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scrna_functions.py
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scrna_functions.py
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import numpy as np
arr = np.asarray
def get_distances(anclist):
dists = arr(anclist)[:, 1].astype(int)
return dists
def calculate_loop_corr_5kb(cormat, tr_adata, gene2anc, megaloop_subloops):
megaloop_subloops = set(megaloop_subloops)
arr = np.asarray
has_megaloop = []
no_megaloop = []
has_megaloop_names = []
no_megaloop_names = []
for c1, gene1 in enumerate(tr_adata.var.index):
for c2, gene2 in enumerate(tr_adata.var.index):
if gene1 <= gene2:
continue
r = cormat[c1, c2]
anc1, anc2 = gene2anc.get(gene1, None), gene2anc.get(gene2, None)
if (anc1 is None) or (anc2 is None):
continue
dist1, dist2 = get_distances(anc1), get_distances(anc2)
distances = np.subtract.outer(arr(dist1), arr(dist2))/1e6
loopcount = 0
for a1 in anc1:
for a2 in anc2:
fullloop = tuple(list(a1) + list(a2))
transpose_fullloop = tuple(list(a2) + list(a1))
# print(fullloop, list(megaloop_subloops)[0])
# raise Exception
if (fullloop in megaloop_subloops) or (transpose_fullloop in megaloop_subloops):
loopcount += 1
if loopcount > 0:
has_megaloop.append(r)
has_megaloop_names.append([gene1, gene2])
elif loopcount == 0:
no_megaloop.append(r)
no_megaloop_names.append([gene1, gene2])
return has_megaloop, no_megaloop, has_megaloop_names, no_megaloop_names
import time
# def calculate_megaloops_5kb(cormat, tr_adata, gene2adata_ind, anc2gene, gene2dist, megaloop_subloops, ):
# megaloop_subloops = set(megaloop_subloops)
# genes_have_loop = np.zeros_like(cormat)
# for loop in megaloop_subloops:
# loop1, loop2 = loop[:3], loop[3:6]
# genes1, genes2 = anc2gene.get(loop1, []), anc2gene.get(loop2, [])
# for g1 in genes1:
# for g2 in genes2:
# i1 = gene2adata_ind.get(g1, None)
# i2 = gene2adata_ind.get(g2, None)
# if (i1 is None) or (i2 is None):
# continue
# i1, i2 = sorted([i1, i2])
# genes_have_loop[i1, i2] = 1
# genes_have_loop[i2, i1] = 1
# good = np.where(np.triu(genes_have_loop, k=1))
# bad = np.where(np.triu(1-genes_have_loop, k=1))
# return genes_have_loop, cormat[good], cormat[bad],
import scipy
import scipy.stats
def compute_megaloop_enrichment_in_high_correlation(genematdict, adatadict, loop_bedfile, gene2dist):
print("N anchors:", len(loop_bedfile))
for c, cond in enumerate(genematdict):
genemat = genematdict[cond].copy()
adata = adatadict[cond].copy()
names = adata.var.index
cormat = np.corrcoef(genemat)
gene2adata_ind = get_gene2adata_ind(adata)
gene2anc, anc2gene, all_ancs = get_dict_from_loops(loop_bedfile)
genes_have_loop, has_loop, no_loop = calculate_megaloops_5kb_fast(cormat, adata, gene2adata_ind, anc2gene, gene2dist, loop_bedfile)
v1, v2 = (genes_have_loop[(np.triu(cormat>.1, k=1))],
genes_have_loop[(np.triu((np.abs(cormat)<0.1), k=1))])
print(cond, scipy.stats.fisher_exact([[v1.sum(), (1-v1).sum()],
[v2.sum(), (1-v2).sum()]]))
import matplotlib.pyplot as plt
import seaborn as sns
from tad_functions import make_df_from_dict
def compute_corr_enrichment_in_megaloops(genematdict, adatadict, loop_bedfile, gene2dist, adata_type='HVG'):
resdict = {}
fig, axs = plt.subplots(1, 3, figsize=(8, 2))
for c, cond in enumerate(genematdict):
ax = axs[c]
genemat = genematdict[cond].copy()
adata = adatadict[cond].copy()
