center=True).mean()
a=pd.concat([a,ff(a)],1)
# a = pd.concat([a, ff((1 - a[a < 1]).apply(np.log).dropna().abs()).round(3)], 1)
return a
def pbs(pop,popxp,outgroup,hudson=False):
n=None
try:
exit()
return loadPiarPop('/home/arya/scan/PBS/{}.{}.PBS'+('','Hudson')[hudson]+'.gz',pop,popxp)
except:
if hudson:
path=PATH.scan + 'SFS/{}.{}.df'
HS = FstHudson(loadPiarPop(path, pop, popxp).xs('pi', 0, 2), pop).rename('HS').dropna()
HL =FstHudson(loadPiarPop(path, pop, outgroup).xs('pi', 0, 2), pop).rename('HL').dropna()
SL =FstHudson(loadPiarPop(path, popxp, outgroup).xs('pi', 0, 2), popxp).rename('SL').dropna()
n=loadPiarPop(path, pop, popxp).xs('m', 0, 2)[pop].rename('n').astype(int)
else:
HS = loadPiarPop('/home/arya/scan/Fst/{}.{}.gz',pop,popxp).rename('HS').dropna()
HL = loadPiarPop('/home/arya/scan/Fst/{}.{}.gz',pop,outgroup).rename('HL').dropna()
SL = loadPiarPop('/home/arya/scan/Fst/{}.{}.gz',popxp,outgroup).rename('SL').dropna()
def pos(x):
x[x<=0]=0
return x
a = pos(GENOME.merge([HS,HL,SL])).fillna(0)
# print 'Merging is done',a.shape
# a = (1 - a[a <1]).apply(np.log).apply(lambda x: x.fillna(x.min()-1)).abs().round(2)
a = (1 - a[a < 1]).apply(np.log).dropna().abs().round(3)
a=(a.HS + a.HL - a.SL).rename('PBS'+('','Hudson')[hudson])
if n is not None: a=pd.concat([a,n],1).dropna()
gz.save(a,'/home/arya/scan/PBS/{}.{}.PBS{}.gz'.format(pop,popxp,('','Hudson')[hudson]))
return a
def loadHLIMetrics(path='~/storage/Data/Human/Tibet/HLI'):
def picardMetrics():
a=execute("find {} | grep _collect_wgs_metrics_output.txt".format(path))[0]
i=a.apply(lambda x: os.path.basename(x).split('_')[0]).rename('ID')
a=a.apply(lambda x: execute("head -n8 {} | grep -v '#'".format(x)).T.set_index(0)[1])
a.index=i
return a
def freemix():
a = execute("find {} | grep selfSM".format(path))[0]
a=a.apply(lambda x: execute('cat {}'.format(x)).T.set_index(0)[1]).set_index('#SEQ_ID')
a.index.name='ID'
return a
return pd.concat([picardMetrics(),freemix()],1)
def removeChr(a):
IDX='CHROM' in a.index.names
if IDX:a=a.reset_index()
a.CHROM=a.CHROM.apply(lambda x: INT(str(x).replace('chr','')))
if IDX:a=a.set_index(['CHROM','POS'])
return a
def loadWNG(pop,padding=2000000):
def get_interval(a, padding=padding):
b = a.reset_index()
b['start'] = b.POS - padding
b['end'] = b.POS + padding
return b
# a=pd.read_csv(dataPath + 'Human/scan/gene-info.csv').set_index(['pop'])
if pop in ['Healthy', 'No-HAPH', 'KGZ']: pop = 'KGZ'
a=pd.read_pickle(PATH.data + 'Human/scan/WNG.df').set_index('pop').loc[[pop]]
a.start-=padding;a.start[a.start<0]=0
a.end += padding
return a
def mergeResults(path='/home/arya/POP/HAT/CEU+CHB/CEU/',f='chr{}.xpehh.CEU.CHB.gz',out='xpehh.CEU.CHB',CHROMS=range(1,23),outpath=None):
if outpath==None: outpath=path
os.system('rm -f ' + path + out)
for c in CHROMS:os.system('zcat {} >> {}'.format( path + f.format(c),outpath+out))
os.system('bgzip -c {0} > {1} && tabix -p vcf {1} && rm -f {0}'.format(outpath+out,outpath+out+'.gz'))
def FstHudson(a,pop):
b=a.join(((a['all'] * 4 - a.loc[:,a.columns!='all'].sum(1)) / 2).rename('between'))
c=(b.between- b[pop])/b.between
c[(b.between<0) | (c<0)]=0
return c.dropna()
def gkern(kernlen=21, nsig=3):
"""Returns a 2D Gaussian kernel Matrix"""
interval = (2*nsig+1.)/(kernlen)
x = np.linspace(-nsig-interval/2., nsig+interval/2., kernlen+1)
kern1d = np.diff(st.norm.cdf(x))
kernel_raw = np.sqrt(np.outer(kern1d, kern1d))
kernel = kernel_raw/kernel_raw.sum()
return kernel
from scipy.signal import convolve2d
import numpy as np
import scipy.stats as st
def gkern(kernlen=21, nsig=3):
"""Returns a 2D Gaussian kernel array."""
