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import operator
import os
from typing import Mapping
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import scanpy as sc
import seaborn as sns
import tqdm
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import MinMaxScaler
# The class is modified from https://github.com/nceglia/genevector
class GeneEmbedding(object):
def __init__(self, embeddings: Mapping):
self.embeddings = embeddings
# self.context = dataset.data
self.vector = []
self.genes = []
for gene in tqdm.tqdm(self.embeddings.keys()):
self.vector.append(self.embeddings[gene])
self.genes.append(gene)
def read_embedding(self, filename):
embedding = dict()
lines = open(filename, "r").read().splitlines()[1:]
for line in lines:
vector = line.split()
gene = vector.pop(0)
embedding[gene] = np.array([float(x) for x in vector])
return embedding
def get_adata(self, resolution=20):
mat = np.array(self.vector)
np.savetxt(".tmp.txt", mat)
gdata = sc.read_text(".tmp.txt")
os.remove(".tmp.txt")
gdata.obs.index = self.genes
sc.pp.neighbors(gdata, use_rep="X")
sc.tl.leiden(gdata, resolution=resolution)
sc.tl.umap(gdata)
return gdata
def plot_similarities(self, gene, n_genes=10, save=None):
df = self.compute_similarities(gene).head(n_genes)
_, ax = plt.subplots(1, 1, figsize=(3, 6))
sns.barplot(data=df, y="Gene", x="Similarity", palette="magma_r", ax=ax)
ax.set_title("{} Similarity".format(gene))
if save != None:
plt.savefig(save)
def plot_metagene(self, gdata, mg=None, title="Gene Embedding"):
highlight = []
labels = []
clusters = collections.defaultdict(list)
for x, y in zip(gdata.obs["leiden"], gdata.obs.index):
clusters[x].append(y)
if x == mg:
highlight.append(str(x))
labels.append(y)
else:
highlight.append("_Other")
_labels = []
for gene in labels:
_labels.append(gene)
gdata.obs["Metagene {}".format(mg)] = highlight
_, ax = plt.subplots(1, 1, figsize=(8, 6))
sc.pl.umap(gdata, alpha=0.5, show=False, size=100, ax=ax)
sub = gdata[gdata.obs["Metagene {}".format(mg)] != "_Other"]
sc.pl.umap(
sub,
color="Metagene {}".format(mg),
title=title,
size=200,
show=False,
add_outline=False,
ax=ax,
)
for gene, pos in zip(gdata.obs.index, gdata.obsm["X_umap"].tolist()):
if gene in _labels:
ax.text(
pos[0] + 0.04,
pos[1],
str(gene),
fontsize=6,
alpha=0.9,
fontweight="bold",
)
plt.tight_layout()
def plot_metagenes_scores(self, adata, metagenes, column, plot=None):
plt.figure(figsize=(5, 13))
matrix = []
meta_genes = []
cfnum = 1
cfams = dict()
for cluster, vector in metagenes.items():
row = []
cts = []
for ct in set(adata.obs[column]):
sub = adata[adata.obs[column] == ct]
val = np.mean(sub.obs[str(cluster) + "_SCORE"].tolist())
row.append(val)
cts.append(ct)
matrix.append(row)
label = str(cluster) + "_SCORE: " + ", ".join(vector[:10])
if len(set(vector)) > 10:
label += "*"
meta_genes.append(label)
cfams[cluster] = label
cfnum += 1
matrix = np.array(matrix)
df = pd.DataFrame(matrix, index=meta_genes, columns=cts)
plt.figure()
sns.clustermap(
df,
figsize=(5, 9),
dendrogram_ratio=0.1,
cmap="mako",
yticklabels=True,
standard_scale=0,
)
plt.tight_layout()
if plot:
plt.savefig(plot)
def score_metagenes(self, adata, metagenes):
for p, genes in metagenes.items():
try:
sc.tl.score_genes(adata, score_name=str(p) + "_SCORE", gene_list=genes)
scores = np.array(adata.obs[str(p) + "_SCORE"].tolist()).reshape(-1, 1)
scaler = MinMaxScaler()
scores = scaler.fit_transform(scores)
scores = list(scores.reshape(1, -1))[0]
adata.obs[str(p) + "_SCORE"] = scores
except Exception as e:
adata.obs[str(p) + "_SCORE"] = 0.0
def get_metagenes(self, gdata):
metagenes = collections.defaultdict(list)
for x, y in zip(gdata.obs["leiden"], gdata.obs.index):
metagenes[x].append(y)
return metagenes
def compute_similarities(self, gene, subset=None, feature_type=None):
if gene not in self.embeddings:
return None
# if feature_type:
# subset = []
# for gene in list(self.embeddings.keys()):
# if feature_type == self.context.feature_types[gene]:
# subset.append(gene)
embedding = self.embeddings[gene]
distances = dict()
if subset:
targets = set(list(self.embeddings.keys())).intersection(set(subset))