v = np.ravel((adata.layers['poo']==0).mean(axis=0))
good = v > -1
adata = adata[:, good]
genemat = genemat[good, :]
names = adata.var.index
cormat = np.corrcoef(genemat)
gene2adata_ind = get_gene2adata_ind(adata)
gene2anc, anc2gene, all_ancs = get_dict_from_loops(loop_bedfile)
_, has_loop, no_loop = calculate_megaloops_5kb_fast(cormat, adata, gene2adata_ind, anc2gene, gene2dist, loop_bedfile)
print(cond, scipy.stats.ranksums(has_loop[~np.isnan(has_loop)], no_loop[::1000][~np.isnan(no_loop[::1000])]))
d = {'Has Loop' : list(has_loop),
'No Loop' : list(no_loop[::100]),
}
df = make_df_from_dict(d)
sns.boxplot(data=df, x='labels', y='values', ax=ax, fliersize=0)
ax.set_title(f"Pearson r for {adata_type} \n with loops ({cond})")
ax.set_xlabel("Label")
ax.set_ylabel("Pearson R")
ax.set_ylim([-.1, .1])
resdict[cond] = d
return fig, axs, resdict
def compute_corr_enrichment_in_megaloops(genematdict, adatadict, loop_bedfile, gene2dist, adata_type='HVG'):
resdict = {}
fig, axs = plt.subplots(1, 3, figsize=(8, 2))
for c, cond in enumerate(genematdict):
ax = axs[c]
genemat = genematdict[cond].copy()
adata = adatadict[cond].copy()
#v = np.ravel((adata.layers['poo']==0).mean(axis=0))
#good = v > -1
#adata = adata[:, good]
#genemat = genemat[good, :]
names = adata.var.index
cormat = np.corrcoef(genemat)
gene2adata_ind = get_gene2adata_ind(adata)
gene2anc, anc2gene, all_ancs = get_dict_from_loops(loop_bedfile)
_, has_loop, no_loop = calculate_megaloops_5kb_fast(cormat, adata, gene2adata_ind, anc2gene, gene2dist, loop_bedfile)
print(cond, scipy.stats.ranksums(has_loop[~np.isnan(has_loop)], no_loop[::1000][~np.isnan(no_loop[::1000])]))
d = {'Has Loop' : list(has_loop),
'No Loop' : list(no_loop[::100]),
}
df = make_df_from_dict(d)
sns.boxplot(data=df, x='labels', y='values', ax=ax, fliersize=0)
ax.set_title(f"Pearson r for {adata_type} \n with loops ({cond})")
ax.set_xlabel("Label")
ax.set_ylabel("Pearson R")
ax.set_ylim([-.1, .1])
resdict[cond] = d
return fig, axs, resdict
def compute_corr_enrichment_in_anchors(genematdict, adatadict, loop_bedfile, gene2dist, adata_type='HVG', L = 0, R = np.inf):
resdict = {}
fig, axs = plt.subplots(1, 3, figsize=(8, 2))
for c, cond in enumerate(genematdict):
ax = axs[c]
genemat = genematdict[cond].copy()
adata = adatadict[cond].copy()
names = adata.var.index
cormat = np.corrcoef(genemat)
gene2adata_ind = get_gene2adata_ind(adata)
gene2anc, anc2gene, gene2chrom, all_loops = get_dict_from_loops(loop_bedfile)
all_ancs = get_unique(loops_to_anchors(all_loops))
_, has_loop, no_loop = calculate_megaloops_from_anchors(cormat, adata, gene2adata_ind, anc2gene,
gene2dist, gene2chrom, all_ancs, L = L, R = R)
d = {'Has Loop' : list(has_loop),
'No Loop' : list(no_loop[::100]),
}
df = make_df_from_dict(d)
sns.boxplot(data=df, x='labels', y='values', ax=ax, fliersize=0)
ax.set_title(f"Pearson r for {adata_type} \n with loops ({cond})")
ax.set_xlabel("Label")
ax.set_ylabel("Pearson R")
ax.set_ylim([-.1, .1])
resdict[cond] = d
return fig, axs, resdict
import sklearn
import sklearn.preprocessing
from sklearn.preprocessing import LabelEncoder
def calculate_megaloops_from_anchors(cormat, tr_adata, gene2adata_ind, anc2gene, gene2dist, gene2chrom, anchors,
L = 0, R = np.inf):
n = cormat.shape[0]
genes_have_anchor = np.zeros(n)
for anc in anchors:
genes1 = anc2gene.get(anc, [])