interval = (2 * nsig + 1.) / (kernlen)
x = np.linspace(-nsig - interval / 2., nsig + interval / 2., kernlen + 1)
kern1d = np.diff(st.norm.cdf(x))
kernel_raw = np.sqrt(np.outer(kern1d, kern1d))
kernel = kernel_raw / kernel_raw.sum()
return kernel
def dens(b, filters=[5, 10, 20, 20]):
conv = lambda x, k: pd.DataFrame(convolve2d(x.fillna(0), gkern(k, 1), mode='same'), index=x.index,
columns=x.columns).replace({0: np.nan})
x = b.copy(True)
for k in filters:
x = conv(x, k)
return x
def outlier2D(a,k=200):
from scipy.signal import convolve2d
def dens(b, filters=[5, 10, 20]):
conv = lambda x, k: pd.DataFrame(convolve2d(x.fillna(0), gkern(k, 1), mode='same'), index=x.index,
columns=x.columns).replace({0: np.nan})
x = b.copy(True)
for k in filters:
x = conv(x, k)
return x
top = a.set_index('RQ').loc[a.RQ.max()]['LRQP'].unique()
b = a[['n', 'LRQP']].astype(str)
b = (b.n + '.' + b['LRQP']).value_counts()
b.index = pd.MultiIndex.from_tuples(map(lambda x: tuple(map(int, x.split('.'))), b.index.tolist()))
b = b.unstack(0).sort_index(ascending=False)
c = pd.concat([b.stack().rename('n'), dens(b)[~b.isnull()].loc[top].stack().rename('prob')],
1).dropna().sort_values(
'prob')
# o = c[c.n.cumsum() <= a.shape[0] / 1000] # top .1 %ile #
try:
o=pd.concat([c[c.n.cumsum()<=k],c[c.n.cumsum()>k].iloc[[0]]])
except:
# print c
o=c[c.n.cumsum()<=k]
o = a.reset_index().set_index(['LRQP', 'n']).loc[o.index].reset_index().set_index(['CHROM', 'POS'])
return o
def damp(x , a=1):
return x.apply(lambda y: y+a*np.sign(y))
def describe(a,cond=True):
if cond:
f = lambda x: describe(x,False )
x=a
return pd.concat([f(x),f(x[x>0]),f(-x),f(x[x>x.median()]),f(x[x>=x.quantile(0.95)]),f(x[x>=x.quantile(0.99)])],1,keys=['all','pos','median','q95','q99'])
else:
desc = a.describe()
desc['q9'] = a.quantile(0.9)
desc['q95'] = a.quantile(0.95)
desc['q99'] = a.quantile(0.99)
desc['q999'] = a.quantile(0.999)
desc['q9999'] = a.quantile(0.9999)
desc['q99999'] = a.quantile(0.99999)
return desc
def describe2Tail(a):
return pd.concat([describe(a),describe(-a)],1,keys=['+','-'])
def savePermille(fname,n=None):
a=gz.load(fname)
a=a.loc[:, (a.iloc[0].apply(type) != str).tolist()]
gz.save(a.apply(lambda x: rankPercentile(x, 1e3,n)),fname.replace('.gz','.permille.gz'))
gz.save(a.apply(lambda x: rankPercentile(x, 1e6, n)), fname.replace('.gz', '.ppm.gz'))
def savePermilleTruncated(pops,f='/home/arya/scan/Fisher/{}.{}.gz'):
try:
n=pd.read_pickle('/home/arya/Kyrgyz/scan/{}.{}.AF.desc.snp.df'.format(pops[0],pops[1]))['+']['all']['count']
# print pops[:2],"{:,}".format(int(n))
savePermille(f.format(pops[0],pops[1]),n)
except:
print('Error', pops)
def loadAprioriList(i=0,x=None):
a=pd.read_pickle('/home/arya/Kyrgyz/aprioriList/all.df')
if x is not None:
print('Loading {} List'.format(x))
a = a.loc[x]
else:
print('Loading {} List'.format(a.index.levels[0][i]))
a = a.loc[a.index.levels[0][i]]
