import numpy as np
from scanorama import *
from scipy.sparse import vstack
from scipy.stats import mannwhitneyu
from benchmark import write_table
from process import load_names, merge_datasets, process
from time_align import time_align_correlate, time_align_visualize, time_dist
NAMESPACE = 'mono_macro'
data_names = [
'data/macrophage/monocytes',
'data/pbmc/10x/cd14_monocytes',
'data/macrophage/mcsf_day3_1',
'data/macrophage/mcsf_day3_2',
'data/macrophage/mcsf_day6_1',
'data/macrophage/mcsf_day6_2',
]
def diff_expr(A, B, genes, permute_cutoff, verbose=True):
p_vals = []
for idx, gene in enumerate(genes):
if sum(A[:, idx]) == 0 and sum(B[:, idx]) == 0:
p_vals.append(1.)
continue
u, p = mannwhitneyu(A[:, idx], B[:, idx])
p_vals.append(p)
de_genes = []
for idx, gene in enumerate(genes):
if p_vals[idx] < permute_cutoff:
if verbose:
print('{}\t{}'.format(gene, p_vals[idx]))
de_genes.append(gene)
return de_genes
if __name__ == '__main__':
from process import process
process(data_names)
datasets, genes_list, n_cells = load_names(data_names, norm=False)
datasets, genes = merge_datasets(datasets, genes_list)
datasets_dimred, genes = process_data(datasets, genes)
_, A, _ = find_alignments_table(datasets_dimred)
time = np.array([ 0, 0, 3, 3, 6, 6 ]).reshape(-1, 1)
time_align_correlate(A, time)
time_dist(datasets_dimred, time)
x = np.array([ 0, 0, 1, 1, 2, 2 ]).reshape(-1, 1)
y = [ -.1, .1, -.1, .1, -.1, .1 ]
time_align_visualize(A, x, y, namespace=NAMESPACE)
X = vstack(datasets).toarray()
write_table(X, genes, 'data/macrophage/' + NAMESPACE, cell_name=NAMESPACE)
de_genes = diff_expr(
vstack(datasets[:2]).toarray(),
vstack(datasets[4:]).toarray(),
genes, 8.534789741091e-169, verbose=False
)
with open('data/macrophage/mono_macro_diffexpr_uncorrected.txt', 'w') as of:
of.write('\n'.join(de_genes) + '\n')
labels = []
days = []
curr_label = 0
curr_day = 0
for i, a in enumerate(datasets):
labels += [ data_names[i] ] * int(a.shape[0])
curr_label += 1
if data_names[i] == 'data/macrophage/mcsf_day3_1':
curr_day += 3
if data_names[i] == 'data/macrophage/mcsf_day6_1':
curr_day += 3
days += list(np.zeros(a.shape[0]) + curr_day)
with open('data/macrophage/mono_macro_meta.txt', 'w') as of:
of.write('Days\tBatch\n')
for idx, day in enumerate(days):
of.write('mono_macro{}\t{}\t{}'
.format(idx, int(day), labels[idx].split('/')[-1]) + '\n')
with open('data/macrophage/' + NAMESPACE + '_genes.txt', 'w') as f:
f.write('gene_short_name\n')
for gene in genes:
f.write('{0}\t{0}\n'.format(gene))
assemble(datasets_dimred, expr_datasets=datasets)
X = vstack(datasets).toarray()
write_table(X, genes, 'data/macrophage/' + NAMESPACE + '_corrected',
cell_name=NAMESPACE)
de_genes = diff_expr(
vstack(datasets[:2]).toarray(),
vstack(datasets[4:]).toarray(),
genes, 8.534789741091e-169, verbose=False
)
with open('data/macrophage/mono_macro_diffexpr_scanorama.txt', 'w') as of:
of.write('\n'.join(de_genes) + '\n')
