jblitzar/github-python · Datasets at Hugging Face

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# covmat_stops = covmat[np.ix_(elongating_orfs.values, elongating_orfs.values)]
# orf_strs_stops = orf_strs[elongating_orfs.values]
# stop_strength_df['stop_strength_nohistop'] = gstop_grps['orf_strength'].aggregate(np.sum)
# stop_strength_df['W_stop_nohistop'] = pd.Series({gstop:orf_strs_stops[rownums].dot(np.linalg.inv(covmat_stops[np.ix_(rownums,rownums)]))
# .dot(orf_strs_stops[rownums]) for (gstop, rownums) in gstop_grps.indices.iteritems()})

return orf_strength_df, start_strength_df, stop_strength_df
else:
return orf_strength_df, start_strength_df


def _regress_chrom(chrom_to_do):
"""Applies _regress_tfam() to all of the transcript families on a chromosome"""
chrom_orfs = pd.read_hdf(opts.orfstore, 'all_orfs', mode='r', where="chrom == %r and tstop > 0 and tcoord > 0" % chrom_to_do,
columns=['orfname', 'tfam', 'tid', 'tcoord', 'tstop', 'AAlen', 'chrom', 'gcoord', 'gstop', 'strand',
'codon', 'orftype', 'annot_start', 'annot_stop'])
# tcoord > 0 removes ORFs where the first codon is an NTG, to avoid an indexing error
# Those ORFs would never get called anyway since they couldn't possibly have any reads at their start codon

if opts.exclude:
chrom_orfs = chrom_orfs[~chrom_orfs['tfam'].isin(opts.exclude)]

if restrictbystartfilenames:
restrictedstarts = pd.DataFrame()
for (restrictbystart, minw) in zip(restrictbystartfilenames, opts.minwstart):
restrictedstarts = restrictedstarts.append(
pd.read_hdf(restrictbystart, 'start_strengths', mode='r', where="(chrom == %r) & (W_start > minw)" % chrom_to_do,
columns=['tfam', 'chrom', 'gcoord', 'strand']), ignore_index=True).drop_duplicates()
chrom_orfs = chrom_orfs.merge(restrictedstarts) # inner merge acts as a filter

if chrom_orfs.empty:
if opts.verbose > 1:
with log_lock:
logprint('No ORFs found on %s' % chrom_to_do)
return failure_return

inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]
gnd = HashedReadBAMGenomeArray(inbams, ReadKeyMapFactory(Pdict, read_length_nmis))

res = tuple([pd.concat(res_dfs) for res_dfs in zip(*[_regress_tfam(tfam_set, gnd) for (tfam, tfam_set) in chrom_orfs.groupby('tfam')])])

for inbam in inbams:
inbam.close()

if opts.verbose > 1:
with log_lock:
logprint('%s complete' % chrom_to_do)

return res

with pd.HDFStore(opts.orfstore, mode='r') as orfstore:
chroms = orfstore.select('all_orfs/meta/chrom/meta').values # because saved as categorical, this is the list of all chromosomes

if os.path.isfile(metafilename) and not opts.force:
if opts.verbose:
logprint('Loading metagene')

metagene = pd.read_csv(metafilename, sep='\t').set_index(['region', 'position'])
metagene.columns = metagene.columns.astype(int) # they are read lengths
assert (metagene.columns == rdlens).all()
startprof = metagene.loc['START']
cdsprof = metagene.loc['CDS']
stopprof = metagene.loc['STOP']
startnt = (startprof.index.min(), startprof.index.max()+1)
assert len(cdsprof) == 3
stopnt = (stopprof.index.min(), stopprof.index.max()+1)
startprof = startprof.values.T
cdsprof = cdsprof.values.T
stopprof = stopprof.values.T
else:
if opts.verbose:
logprint('Calculating metagene')

startnt = (-abs(opts.startrange[0])3, abs(opts.startrange[1])3) # force <=0 and >= 0 for the bounds
stopnt = (-abs(opts.stoprange[0])3, abs(opts.stoprange[1])3)

if stopnt[0] >= -6:
raise ValueError('STOPRANGE must encompass at least 3 codons prior to the stop')
min_AAlen = (startnt[1]-stopnt[0])/3 # actually should be longer than this to ensure at least one codon in the body
startlen = startnt[1]-startnt[0]
stoplen = stopnt[1]-stopnt[0]

workers = mp.Pool(opts.numproc)
(startprof, cdsprof, stopprof, num_cds_incl) = [sum(x) for x in zip(*workers.map(_get_annotated_counts_by_chrom, chroms))]
workers.close()

startprof /= num_cds_incl # technically not necessary, but helps for consistency of units across samples
cdsprof /= num_cds_incl
stopprof /= num_cds_incl

pd.concat((pd.DataFrame(data=startprof.T,
index=pd.MultiIndex.from_tuples([('START', x) for x in range(*startnt)], names=['region', 'position']),
columns=pd.Index(rdlens, name='rdlen')),
pd.DataFrame(data=cdsprof.T,
index=pd.MultiIndex.from_tuples([('CDS', x) for x in range(3)], names=['region', 'position']),
columns=pd.Index(rdlens, name='rdlen')),
pd.DataFrame(data=stopprof.T,
index=pd.MultiIndex.from_tuples([('STOP', x) for x in range(*stopnt)], names=['region', 'position']),
columns=pd.Index(rdlens, name='rdlen')))) \
.to_csv(metafilename, sep='\t')