a=a.set_index('Category').loc['Protein Coding'].sort_values('Relevance score',ascending=False)
return a.drop(['Gifts', 'GC Id'],1).reset_index(drop=True).rename(columns={'Relevance score':'GCscore'})
split = lambda x, i: [x[:i], x[i:]]
import random
def randomPartitionSet(seq,n):
return split(random.sample(seq, len(seq)), n)
def loadUKBB_ANN(CHROM=None,path=PATH.UKBB):
if CHROM is None:
return pd.concat(map(lambda x: loadUKBB_ANN(x,path),range(1,23)+['X']))
return pd.read_pickle(path+'chr{}.vcf.gz.ANN.sdf'.format(CHROM))
def intervalsSNPEff(CHROM=None,path=None,fname=None):
# print CHROM
if fname is not None:
g = pd.read_pickle(fname)
else:
g=pd.read_pickle(path+'chr{}.ANN.sdf'.format(CHROM)).loc[[CHROM]]
g=g.reset_index().set_index('Feature_Type').sort_index().xs('transcript').reset_index(drop=True).set_index(['CHROM','POS']).sort_index()
a= scan.Genome(g.Gene_Name.iloc[:], f=lambda x: [x.unique()]).apply(lambda x: list(x[0])).rename('genes')
b= scan.Genome(g.Annotation.iloc[:], f=lambda x: [x.value_counts()]).apply(lambda x: x[0])
c= scan.Genome(g.Annotation_Impact.iloc[:], f=lambda x: [x.value_counts()]).apply(lambda x: x[0])
if fname is not None:
pd.concat([a, b, c], 1).to_pickle(fname+'.idf')
else:
pd.concat([a,b,c],1).to_pickle(path+'chr{}.ANN.idf'.format(CHROM))
def loadFreqs(pops,i,AA=False,pad=0):
f=('/home/arya/POP/KGZU+ALL/GT/AF.gz','/home/arya/POP/KGZU+ALL/GT/DAF.gz')[AA]
# f = ('/home/arya/POP/HAT/GT/AF.gz', '/home/arya/POP/HAT/GT/DAF.gz')[AA]
try:
if '.' in pops[0]:
x=loadHealthyFreqs(pops[0].split('.')[0],BED.expand(i, pad))
x = polymorphixDF(pd.concat([x,gz.load(i=BED.expand(i, pad), f=f, indvs=pops[1:])],1))
else:
x=polymorphixDF(gz.load(i=BED.expand(i, pad), f=f, indvs=pops))
return x
except:
pass
def loadHealthyFreqs(pop,i,AA=False,pad=0):
try:return (gz.load('/home/arya/POP/HAT/GT/chr{}.vcf.gz', i, indvs=healthyIndvs(pop)).mean(1) / 2).rename(pop+'.HLT')
except:
pass
def healthyIndvs(pop):
return VCF.loadPanel().set_index(['super_pop', 'pop']).sort_index().loc[(pop, 'HLT'), 'sample'].tolist()
def scanAndPlot(a,f,k=100,ann=None,winSize=50000):
b= scan.Genome(a, f,winSize=winSize)
import UTILS.Plots as pplt
pplt.Manhattan(b, top_k=k)
# if winSize=500
I = BED.getIntervals(b.sort_values().iloc[-k:], padding=50000, ann=ann, expandedIntervalGenes=True)
import pylab as plt
plt.suptitle(intervalStats(I))
return I
def intervalStats(I):
return '{} genes in {} intervals of total {}Kb length '.format( len(getGeneList(I.genes)), I.shape[0], I.len.sum()/1000)
class skit:
def __init__(self,i,pops,path = '/home/arya/POP/KGZU+ALL/GT/'):
self.gt = skit.getGT(i, pops, path)
self.p1, self.p2, self.p3 = skit.getIndvs(pops)
@staticmethod
def getGT(i,pops,path):
freq = gz.load(path+'AF.gz', i, indvs=pops).loc[i.CHROM]
freq = freq[(freq[pops[0]] - freq[pops[1]]).round(2).abs() > 0.0]
gt = gz.load(path+'chr{}.vcf.gz', i, indvs=VCF.IDs(pops)).loc[i.CHROM].loc[freq.index].dropna()
