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605f784 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # Adapted from https://github.com/GuanLab/Leopard/blob/master/data/quantile_normalize_bigwig.py
import argparse, time
import numpy as np
import pyBigWig
# Human chromosomes in hg19, and their sizes in bp
chrom_sizes = {'chr1': 249250621, 'chr10': 135534747, 'chr11': 135006516, 'chr12': 133851895, 'chr13': 115169878, 'chr14': 107349540, 'chr15': 102531392, 'chr16': 90354753, 'chr17': 81195210, 'chr18': 78077248, 'chr19': 59128983, 'chr2': 243199373, 'chr20': 63025520, 'chr21': 48129895, 'chr22': 51304566, 'chr3': 198022430, 'chr4': 191154276, 'chr5': 180915260, 'chr6': 171115067, 'chr7': 159138663, 'chr8': 146364022, 'chr9': 141213431, 'chrX': 155270560}
def qn_sample_to_array(
input_celltypes,
input_chroms=None,
subsampling_ratio=1000,
data_pfx = '/users/abalsubr/wilds/examples/data/encode_v1.0/'
):
"""
Compute and write distribution of DNase bigwigs corresponding to input celltypes.
"""
if input_chroms is None:
input_chroms = chrom_sizes.keys()
qn_chrom_sizes = { k: chrom_sizes[k] for k in input_chroms }
# Initialize chromosome-specific seeds for subsampling
chr_to_seed = {}
i = 0
for the_chr in qn_chrom_sizes:
chr_to_seed[the_chr] = i
i += 1
# subsampling
sample_len = np.ceil(np.array(list(qn_chrom_sizes.values()))/subsampling_ratio).astype(int)
sample = np.zeros(sum(sample_len))
start = 0
j = 0
for the_chr in qn_chrom_sizes:
np.random.seed(chr_to_seed[the_chr])
for ct in input_celltypes:
path = data_pfx + 'DNASE.{}.fc.signal.bigwig'.format(ct)
bw = pyBigWig.open(path)
signal = np.nan_to_num(np.array(bw.values(the_chr, 0, qn_chrom_sizes[the_chr])))
index = np.random.randint(0, len(signal), sample_len[j])
sample[start:(start+sample_len[j])] += (1.0/len(input_celltypes))*signal[index]
start += sample_len[j]
j += 1
print(the_chr, ct)
sample.sort()
np.save(data_pfx + "qn.{}.npy".format('.'.join(input_celltypes)), sample)
if __name__ == '__main__':
train_chroms = ['chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr10', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr22', 'chrX']
all_celltypes = ['H1-hESC', 'HCT116', 'HeLa-S3', 'K562', 'A549', 'GM12878', 'MCF-7', 'HepG2', 'liver']
for ct in all_celltypes:
qn_sample_to_array([ct], input_chroms=train_chroms)
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