def from_bed(
cls, bed, location, chrom_size_path, region_dim="region", sort_bed=True
):
"""
Create empty RegionDS from a bed file.
Parameters
----------
bed
location
region_dim
chrom_size_path
sort_bed
Returns
-------
"""
# sort bed based on chrom_size_path
if isinstance(bed, (str, pathlib.PosixPath)):
if sort_bed:
bed = BedTool(bed).sort(g=chrom_size_path).to_dataframe()
else:
bed = BedTool(bed)
else:
bed = bed
n_cols = bed.shape[1]
if n_cols == 3:
bed.index = bed.index.map(lambda i: f"{region_dim}_{i}")
elif n_cols == 4:
bed.set_index(bed.columns[3], inplace=True)
else:
raise ValueError(
"bed file need to be either 3 columns (chrom, start, end) "
"or 4 columns (chrom, start, end, name)"
)
bed.index.name = region_dim
bed.columns = ["chrom", "start", "end"]
ds = xr.Dataset({})
region_dim = bed.index.name
for k, v in bed.items():
key = f"{region_dim}_{k}"
ds.coords[key] = v
if ds.coords[key].dtype == "object":
ds.coords[key] = ds.coords[key].astype(str)
location = pathlib.Path(location).absolute()
location.mkdir(exist_ok=True, parents=True)
region_ds = cls(
ds,
region_dim=region_dim,
location=location,
chrom_size_path=chrom_size_path,
)
region_ds.save()
return region_ds
def preprocess(self,
chrlenPath,
genomePath,
w=100,
upStream=1000,
downStream=1000,
overlap=0.5,
method='mean',
col=4,
type='bed',
n_workers=4):
assert upStream % w == 0 and downStream % w == 0
window = BedTool().window_maker(g=chrlenPath, w=w)
genes = BedTool(genomePath).to_dataframe()
genes = genes[genes['feature'] == 'gene'][[
'seqname', 'start', 'strand', 'attributes'
]]
genes['attributes'] = genes['attributes'].apply(
lambda x: x[x.find('=') + 1:x.find(';')])
genes['start'] = genes['start'].apply(lambda x: x - upStream)
genes['end'] = genes['start'] + upStream + downStream
genes = genes[['seqname', 'start', 'end', 'attributes', 'strand']]
genes.columns = ['chrom', 'start', 'end', 'ID', 'strand']
genes = genes[genes.start >= 0]
genes.chrom = genes.chrom.apply(lambda x: x[0].lower() + x[1:])
atlas = BedTool.from_dataframe(genes[['chrom', 'start', 'end',
'ID']]).sort()
genes = genes.set_index(['ID'])
def worker(atlas, window, path, genes, col, method, overlap, type):
p = BedTool(path).sort()
a = None
if type == 'bed':
a = window.map(p, c=1, o='count', F=overlap)
elif type == 'sigbed':
a = window.map(p, o=method, c=col, F=overlap)
tmp = atlas.intersect(a, loj=True, wa=True, wb=True).to_dataframe()
grps = tmp.groupby(['name'])
data = []
for ind in genes.index:
row = grps.get_group(ind)['thickEnd'].tolist()
data.append(row if genes.ix[ind].strand == '+' else row[::-1])
return pd.DataFrame(data, index=genes.index.tolist())
self.raw = {}
with futures.ThreadPoolExecutor(max_workers=n_workers) as executor:
jobs = {}
for i, path in enumerate(self.paths):
job = executor.submit(worker, atlas, window, path, genes, col,
method, overlap, type)
jobs[job] = self.names[i]
for job in futures.as_completed(jobs):
self.raw[jobs[job]] = job.result().dropna()
if self.genes == None:
self.genes = self.raw[jobs[job]].index.tolist()