return gt
@staticmethod
def getIndvs(pops):
p1 = VCF.ID(pops[0]).tolist()
p2 = VCF.ID(pops[1]).tolist()
p3=None
if len(pops)>2:p3 = VCF.ID(pops[2]).tolist()
return p1,p2,p3
@staticmethod
def __PBS(gt, p1, p2, p3):
T=lambda x: abs(np.log(1-x))
return (T(skit.__FstWin(gt, p1, p2)) +T(skit.__FstWin(gt, p1, p3)) - T(skit.__FstWin(gt, p2, p3)))/2.
@staticmethod
def __FstWin(gt, p1, p2, weir=True):
"""
conda install -c conda-forge scikit-allel
"""
import allel
gt = gt[p1 + p2]
id = pd.Series(range(gt.shape[1]), index=gt.columns)
subpops = [id.loc[p1].tolist(), id.loc[p2].tolist()]
m = {0: [0, 0], 1: [0, 1], 2: [1, 1]}
g = np.array(gt.applymap(lambda x: m[x]).values.tolist())
n = g.shape[0]
if weir:
f = allel.stats.windowed_weir_cockerham_fst
a = f(np.arange(n), g, subpops, size=n, start=0, stop=n - 1)[0][0]
else:
f = allel.stats.windowed_hudson_fst
g = allel.GenotypeArray(g)
ac1 = g.count_alleles(subpop=subpops[0])
ac2 = g.count_alleles(subpop=subpops[1])
a = f(np.arange(n), ac1, ac2, size=n, start=0, stop=n - 1)[0][0]
if a<0: a=0
return a
def PBS(self):
return skit.__PBS(self.gt,self.p1,self.p2,self.p3)
def FstWin(self):
return skit.__FstWin(self.gt, self.p1, self.p2)
def computeEmpiricalPvalPBS(self,k=1000):
obs = self.PBS()
p12 = self.p1 + self.p2
def _null():
p1, p2 = randomPartitionSet(p12, len(self.p2))
a=skit.__PBS(self.gt,p1,p2 , self.p3)
# print a
return a
null=pd.Series([_null() for _ in range(100)])
# return null
if k>100:
if (pd.Series(null) >= obs).mean() <= 0.1:
null = pd.Series([_null() for _ in range(k-100)]).append(null)
return (null >= obs).mean()
def computeEmpiricalPvalPBSHelper(args):
i,pops=args
return skit(i, pops).computeEmpiricalPvalPBS(k=100)
def loadGTEx(i,tissue='Lung',f='/home/arya/GTEx/PAH/tabix/chr{}.{}.allpairs.txt.gz'):
import mygene
"""
conda install -c bioconda mygene
"""
mg = mygene.MyGeneInfo()
a=gz.load(f=f.format(i.CHROM,tissue),i=i)
g=a.gene.drop_duplicates().tolist()
def query(x):
try: return mg.query(x.split('.')[0], scopes='ensembl.gene')['hits'][0]['symbol']
except: pass
g=pd.Series(map(query,g),index=g)
return a.replace({'gene':g.to_dict()}).dropna()
def sfs(x,rangeIndex=False,strIndex=False,norm=False):
y= pd.cut( x.values,bins=np.arange(0,1.01,0.1),include_lowest=True).value_counts()
if strIndex:
f=lambda x: '{:.1f}'.format(x.right)
else:
f = lambda x: np.round(x.right,1)
if not rangeIndex:
y=pd.Series(y.values, index=map(f, y.index.values))
if norm: y=y/y.sum()
return y
def loadPlinkGTgz(f,header=True,dtype=None):
"""
:param f: recode A created
:return:
"""
if header:
a = pd.read_csv(f, sep='\t', index_col=0,dtype=dtype)
a.columns = map(lambda x: '_'.join(x.split('_')[:-1]), a.columns)
else:
a = pd.read_csv(f, sep='\t', index_col=0, header=None,dtype=dtype)
return a
def loadPlinkFreq(x,pop,AC=False,ID=False,name=False,level=False,CHROM=False,flat=False,splitID=False,path=PATH.data+'Human/1KG/hg38/bed/pgen/refFromFa/CPRA/AF/'):
