def infer_structure(contactMat,
structure,
alpha,
num_threads,
classical=False):
"""Infers 3D coordinates for one structure"""
assert len(structure.nonzero_abs_indices()) == len(contactMat)
at.makeSymmetric(contactMat)
rowsums = np.array([sum(row) for row in contactMat])
assert len(np.where(rowsums == 0)[0]) == 0
distMat = at.contactToDist(contactMat, alpha)
at.makeSymmetric(distMat)
if classical: #classical MDS
coords = la.cmds(distMat)
else:
coords = manifold.MDS(n_components=3,
metric=True,
random_state=np.random.RandomState(),
verbose=0,
dissimilarity="precomputed",
n_jobs=num_threads).fit_transform(distMat)
structure.setCoords(coords)
def fullMDS(path, classical=False):
"""MDS without partitioning"""
cluster = dt.clusterFromBed(path, None, None)
contactMat = dt.matFromBed(path, cluster)
distMat = at.contactToDist(contactMat)
infer_cluster(contactMat, cluster, classical)
return cluster
def infer_clusters(contactMat, clusters, offsets, classical=False):
"""Infers 3D coordinates for multiple clusters with same contact matrix"""
assert sum([len(cluster.getPointNums())
for cluster in clusters]) == len(contactMat)
at.makeSymmetric(contactMat)
rowsums = np.array([sum(row) for row in contactMat])
assert len(np.where(rowsums == 0)[0]) == 0
distMat = at.contactToDist(contactMat)
at.makeSymmetric(distMat)
if classical: #classical MDS
coords = st.cmds(distMat)
else:
mds = manifold.MDS(n_components=3,
metric=True,
random_state=np.random.RandomState(seed=3),
verbose=0,
dissimilarity="precomputed",
n_jobs=-1)
coords = mds.fit_transform(distMat)
for offset, cluster in zip(offsets, clusters):
for i in range(len(cluster.getPoints())):
cluster.getPoints()[i].pos = coords[i + offset]
def infer_structure(contactMat,
structure,
alpha,
num_threads,
weight,
classical=False):
"""Infers 3D coordinates for one structure"""
assert len(structure.nonzero_abs_indices()) == len(contactMat)
expected = get_expected(contactMat)
for i in range(len(contactMat)):
for j in range(i):
contactMat[i, j] = (
1 - weight) * contactMat[i, j] + weight * expected[i - j - 1]
at.makeSymmetric(contactMat)
rowsums = np.array([sum(row) for row in contactMat])
assert len(np.where(rowsums == 0)[0]) == 0
distMat = at.contactToDist(contactMat, alpha)
at.makeSymmetric(distMat)
distMat = distMat / np.mean(distMat) #normalize
if classical: #classical MDS
coords = la.cmds(distMat)
else:
coords = manifold.MDS(n_components=3,
metric=True,
random_state=np.random.RandomState(),
verbose=0,
dissimilarity="precomputed",
n_jobs=num_threads).fit_transform(distMat)
structure.setCoords(coords)
def infer_structures(contactMat, structures, offsets, alpha, num_threads, classical=False): """Infers 3D coordinates for multiple structures with same contact matrix""" assert sum([len(structure.getPointNums()) for structure in structures]) == len(contactMat) at.makeSymmetric(contactMat) rowsums = np.array([sum(row) for row in contactMat]) assert len(np.where(rowsums == 0)[0]) == 0 distMat = at.contactToDist(contactMat, alpha) at.makeSymmetric(distMat) if classical: #classical MDS coords = la.cmds(distMat) else: coords = manifold.MDS(n_components=3, metric=True, random_state=np.random.RandomState(), verbose=0, dissimilarity="precomputed", n_jobs=num_threads).fit_transform(distMat) for offset, structure in zip(offsets, structures): structure.setCoords(coords[offset:offset+len(structure.getPoints())])
def distmat(contactMat, structure, alpha, weight, num_threads):
assert len(structure.nonzero_abs_indices()) == len(contactMat)
expected = get_expected(contactMat)
for i in range(len(contactMat)):
for j in range(i):
corrected = (
1 - weight) * contactMat[i, j] + weight * expected[i - j - 1]
contactMat[i, j] = corrected
contactMat[j, i] = corrected
rowsums = np.array([sum(row) for row in contactMat])
assert len(np.where(rowsums == 0)[0]) == 0
distMat = at.contactToDist(contactMat, alpha)
distMat = distMat / np.mean(distMat) #normalize
return distMat
def infer_cluster(contactMat, cluster, classical=False):
"""Infers 3D coordinates for one cluster"""
assert len(cluster.getPointNums()) == len(contactMat)
at.makeSymmetric(contactMat)
rowsums = np.array([sum(row) for row in contactMat])
assert len(np.where(rowsums == 0)[0]) == 0
distMat = at.contactToDist(contactMat)
at.makeSymmetric(distMat)
if classical: #classical MDS
coords = st.cmds(distMat)
else:
mds = manifold.MDS(n_components=3,
metric=True,
random_state=np.random.RandomState(seed=3),
verbose=0,
dissimilarity="precomputed",
n_jobs=-1)
coords = mds.fit(distMat).embedding_
for i in range(len(cluster.getPoints())):
cluster.getPoints()[i].pos = coords[i]
from matplotlib import pyplot as plt
import numpy as np
import sys
sys.path.append("..")
