def reduce_data(beta_path, df, is_nice):
if is_nice:
df = df[['startCpG', 'endCpG']].astype(int)
start = df['startCpG'].values[0]
end = df['endCpG'].values[df.shape[0] - 1]
return fast_method(load_beta_data(beta_path, (start, end)), df)
else:
return slow_method(load_beta_data(beta_path), df)
def merge_betas(betas, opath):
"""
Merge all betas by summing their values element-wise, while keeping the dimensions
:param betas: list of beta files
:param opath: merged beta file
"""
data = load_beta_data(betas[0]).astype(np.int)
for b in betas[1:]:
data += load_beta_data(b)
# Trim / normalize to range [0, 256)
data = trim_to_uint8(data)
# Dump
data.tofile(opath)
return data
def apply_filter_wrapper(args, blocks_bins, finds, beta_path, df):
try:
# load beta file:
data = load_beta_data(beta_path)
# reduce to blocks:
blocks_bins[-1] -= 1
reduced_data = np.add.reduceat(data, blocks_bins)[finds][:-1]
# dump to file
out_name = splitext(splitext(basename(args.blocks_file))[0])[0]
out_name = splitext(basename(beta_path))[0] + '_' + out_name + '.bin'
out_name = out_name.replace('_genome', '')
out_name = op.join(args.out_dir, out_name)
trim_to_uint8(reduced_data).tofile(out_name)
print(out_name)
if args.bedGraph:
with np.errstate(divide='ignore', invalid='ignore'):
beta_vals = reduced_data[:, 0] / reduced_data[:, 1]
eprint(beta_vals.shape, df.shape)
# beta_vals[reduced_data[:, 1] == 0] = np.nan
df['beta'] = beta_vals
df.to_csv(out_name.replace('.bin', '.bedGraph'), sep='\t',
index=None, header=None, na_rep=-1,
float_format='%.2f')
except Exception as e:
print('Failed with beta', beta_path)
print('Exception:', e)
def compare_all_paires(args):
betas = args.betas
sites = GenomicRegion(args).sites
tables = [load_beta_data(b, sites) for b in betas]
names = [op.splitext(op.basename(b))[0] for b in betas]
# break names to lines
nnames = []
k = 20
for n in names:
lst = [n[0 + i:k + i] for i in range(0, len(n), k)]
nn = '\n'.join(lst)
nnames.append(nn)
N = len(tables)
fig, axs = plt.subplots(N, N)
for i in range(N):
for j in range(i + 1):
comp2(tables[i], tables[j], args.min_cov, axs[i, j])
axs[i, 0].set_ylabel(nnames[i], fontsize=8)
for j in range(N):
axs[0, j].set_title(nnames[j], fontsize=8)
for ax in axs.flat:
ax.label_outer()
fig.tight_layout()
if args.outpath is not None:
plt.savefig(args.outpath)
eprint(f'[wt cmp] dumped figure to {args.outpath}')
if args.show or args.outpath is None:
plt.show()
def compare_all_paires(betas, min_cov, sites):
tables = [load_beta_data(b, sites) for b in betas]
names = [op.splitext(op.basename(b))[0] for b in betas]
for x, y in combinations(range(len(tables)), r=2):
plt.figure()
comp2(tables[x], tables[y], (names[x], names[y]), min_cov)
plt.show()
def beta_cov(beta_path, sites=None, bed_wrapper=None, print_res=False):
if bed_wrapper:
res = beta_cov_by_bed(beta_path, bed_wrapper)
else:
res = np.mean(load_beta_data(beta_path, sites)[:, 1])
if print_res:
print('{}\t{:.2f}'.format(pretty_name(beta_path), res))
return res
def beta_cov_by_bed(beta_path, bed_wrapper):
nr_sites = 0
total_cov = 0
for gr in bed_wrapper.iter_grs():
table = load_beta_data(beta_path, gr.sites)[:, 1]
nr_sites += table.size
total_cov += table.sum()
return total_cov / nr_sites if nr_sites else 0
def view_beta(beta_path, gr, opath):
"""
View beta file in given region/sites range
:param beta_path: beta file path
:param gr: a GenomicRegion object
:param opath: output path (or stdout)
"""
data = load_beta_data(beta_path, gr.sites)
np.savetxt(opath, data, fmt='%s', delimiter='\t')
def mult_pat2beta(pat_path, out_beta, nr_sites, args):
processes = []
with Pool(args.threads) as p:
chroms = list(
GenomeRefPaths(args.genome).get_chrom_cpg_size_table()['chr'])
for chrom in sorted(chroms):
beta = '{}.{}.beta'.format(op.splitext(out_beta)[0], chrom)
params = (chrom, pat_path, beta, nr_sites)
processes.append(p.apply_async(chr_thread, params))
p.close()
p.join()
res = np.zeros((nr_sites, 2), dtype=np.uint8)
for bpath in [pr.get() for pr in processes]:
res += load_beta_data(bpath)
os.remove(bpath)
res.tofile(out_beta)
return out_beta
def single_beta(beta_path, indices, cov_thresh):
return op.splitext(op.basename(beta_path))[0], \
beta2vec(load_beta_data(beta_path)[indices - 1], min_cov=cov_thresh).astype(np.float16)
def load_data(self):
# raw table from *beta files:
dsets = np.zeros((len(self.files), self.nr_sites, 2))
for i, file in enumerate(self.files):
dsets[i] = load_beta_data(file, (self.start, self.end))
return dsets
def load_data(self):
# raw table from *beta files:
dsets = np.zeros((len(self.files), self.nr_sites, 2))
for i, fpath in enumerate(self.files):
dsets[i] = load_beta_data(fpath, self.gr.sites)
return dsets
def load_beta(self, beta_path):
""" Load beta to a numpy array """
sites = (1, DEBUG_NR + 1) if self.debug else self.gr.sites
barr = load_beta_data(beta_path, sites=sites)
assert (barr.shape[0] == self.ref_dict.shape[0])
return barr