if pop=='UKBB':
path=PATH.UKBB+'AF/'
ac = ['', ',7'][AC]
id = ['', ',3'][ID|splitID]
cols = ['POS', 'ID','REF','ALT', 'freq','AC']
if not AC: cols=np.delete(cols, -1)
if not len(id): cols = np.delete(cols, 1)
f=path+'{}.gz'.format(pop)
if x =='.':
a=execute('zcat {} |cut -f2{},4,5,6{}'.format(f, id, ac))
else:
a = execute('{}bin/tabix {} {} |cut -f2{},4,5,6{}'.format(home, f, x,id, ac))
a.columns=cols
a.freq = a.freq.astype(float)
a.POS=a.POS.astype(int)
a=a.set_index('POS')
if splitID:
a[1] = a.ID.apply(lambda x: x.split('_')[2:])
a['REF_ID'] = a[1].apply(lambda x: x[0])
a['ALT_ID'] = a[1].apply(lambda x: x[1])
a=a.drop(1,1)
if (not ID) and (splitID):
a = a.drop('ID', 1)
if name: a=a.rename(columns={'freq':pop})
if level: a=pd.concat([a],1,keys=[pop])
if flat: a=renameColumns(a,'_'+pop,False)
if CHROM:
if x=='.':
a['CHROM'] = a.ID.apply(lambda x: INT(x.split('_')[0]))
a.index.name='POS'
a=a.reset_index().set_index(['CHROM','POS'])
else:
a = pd.concat([a], keys=[x])
a.index.names=['CHROM','POS']
return a
def combinations(seq,minSize=1):
from itertools import combinations, chain
a= map(list, list(chain(*map(lambda n: combinations(seq, n), range(len(seq)+1)))))
return [x for x in a if len(x)>=minSize]
def GBD():
f='/home/ubuntu/storage/Data/Human/GBD/CAD.df'
try:
aa=pd.read_pickle(f)
except:
aa=pd.read_csv('/home/ubuntu/storage/Data/Human/GBD/GBD_USA2016_CAD_Incidence_Prevalence_Rate.csv', index_col=[0,3])['sex val'.split()]
aa.sex=aa.sex.replace({'Male':1,'Female':0})
aa=aa.set_index('sex',append=True)['val'].unstack(level=0)
aa.to_pickle(f)
return aa
def ICD10(a=None,index=False):
import pandas as pd
if a is None:
return pd.read_csv('/home/ubuntu/storage/Data/Human/ICD10/codes.txt', sep=';', header=None, index_col=7)[8]
aa=pd.Series(a.index).rename('ICD10').reset_index()
i=aa.columns[:-1].tolist()
pd.options.display.max_colwidth = 1000
aa=aa.set_index(aa.columns[-1]).join(ICD10().rename('Desc')).reset_index().set_index(i)
if index:
print('hi')
aa=pd.DataFrame(a).join(aa.set_index('ICD10'))
return aa
def ICD10Super():
"https://www.icd10data.com/ICD10CM/Codes"
return pd.DataFrame(pd.Series("""A00-B99 Certain infectious and parasitic diseases
C00-D49 Neoplasms
D50-D89 Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism
E00-E89 Endocrine, nutritional and metabolic diseases
F01-F99 Mental, Behavioral and Neurodevelopmental disorders
G00-G99 Diseases of the nervous system
H00-H59 Diseases of the eye and adnexa
H60-H95 Diseases of the ear and mastoid process
I00-I99 Diseases of the circulatory system
J00-J99 Diseases of the respiratory system
K00-K95 Diseases of the digestive system
L00-L99 Diseases of the skin and subcutaneous tissue
M00-M99 Diseases of the musculoskeletal system and connective tissue
N00-N99 Diseases of the genitourinary system
O00-O9A Pregnancy, childbirth and the puerperium
