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get_focal_contacts.py
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get_focal_contacts.py
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import scipy
import scipy.signal
import numpy as np
import pandas as pd
from hic_zscore_functions import make_obs_exp_nolog
from copy import deepcopy
arr = np.asarray
def make_auxiliary_scores(chrom1, chrom2, mat, oe_mat_ind, A_mat_ind, B_mat_ind, chrom_to_start_50kb):
n1, n2 = mat.shape
inner_scores = []
outer_scores = []
vert_scores = []
side_scores = []
c = 0
inner_filter, outer_filter, left_filter, right_filter, up_filter, down_filter = set_filters()
s = inner_filter.shape[0]//2
side_filter = left_filter + right_filter
vert_filter = up_filter + down_filter
offset = s
filters = [outer_filter, outer_filter, outer_filter]
matrices = [A_mat_ind*mat, B_mat_ind*mat, oe_mat_ind*mat]
labels = ['outer_A_scores', 'outer_B_scores', 'outer_oe_scores']
score_df = process_matrix_with_filters(chrom1, chrom2, matrices, filters, labels, chrom_to_start_50kb)
return score_df
def set_filters():
n = 31
inner_filter = np.zeros((n, n))
outer_filter = np.ones((n, n))
w = 3
mid = inner_filter.shape[1]//2
inner_filter[mid-w:mid+w+1, mid-w:mid+w+1] = 1
outer_filter -= inner_filter
left_filter = np.zeros((n, n))
left_filter[mid-w:mid+w+1, :n//2] = 1
left_filter[inner_filter==1] = 0
right_filter = np.zeros((n, n))
right_filter[mid-w:mid+w+1, n//2:] = 1
right_filter[inner_filter==1] = 0
up_filter = left_filter.T
down_filter = right_filter.T
return inner_filter, outer_filter, left_filter, right_filter, up_filter, down_filter
def make_ind_dict(chrom, merged_cool, compartment_vec, chrom_to_start, chrom_to_end):
inddict = {}
m = merged_cool.matrix(balance=True).fetch(chrom).astype(float)
oe_mat = np.log2(make_obs_exp_nolog(m, pc=0)[0])
inddict["oe"] = oe_mat > 0
s, e = chrom_to_start[chrom], chrom_to_end[chrom]
comp = deepcopy(compartment_vec)[s:e]
assert len(comp) == len(m)
A_comp_ind = np.add.outer(comp, comp) > 1.2
B_comp_ind = np.add.outer(comp, comp) < -1.2
inddict["A"] = A_comp_ind
inddict["B"] = B_comp_ind
return inddict
def make_count_df(chrom1, chrom2, inddict, chrom_to_start):
countdict = {}
inner_filter, outer_filter, left_filter, right_filter, up_filter, down_filter = set_filters()
oe_mat_ind, A_mat_ind, B_mat_ind = inddict['oe'], inddict['A'], inddict['B']
filters = [outer_filter, outer_filter, outer_filter]
matrices = [A_mat_ind, B_mat_ind, oe_mat_ind]
labels = ['outer_A_counts', 'outer_B_counts', 'outer_oe_counts']
count_df = process_matrix_with_filters(chrom1, chrom2, matrices, filters, labels, chrom_to_start)
return count_df
def prepare_raw_matrix(cool, chrom):
m_tmp = np.triu(cool.matrix(balance=False).fetch(chrom).astype(float))
np.fill_diagonal(m_tmp, np.nan)
np.fill_diagonal(m_tmp[1:, :], np.nan)
np.fill_diagonal(m_tmp[:, 1:], np.nan)
return m_tmp
def prepare_raw_inter_matrix(cool, chrom1, chrom2):
m_tmp = cool.matrix(balance=False).fetch(chrom1, chrom2).astype(float)
return m_tmp
def get_aux_df(chrom1, chrom2, cond, m_tmp, cool, inddict, countdict, chrom_to_start,):
oe_mat_ind, A_comp_ind, B_comp_ind = inddict["oe"], inddict["A"], inddict["B"]