else:
targets = list(self.embeddings.keys())
for target in targets:
if target not in self.embeddings:
continue
v = self.embeddings[target]
distance = float(
cosine_similarity(
np.array(embedding).reshape(1, -1), np.array(v).reshape(1, -1)
)[0]
)
distances[target] = distance
sorted_distances = list(
reversed(sorted(distances.items(), key=operator.itemgetter(1)))
)
genes = [x[0] for x in sorted_distances]
distance = [x[1] for x in sorted_distances]
df = pd.DataFrame.from_dict({"Gene": genes, "Similarity": distance})
return df
# def clusters(self, clusters):
# average_vector = dict()
# gene_to_cluster = collections.defaultdict(list)
# matrix = collections.defaultdict(list)
# total_average_vector = []
# for gene, cluster in zip(self.context.expressed_genes, clusters):
# if gene in self.embeddings:
# matrix[cluster].append(self.embeddings[gene])
# gene_to_cluster[cluster].append(gene)
# total_average_vector.append(self.embeddings[gene])
# self.total_average_vector = list(np.average(total_average_vector, axis=0))
# for cluster, vectors in matrix.items():
# xvec = list(np.average(vectors, axis=0))
# average_vector[cluster] = np.subtract(xvec, self.total_average_vector)
# return average_vector, gene_to_cluster
def generate_weighted_vector(self, genes, markers, weights):
vector = []
for gene, vec in zip(self.genes, self.vector):
if gene in genes and gene in weights:
vector.append(weights[gene] * np.array(vec))
if gene not in genes and gene in markers and gene in weights:
vector.append(list(weights[gene] * np.negative(np.array(vec))))
return list(np.sum(vector, axis=0))
def generate_vector(self, genes):
vector = []
for gene, vec in zip(self.genes, self.vector):
if gene in genes:
vector.append(vec)
assert len(vector) != 0, genes
return list(np.average(vector, axis=0))
def generate_weighted_vector(self, genes, weights):
vector = []
weight = []
for gene, vec in zip(self.genes, self.vector):
if gene in genes and gene in weights:
vector.append(vec)
weight.append(weights[gene])
assert len(vector) != 0, genes
return list(np.average(vector, axis=0, weights=weight))
def cluster_definitions_as_df(self, top_n=20):
similarities = self.cluster_definitions
clusters = []
symbols = []
for key, genes in similarities.items():
clusters.append(key)
symbols.append(", ".join(genes[:top_n]))
df = pd.DataFrame.from_dict({"Cluster Name": clusters, "Top Genes": symbols})
return df
@staticmethod
def read_vector(vec):
lines = open(vec, "r").read().splitlines()
dims = lines.pop(0)
vecs = dict()
for line in lines:
try:
line = line.split()
gene = line.pop(0)
vecs[gene] = list(map(float, line))
except Exception as e:
continue
return vecs, dims
def get_similar_genes(self, vector):
distances = dict()
targets = list(self.embeddings.keys())
for target in targets:
if target not in self.embeddings:
continue
v = self.embeddings[target]
distance = float(
cosine_similarity(
np.array(vector).reshape(1, -1), np.array(v).reshape(1, -1)
)[0]
)
distances[target] = distance
sorted_distances = list(
reversed(sorted(distances.items(), key=operator.itemgetter(1)))
)
genes = [x[0] for x in sorted_distances]
distance = [x[1] for x in sorted_distances]
df = pd.DataFrame.from_dict({"Gene": genes, "Similarity": distance})
return df
def generate_network(self, threshold=0.5):
G = nx.Graph()
a = pd.DataFrame.from_dict(self.embeddings).to_numpy()
similarities = cosine_similarity(a.T)
genes = list(self.embeddings.keys())
similarities[similarities < threshold] = 0
edges = []
nz = list(zip(*similarities.nonzero()))
for n in tqdm.tqdm(nz):
edges.append((genes[n[0]], genes[n[1]]))
G.add_nodes_from(genes)
G.add_edges_from(edges)
return G
@staticmethod
def average_vector_results(vec1, vec2, fname):
output = open(fname, "w")
vec1, dims = GeneEmbedding.read_vector(vec1)
vec2, _ = GeneEmbedding.read_vector(vec2)
genes = list(vec1.keys())
output.write(dims + "\n")
for gene in genes:
v1 = vec1[gene]
v2 = vec2[gene]
meanv = []
for x, y in zip(v1, v2):
meanv.append(str((x + y) / 2))
output.write("{} {}\n".format(gene, " ".join(meanv)))
output.close()
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