P00-P96 Certain conditions originating in the perinatal period
Q00-Q99 Congenital malformations, deformations and chromosomal abnormalities
R00-R99 Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified
S00-T88 Injury, poisoning and certain other consequences of external causes
V00-Y99 External causes of morbidity
Z00-Z99 Factors influencing health status and contact with health services""".split('\n')).apply(lambda x: x.split(' ')).tolist()).set_index(0)[1]
def superDisease(heart=True):
h= pd.DataFrame(pd.Series("""K00-K95 Digestive
O00-O99 Pregnancy
I00-I09 Rheumatic
I10-I16 Hypertensive
I20-I25 Ischemic
I26-I28 Pulmonary
I30-I52 OtherHeart
I60-I69 Cerebrovascular
I70-I79 Arteries
I80-I89 Veins
I95-I99 OtherCirc""".split('\n')).apply(lambda x: x.strip().split(' ')).tolist()).set_index(1)[0]
o= pd.Series({'Diabetes':'E08-E13','Neoplasms':'C00-D49','Mental':'F01-F99','Respiratory':'J00-J99','Metabolic':'E70-E88','Thyroid':'E00-E07','Symptoms':'R00-R09'})
return pd.concat([o,h]).drop('Ischemic')
def binaryHist(A):
a=A.astype(int)
z=a.iloc[:,0].astype(str)
for c in a.columns[1:]:
z=z+a[c].astype(str)
return z.value_counts().rename('.'.join(map(str,a.columns)))
def AX(FIG=False,figsize=(8, 6)):
import pylab as plt
fig,ax=plt.subplots(1, 1,figsize= figsize, dpi=144)
if FIG: return fig
return ax
def plinkGT(CHROM,POS=None,verbose=False,cat=False,fold=False,ids=None):
if POS is None:
if isinstance(CHROM,list):
return pd.concat(map(plinkGT,CHROM),1)
CHROM,POS=CHROM
path=PATH.UKBB
import uuid
f='/tmp/'+str(uuid.uuid4())
cmd=home+'bin/plink2 --bpfile {3}chr{0} --from-bp {1} --to-bp {1} --chr {0} --out {2} --recode A > /dev/null 2>&1'.format(CHROM,POS,f,path)
if verbose:
print(cmd)
os.system( cmd);
a=pd.read_csv(f+'.raw',sep='\t').set_index('IID').iloc[:,-1].round().astype(np.int8)
os.system('rm -f ' + f + '.*')
if cat:a=a.astype('category')
# a.columns=map(lambda x: '_'.join(x.split('_')[:-1]),a.columns)
a.name= '_'.join( a.name.split('_')[:-1])
if fold and not cat:
if a.mean()>1:
a=2-a
if ids is not None:
a=a.loc[np.intersect1d(ids,a.index)]
return a
def loadParquetHelper(x):
y=pd.read_parquet(x)
if 'CPRA' in y.columns:
y=y.set_index('CPRA')
return y
def loadParquet(f,nProc=8):
import glob,multiprocessing
files = glob.glob(f+'/*.parquet')
pool = multiprocessing.Pool(nProc)
a= pd.concat(map(loadParquetHelper, files))
pool.terminate()
return a
def saveDF(df,fname,index=True):
df.to_pickle(fname)
df.to_csv(fname.replace('.df','.tsv.gz'),sep='\t',compression='gzip',index=index)
class LDPrune():
def __init__(self,f='/home/ubuntu/aux3/GWAS/train/AF/UKBB_Males.gz'):
print('Loading')
a = pd.read_csv(f, sep='\t', header=None)
a=a.join(MAF(a[5]).round(3).rename('xf'))
a['i']=a.xf.astype('category').cat.codes
a=a[[2,'i']].set_index('i')[2]
self.a=a
@staticmethod
def prune(f='/home/ubuntu/aux3/GWAS/train/LDPrune/ld.500.snps.ld.gz',cutoff=0.9):
