def load_region(callset, chrom, start_position=0, stop_position=None,
variants_fields=None,
calldata_fields=None,
variants_query=None,
samples=None):
"""Load data into memory from `callset` for the given region.
Parameters
----------
callset : HDF5 file or group
A file or group containing a variant call set.
chrom : string
The chromosome to extract data for.
start_position : int, optional
The start position for the region to extract data for.
stop_position : int, optional
The stop position for the region to extract data for.
variants_fields : sequence of strings, optional
Names of the variants datasets to extract.
calldata_fields : sequence of strings, optional
Names of the calldata datasets to extract.
variants_query : string, optional
A query to filter variants. Note that this query is applied
after data for the region has been loaded, so any fields
referenced in this query need to be included in `variants_fields`.
samples : sequence of strings, optional
Selected samples to extract.
Returns
-------
variants : dict
A dictionary mapping dataset identifiers to ndarrays.
calldata : dict
A dictionary mapping dataset identifiers to ndarrays.
"""
# obtain chromosome group
grp_chrom = callset[chrom]
# setup output variables
variants = dict()
calldata = dict()
# obtain variant positions
pos = grp_chrom['variants']['POS']
# select samples needs list of all samples, check one is stored in the
# callset and fail early if not
all_samples = None
if samples is not None:
# find all samples
if 'samples' in callset.keys():
all_samples = list(callset['samples'])
elif 'samples' in grp_chrom.keys():
all_samples = list(grp_chrom['samples'])
else:
raise Exception('list of all samples not found in callset')
# locate region
loc = anhima.loc.locate_interval(pos, start_position, stop_position)
# extract variants data
if variants_fields:
if isinstance(variants_fields, string_types):
variants_fields = [variants_fields]
for f in variants_fields:
variants[f] = grp_chrom['variants'][f][loc, ...]
# extract calldata
if calldata_fields:
if isinstance(calldata_fields, string_types):
calldata_fields = [calldata_fields]
for f in calldata_fields:
calldata[f] = grp_chrom['calldata'][f][loc, ...]
# select variants
if variants_query is not None:
condition = numexpr.evaluate(variants_query, local_dict=variants)
for f in variants:
variants[f] = np.compress(condition, variants[f], axis=0)
for f in calldata:
calldata[f] = np.compress(condition, calldata[f], axis=0)
# select samples
if samples is not None:
# TODO check dtype of all_samples
samples = force_bytes(samples)
sample_indices = [all_samples.index(s) for s in samples]
for f in calldata:
calldata[f] = np.take(calldata[f], sample_indices, axis=1)
return variants, calldata
def save_tped(path, callset, chrom,
start_position=0,
stop_position=None,
samples=None):
"""Save genotype data from an HDF5 callset to a Plink transposed format
file (TPED).
Parameters
----------
path : string or file-like
Path of file to write, or file-like object to write to.
callset : HDF5 file or group
A file or group containing a variant call set.
chrom : string
The chromosome to extract data for.
start_position : int, optional
The start position for the region to extract data for.
stop_position : int, optional
The stop position for the region to extract data for.
samples : sequence of strings, optional
Selection of samples to extract genotypes for, defaults to all samples.
Notes
-----
Note that the current implementation loads all data from the requested
region into memory before writing out to TPED, so may not be applicable
to very large datasets.
"""
variants, calldata = load_region(callset,
chrom,
start_position,
stop_position,
variants_fields=['POS', 'REF', 'ALT'],
calldata_fields=['genotype'])
# determine samples that we will use
if samples is None:
genotypes = calldata['genotype']
else:
samples = force_bytes(samples)
h5_samples = callset[chrom]['samples'][:].tolist()
genotypes = np.take(
calldata['genotype'],
[h5_samples.index(s) for s in samples],
axis=1)
ref = variants['REF']
alt = variants['ALT']
if alt.ndim > 1:
alt = alt[:, 0]
pos = variants['POS']
anhima.io.save_tped(path,
genotypes=genotypes,
ref=ref,
alt=alt,
pos=pos,
chromosome=chrom,
identifier=None,
genetic_distance=None)