aux_score_df = make_auxiliary_scores(chrom1, chrom2, m_tmp, oe_mat_ind, A_comp_ind, B_comp_ind, chrom_to_start)
return aux_score_df
def get_oe_mat(mat, pc=0):
oe_mat = np.log2(make_obs_exp_nolog(mat, pc=0)[0])
return oe_mat
def get_inter_exp_mat(mat, pc=0):
e = np.nanmean(mat)
return e
def get_inter_oe_mat(mat, pc=0):
e = np.nanmean(mat)
oe_mat = np.log2((mat+pc)/(e+pc))
return oe_mat
def get_places_with_high_oe(oe_mat, chrom1, chrom2, chrom_to_start, co=0):
indsoi = arr(np.where(oe_mat > co))
X, Y = indsoi
X = X + chrom_to_start[chrom1]
Y = Y + chrom_to_start[chrom2]
v = np.ravel(X).astype(str)
w = np.ravel(Y).astype(str)
places_with_high_oe = pd.Series(v) + "_" + pd.Series(w)
return places_with_high_oe
def normalize_aux_df(aux_df, count_df, cond, tmp_df):
tmp_df["A_scores_" + cond] = ((aux_df['outer_A_scores'] / count_df['outer_A_counts'])*10).astype(float).round()
tmp_df["B_scores_" + cond] = ((aux_df['outer_B_scores'] / count_df['outer_B_counts'])*10).astype(float).round()
tmp_df["oe_scores_" + cond] = ((aux_df['outer_oe_scores'] / count_df['outer_oe_counts'])*10).astype(float).round()
tmp_df["A_scores_" + cond] = tmp_df["A_scores_" + cond].fillna(0)
tmp_df["B_scores_" + cond] = tmp_df["B_scores_" + cond].fillna(0)
tmp_df["oe_scores_" + cond] = tmp_df["oe_scores_" + cond].fillna(0)
return tmp_df
def make_readcount_df(params, n_filters = 4):
(merged_cool, chrom_to_start,
chrom_to_end, cool_dict, chrom, compartment_vec) = params
balanced_mat = merged_cool.matrix(balance=True).fetch(chrom).astype(float)
nanmarker = np.isnan(balanced_mat)
oe_mat = np.triu(get_oe_mat(balanced_mat), k = 32)
places_with_high_oe = get_places_with_high_oe(oe_mat, chrom, chrom, chrom_to_start, co=1)
nan_df = make_scores(chrom, chrom, np.triu(~nanmarker), chrom_to_start)
t = nan_df.index.isin(places_with_high_oe)
nan_df = nan_df.loc[t]
inddict = make_ind_dict(chrom, merged_cool, compartment_vec,
chrom_to_start, chrom_to_end)
count_df = make_count_df(chrom, chrom, inddict, chrom_to_start)
assert len(nan_df.columns) == 4 ## One column is for 'places'
frac_nan_df = (nan_df)/(nan_df.max(axis=0))
tmp_df = pd.DataFrame()
for cond, cool in cool_dict.items():
m_tmp = prepare_raw_matrix(cool, chrom)
score_df = make_scores(chrom, chrom, m_tmp, chrom_to_start)
score_df = score_df.loc[t]
score_df = (score_df/frac_nan_df).astype(float).round()
tmp_df["inner_" + cond] = score_df['inner_scores']
tmp_df["outer_" + cond] = score_df['outer_scores']
tmp_df["vert_" + cond] = score_df['vert_scores']
tmp_df["side_" + cond] = score_df['side_scores']
#break
for cond, cool in cool_dict.items():
m_tmp = prepare_raw_matrix(cool, chrom)
aux_df = get_aux_df(chrom, chrom, cond, m_tmp, cool, inddict, count_df, chrom_to_start)
aux_df = aux_df.loc[t]
sub_count_df = count_df.loc[t]
normalize_aux_df(aux_df, sub_count_df, cond, tmp_df)
#break
tmp_df.index = aux_df.index
return tmp_df
import scipy
n_workers = 16
def make_scores(chrom1, chrom2, mat, chrom_to_start_50kb):
n1, n2 = mat.shape
c = 0
inner_filter, outer_filter, left_filter, right_filter, up_filter, down_filter = set_filters()
s = inner_filter.shape[0]//2
side_filter = left_filter + right_filter
vert_filter = up_filter + down_filter
offset = s