# covmat_stops = covmat[np.ix_(elongating_orfs.values, elongating_orfs.values)]

# orf_strs_stops = orf_strs[elongating_orfs.values]

# stop_strength_df['stop_strength_nohistop'] = gstop_grps['orf_strength'].aggregate(np.sum)

# stop_strength_df['W_stop_nohistop'] = pd.Series({gstop:orf_strs_stops[rownums].dot(np.linalg.inv(covmat_stops[np.ix_(rownums,rownums)]))

# .dot(orf_strs_stops[rownums]) for (gstop, rownums) in gstop_grps.indices.iteritems()})

return orf_strength_df, start_strength_df, stop_strength_df

else:

return orf_strength_df, start_strength_df

def _regress_chrom(chrom_to_do):

"""Applies _regress_tfam() to all of the transcript families on a chromosome"""

chrom_orfs = pd.read_hdf(opts.orfstore, 'all_orfs', mode='r', where="chrom == %r and tstop > 0 and tcoord > 0" % chrom_to_do,

columns=['orfname', 'tfam', 'tid', 'tcoord', 'tstop', 'AAlen', 'chrom', 'gcoord', 'gstop', 'strand',

'codon', 'orftype', 'annot_start', 'annot_stop'])

# tcoord > 0 removes ORFs where the first codon is an NTG, to avoid an indexing error

# Those ORFs would never get called anyway since they couldn't possibly have any reads at their start codon

if opts.exclude:

chrom_orfs = chrom_orfs[~chrom_orfs['tfam'].isin(opts.exclude)]

if restrictbystartfilenames:

restrictedstarts = pd.DataFrame()

for (restrictbystart, minw) in zip(restrictbystartfilenames, opts.minwstart):

restrictedstarts = restrictedstarts.append(

pd.read_hdf(restrictbystart, 'start_strengths', mode='r', where="(chrom == %r) & (W_start > minw)" % chrom_to_do,

columns=['tfam', 'chrom', 'gcoord', 'strand']), ignore_index=True).drop_duplicates()

chrom_orfs = chrom_orfs.merge(restrictedstarts) # inner merge acts as a filter

if chrom_orfs.empty:

if opts.verbose > 1:

with log_lock:

logprint('No ORFs found on %s' % chrom_to_do)

return failure_return

inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]

gnd = HashedReadBAMGenomeArray(inbams, ReadKeyMapFactory(Pdict, read_length_nmis))

res = tuple([pd.concat(res_dfs) for res_dfs in zip(*[_regress_tfam(tfam_set, gnd) for (tfam, tfam_set) in chrom_orfs.groupby('tfam')])])

for inbam in inbams:

inbam.close()

if opts.verbose > 1:

with log_lock:

logprint('%s complete' % chrom_to_do)

return res

with pd.HDFStore(opts.orfstore, mode='r') as orfstore:

chroms = orfstore.select('all_orfs/meta/chrom/meta').values # because saved as categorical, this is the list of all chromosomes

if os.path.isfile(metafilename) and not opts.force:

if opts.verbose:

logprint('Loading metagene')

metagene = pd.read_csv(metafilename, sep='\t').set_index(['region', 'position'])

metagene.columns = metagene.columns.astype(int) # they are read lengths

assert (metagene.columns == rdlens).all()

startprof = metagene.loc['START']

cdsprof = metagene.loc['CDS']

stopprof = metagene.loc['STOP']

startnt = (startprof.index.min(), startprof.index.max()+1)

assert len(cdsprof) == 3

stopnt = (stopprof.index.min(), stopprof.index.max()+1)

startprof = startprof.values.T

cdsprof = cdsprof.values.T

stopprof = stopprof.values.T

else:

if opts.verbose:

logprint('Calculating metagene')

startnt = (-abs(opts.startrange[0])*3, abs(opts.startrange[1])*3) # force <=0 and >= 0 for the bounds

stopnt = (-abs(opts.stoprange[0])*3, abs(opts.stoprange[1])*3)

if stopnt[0] >= -6:

raise ValueError('STOPRANGE must encompass at least 3 codons prior to the stop')

min_AAlen = (startnt[1]-stopnt[0])/3 # actually should be longer than this to ensure at least one codon in the body

startlen = startnt[1]-startnt[0]

stoplen = stopnt[1]-stopnt[0]

workers = mp.Pool(opts.numproc)

(startprof, cdsprof, stopprof, num_cds_incl) = [sum(x) for x in zip(*workers.map(_get_annotated_counts_by_chrom, chroms))]

workers.close()

startprof /= num_cds_incl # technically not necessary, but helps for consistency of units across samples

cdsprof /= num_cds_incl

stopprof /= num_cds_incl

pd.concat((pd.DataFrame(data=startprof.T,

index=pd.MultiIndex.from_tuples([('START', x) for x in range(*startnt)], names=['region', 'position']),

columns=pd.Index(rdlens, name='rdlen')),

pd.DataFrame(data=cdsprof.T,

index=pd.MultiIndex.from_tuples([('CDS', x) for x in range(3)], names=['region', 'position']),

columns=pd.Index(rdlens, name='rdlen')),

pd.DataFrame(data=stopprof.T,

index=pd.MultiIndex.from_tuples([('STOP', x) for x in range(*stopnt)], names=['region', 'position']),

columns=pd.Index(rdlens, name='rdlen')))) \

.to_csv(metafilename, sep='\t')