for g1 in genes1:
i1 = gene2adata_ind.get(g1, None)
if (i1 is None):
continue
genes_have_anchor[i1] = 1
gene_locations = np.zeros(n)
gene_chroms = np.zeros(n).astype(str)
for gene, adata_ind in gene2adata_ind.items():
location = gene2dist.get(gene, None)
chrom = gene2chrom.get(gene, None)
gene_locations[adata_ind] = location
gene_chroms[adata_ind] = chrom
le = LabelEncoder()
chromvals = arr(le.fit_transform(gene_chroms))
pairwise_chroms = np.equal.outer(chromvals, chromvals)
pairwise_dists = np.abs(np.subtract.outer(gene_locations, gene_locations))
genes_both_with_anchor = np.outer(genes_have_anchor, genes_have_anchor) > 0
inds_to_compare = (pairwise_dists > L) & (pairwise_dists < R) & (pairwise_chroms) & (genes_both_with_anchor)
inds_to_compare = np.triu(inds_to_compare + inds_to_compare.T, k=1)
return genes_have_anchor, cormat[inds_to_compare], cormat[~inds_to_compare]
def calculate_megaloops_5kb_fast(cormat, tr_adata, gene2adata_ind, anc2gene, gene2dist, megaloop_subloops, ):
megaloop_subloops = set(megaloop_subloops)
genes_have_loop = np.zeros_like(cormat)
for loop in megaloop_subloops:
loop1, loop2 = loop[:3], loop[3:6]
genes1, genes2 = anc2gene.get(loop1, []), anc2gene.get(loop2, [])
for g1 in genes1:
for g2 in genes2:
i1 = gene2adata_ind.get(g1, None)
i2 = gene2adata_ind.get(g2, None)
if (i1 is None) or (i2 is None):
continue
i1, i2 = sorted([i1, i2])
genes_have_loop[i1, i2] = 1
good = np.where(np.triu(genes_have_loop, k=1))
bad = np.where(np.triu(1-genes_have_loop, k=1))
return genes_have_loop, cormat[good], cormat[bad]
import aux_functions
from aux_functions import *
def get_dict_from_loops(loops):
all_loops = set(loops)
anchors = [make_str(l) for l in loops_to_anchors(all_loops)]
gene2anc = {}
anc2gene = {}
peak_genes = pbt.BedTool('../peaks/RNA_coverage.narrowPeak')
for i in peak_genes.intersect(anchors, wo=True):
name = i[3]
anc = i[-4:-1]
anc = tuple(anc)
anc2gene.setdefault(anc, [])
gene2anc.setdefault(name, [])
gene2anc[name].append(make_str(anc))
anc2gene[anc].append(name)
gene2chrom = {}
peak_genes = pbt.BedTool('../peaks/RNA_coverage.narrowPeak')
for x in peak_genes:
name = x[3]
gene2chrom[name] = x[0]
return gene2anc, anc2gene, gene2chrom, all_loops
def get_gene2adata_ind(adata):
names = adata.var.index
n = len(names)
return dict(zip(names, np.arange(n)))
def make_gene_mat_dict(adata_batch2):
genematdict = {}
adatadict = {}
cells = ((adata_batch2.obs['ClusterCCA_Revised_Annotation_General'] == 'Treg') &
(adata_batch2.obs['sample_type'] == 'WT.TR'))
treg_adata = adata_batch2[cells, :].copy()
genemat = treg_adata.X.T
subgenemat = genemat.copy()
genematdict['Treg'] = subgenemat
adatadict['Treg'] = treg_adata
cells = ((adata_batch2.obs['ClusterCCA_Revised_Annotation_General'] == 'Tconv_resting') &
(adata_batch2.obs['sample_type'] == 'WT.TC'))
tcon_adata = adata_batch2[cells, :].copy()
genemat = tcon_adata.X.T
subgenemat = genemat.copy()
genematdict['Tconv_resting'] = subgenemat
adatadict['Tconv_resting'] = tcon_adata
cells = ((adata_batch2.obs['ClusterCCA_Revised_Annotation_General'] == 'Tconv_activated') &
(adata_batch2.obs['sample_type'] == 'WT.TC'))
tcon_adata = adata_batch2[cells, :].copy()
genemat = tcon_adata.X.T
subgenemat = genemat.copy()
genematdict['Tconv_activated'] = subgenemat
adatadict['Tconv_activated'] = tcon_adata
return genematdict, adatadict