filters = [inner_filter, outer_filter, vert_filter, side_filter]
labels = ['inner_scores', 'outer_scores', 'vert_scores', 'side_scores']
matrices = (mat, mat, mat, mat)
score_df = process_matrix_with_filters(chrom1, chrom2, matrices, filters, labels, chrom_to_start_50kb)
return score_df
import scipy
import scipy.signal
def process_matrix_with_filters(chrom1, chrom2, matrices, filters, labels, chrom_to_start_50kb):
score_df = pd.DataFrame()
for filt, label, mat in zip(filters, labels, matrices):
n1, n2 = mat.shape
c = 0
s = filt.shape[0]//2
offset = s
scores = scipy.signal.correlate2d(mat, filt)[offset:-offset, offset:-offset]
score_df[label] = np.ravel(scores)
X, Y = np.indices(mat.shape)
X = X + chrom_to_start_50kb[chrom1]
Y = Y + chrom_to_start_50kb[chrom2]
v = np.ravel(X).astype(str)
w = np.ravel(Y).astype(str)
places = pd.Series(v) + "_" + pd.Series(w)
score_df.index = np.ravel(places)
return score_df
def make_interchrom_ind_dict(chrom1, chrom2, merged_cool, compartment_vec, chrom_to_start, chrom_to_end):
inddict = {}
m = merged_cool.matrix(balance=True).fetch(chrom1, chrom2).astype(float)
oe_mat = get_inter_oe_mat(m)
inddict["oe"] = oe_mat > 0
s1, e1 = chrom_to_start[chrom1], chrom_to_end[chrom1]
s2, e2 = chrom_to_start[chrom2], chrom_to_end[chrom2]
comp1 = deepcopy(compartment_vec)[s1:e1]
comp2 = deepcopy(compartment_vec)[s2:e2]
assert len(comp1) == m.shape[0]
assert len(comp2) == m.shape[1]
A_comp_ind = np.add.outer(comp1, comp2) > 1.2
B_comp_ind = np.add.outer(comp1, comp2) < -1.2
inddict["A"] = A_comp_ind
inddict["B"] = B_comp_ind
return inddict
def make_interchrom_readcount_df(params, n_filters = 4):
(merged_cool, chrom_to_start,
chrom_to_end, cool_dict, chrom1, chrom2, compartment_vec) = params
balanced_mat = merged_cool.matrix(balance=True).fetch(chrom1, chrom2).astype(float)
nanmarker = np.isnan(balanced_mat)
oe_mat = get_inter_oe_mat(balanced_mat)
places_with_high_oe = get_places_with_high_oe(oe_mat, chrom1, chrom2, chrom_to_start, co=0)
nan_df = make_scores(chrom1, chrom2, ~nanmarker, chrom_to_start)
t = nan_df.index.isin(places_with_high_oe)
nan_df = nan_df.loc[t]
inddict = make_interchrom_ind_dict(chrom1, chrom2, merged_cool, compartment_vec,
chrom_to_start, chrom_to_end)
count_df = make_count_df(chrom1, chrom2, inddict, chrom_to_start)
assert len(nan_df.columns) == 4 ## One column is for 'places'
frac_nan_df = (nan_df)/(nan_df.max(axis=0))
tmp_df = pd.DataFrame()
for cond, cool in cool_dict.items():
m_tmp = prepare_raw_inter_matrix(cool, chrom1, chrom2)
score_df = make_scores(chrom1, chrom2, m_tmp, chrom_to_start)
score_df = score_df.loc[t]
score_df = (score_df/frac_nan_df).astype(float).round()
tmp_df["inner_" + cond] = score_df['inner_scores']
tmp_df["outer_" + cond] = score_df['outer_scores']
tmp_df["vert_" + cond] = score_df['vert_scores']
tmp_df["side_" + cond] = score_df['side_scores']
#break
for cond, cool in cool_dict.items():
m_tmp = prepare_raw_inter_matrix(cool, chrom1, chrom2)
aux_df = get_aux_df(chrom1, chrom2, cond, m_tmp, cool, inddict, count_df, chrom_to_start)
aux_df = aux_df.loc[t]
sub_count_df = count_df.loc[t]
normalize_aux_df(aux_df, sub_count_df, cond, tmp_df)
#break
tmp_df.index = aux_df.index
return tmp_df, chrom1, chrom2, t