import data_tools as dt
import array_tools as at
import misc
#"true" distance matrix
cluster = dt.clusterFromBed(bedpath, None, None)
contactMat = dt.matFromBed(bedpath, cluster)
distMat = at.contactToDist(contactMat)
at.makeSymmetric(distMat)
for j in range(len(distMat)): #remove diagonal
distMat[j, j] = 0
chromthreed_distMat = misc.distsFromCoords(
"Chromosome3D/output/chr22_100kb/chr22_100kb_coords.tsv")
chromthreed_r = misc.pearson(distMat, chromthreed_distMat)
mmds_distMat = dt.clusterFromFile(
"hic_data/GM12878_combined_22_10kb_mmds_coords.tsv").distMat()
mmds_r = misc.pearson(distMat, mmds_distMat)
cmds_distMat = dt.clusterFromFile(
"hic_data/GM12878_combined_22_10kb_cmds_coords.tsv").distMat()
cmds_r = misc.pearson(distMat, cmds_distMat)
minimds_distMat = dt.clusterFromFile(
"hic_data/GM12878_combined_22_10kb_minimds_coords.tsv").distMat()
minimds_r = misc.pearson(distMat, minimds_distMat)
def normalized_dist_mat(path, structure):
"""Standard processing for creating distance matrix"""
contacts = matFromBed(path, structure)
dists = at.contactToDist(contacts, 4)
at.makeSymmetric(dists)
return dists / np.mean(dists) #normalize
chroms = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, "X")
n = len(chroms)
mmds_rs = np.zeros(n)
cmds_rs = np.zeros(n)
minimds_rs = np.zeros(n)
mogen_rs = np.zeros(n)
for i, chrom in enumerate(chroms):
bedpath = "hic_data/GM12878_combined_{}_10kb.bed".format(chrom)
mmds_structure = dt.structure_from_file(
"hic_data/GM12878_combined_{}_10kb_mmds_coords.tsv".format(chrom))
contactMat = dt.matFromBed(bedpath, mmds_structure)
mmds_true_mat = at.contactToDist(contactMat)
at.makeSymmetric(mmds_true_mat)
for j in range(len(mmds_true_mat)): #remove diagonal
mmds_true_mat[j, j] = 0
mmds_distMat = misc.distMat(mmds_structure)
mmds_rs[i] = misc.pearson(mmds_true_mat, mmds_distMat)
cmds_structure = dt.structure_from_file(
"hic_data/GM12878_combined_{}_10kb_cmds_coords.tsv".format(chrom))
contactMat = dt.matFromBed(bedpath, cmds_structure)
cmds_true_mat = at.contactToDist(contactMat)
at.makeSymmetric(cmds_true_mat)
for j in range(len(cmds_true_mat)): #remove diagonal
cmds_true_mat[j, j] = 0
cmds_distMat = misc.distMat(cmds_structure)
cmds_rs[i] = misc.pearson(cmds_true_mat, cmds_distMat)
