def read_cpg_profiles(filenames, log=None, *args, **kwargs):
"""Read methylation profiles.
Input files can be gzip compressed.
Returns
-------
dict
`dict (key, value)`, where `key` is the output name and `value` the CpG
table.
"""
cpg_profiles = OrderedDict(
) #a dictionary which remember the order of item inserted, when iterating it,
#items are returned in the order their keys were first added.
for filename in filenames:
if log:
log(filename)
cpg_file = dat.GzipFile(
filename, 'r') #Wrapper to read and write gzip-compressed files.
output_name = split_ext(
filename) #Remove file extension from `filename`, defined above
cpg_profile = dat.read_cpg_profile(
cpg_file, sort=True, *args,
**kwargs) #Read CpG profile from TSV or bedGraph file.
#return :class:`pandas.DataFrame` with columns `chromo`, `pos`, `value`.
cpg_profiles[
output_name] = cpg_profile #cpg_profiles store multiple sample information
cpg_file.close()
return cpg_profiles #return ordered dictory, each item is a pandas data frame
def read_cpg_profiles(filenames, log=None, *args, **kwargs):
"""Read methylation profiles.
Input files can be gzip compressed.
Returns
-------
dict
`dict (key, value)`, where `key` is the output name and `value` the CpG
table.
"""
cpg_profiles = OrderedDict()
for filename in filenames:
if log:
log(filename)
cpg_file = dat.GzipFile(filename, 'r')
output_name = split_ext(filename)
cpg_profile = dat.read_cpg_profile(cpg_file,
sort=True,
*args,
**kwargs)
cpg_profiles[output_name] = cpg_profile
cpg_file.close()
return cpg_profiles
def read_cpg_profiles(filenames, *args, **kwargs):
cpg_profiles = OrderedDict()
for filename in filenames:
cpg_file = dat.GzipFile(filename, 'r')
output_name = split_ext(filename)
cpg_profile = dat.read_cpg_profile(cpg_file, sort=True, *args, **kwargs)
cpg_profiles[output_name] = cpg_profile
cpg_file.close()
return cpg_profiles
def annotate(anno_file, chromo, pos):
anno_file = dat.GzipFile(anno_file, 'r')
anno = pd.read_table(anno_file, header=None, usecols=[0, 1, 2],
dtype={0: 'str', 1: 'int32', 2: 'int32'})
anno_file.close()
anno.columns = ['chromo', 'start', 'end']
anno.chromo = anno.chromo.str.upper().str.replace('CHR', '')
anno = anno.loc[anno.chromo == chromo]
anno.sort_values('start', inplace=True)
start, end = an.join_overlapping(anno.start.values, anno.end.values)
anno = np.array(an.is_in(pos, start, end), dtype='int8')
return anno