k=pd.read_csv(f,sep='\t',header=None)
k=pd.DataFrame(k[0].apply(lambda x: x.split()).tolist()).T.set_index(0).T['SNP_A SNP_B R'.split()].set_index('SNP_A SNP_B'.split()).R.astype(float).abs()
# k.value_counts().sort_index()
# i=k.sort_values().index[300]
# c=i[0].split('_')[0]
# z=pd.concat([utl.plinkGT(c,i[0].split('_')[1]),utl.plinkGT(c,i[1].split('_')[1])],1).T.reset_index(drop=True).T
# utl.binaryHist(z),z.mean()
k=k[k.abs()>cutoff]
if k.min()<-cutoff:
print('Warning neglinkage found in',f)
kk=k.astype(bool)
keep,drop=[],[]
for i, row in kk.iloc[:].groupby(level=0):
if i not in drop:
keep+=[i]
drop+=TI(kk.loc[i]).tolist()
elif i not in keep:
drop += TI(kk.loc[i]).tolist()
keep,drop=set(keep),set(drop)
drop=[x for x in drop if x not in np.intersect1d(keep,drop)]
return drop
@staticmethod
def runBin(i,ids):
fout = home + 'train/LDPrune/ld.{}.snps'.format(int(i))
try:
print(i,'loaded')
return pd.read_csv(fout+'.drop')[0].tolist()
except:
pd.Series(ids).to_csv(fout, index=False)
pl='/home/ubuntu/bin/plink'
bed='/home/ubuntu/aux3/GWAS/train/all'
execute( '{} -bfile {} --r gz --extract {} -out {}'.format(pl,bed,fout,fout),returnDF=False)
drop=LDPrune.prune(fout+'.ld.gz')
pd.Series(drop).to_csv(fout+'.drop',index=False)
return drop
def run(self):
args=[(x,y.tolist()) for x,y in self.a.groupby(level=[0])]
from multiprocessing import Pool
DROP=pd.concat(Pool(6).map(LDHelper,args))
D=pd.DataFrame(DROP.tolist()).stack().drop_duplicates().dropna().reset_index(drop=True)
saveDF(D,home+'train/LDPrune/DROP.df',index=False)
@staticmethod
def getCP(x):
y = pd.DataFrame(x.apply(lambda x: x.split('_')).tolist(), columns='CHROM POS REF ALT'.split())
y.POS = y.POS.astype(int)
y.CHROM = y.CHROM.apply(INT)
return pd.Series(x.values, y.set_index(['CHROM', 'POS']).index).sort_index()
@staticmethod
def filter():
ld = pd.read_csv(home + 'train/LDPrune/DROP.tsv.gz', header=None)[0]
ld = pd.DataFrame(LDPrune.getCP(ld).rename('ld'))
all = pd.read_csv(home + 'train/all.bim', header=None, sep='\t')[1]
all = pd.DataFrame(LDPrune.getCP(all).rename('all'))
c = GENOME.merge([all, ld], None)
c[c.ld.isnull()]['all'].reset_index(drop=True).to_csv(home + 'train/LDPrune/KEEP.tsv.gz', index=False,
compression='gzip')
# utl.LDPrune().run();utl.LDPrune().filter()
def LDHelper(args):
i, ids=args
i=int(i)
try:
return pd.Series([LDPrune.runBin(i,ids)],index=[i])
except:
print('error in',i)
return []
def reinstallAILibs():
execute('/databricks/python/bin/pip install --upgrade git+https://airanmehr:d21eb460800eb76f46c664e7601689d69ead20dd@github.hli.io/airanmehr/AILibs.git', returnDF=False)
print('AILibs sucessfully was installed. Now detach and reattach your Notebook.')
class PLINKGWAS():
@staticmethod
def load(X,CHROMS=list(range(1,23))+['X'],col='P',GWASID=1,cutoff=None,takelog=True,
path= PATH.UKBB+'GWAS/train/gwas{}/'):
"""
:param col: OR or P
"""
CP = 'CHROM POS'.split()