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bricaudbricaud
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correct small bug and adpat to tissue files
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-9
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2 files changed

+257
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‎genotype-graph.ipynb

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@@ -62,14 +62,7 @@
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"genotype_path = os.path.join(s3_path, 'geno_reduced.csv.gz')\n",
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"#genotype_path = os.path.join(s3_path, 'genotype_BXD.csv.gz')\n",
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"genotype = pd.read_csv(genotype_path, storage_options=storage_options)\n",
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"print('File {} Opened.'.format(genotype_path))\n",
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"phenotype_path = os.path.join(s3_path, 'Phenotype.txt.gz')\n",
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"phenotype = pd.read_csv(phenotype_path, sep='\\t', storage_options=storage_options)\n",
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"print('File {} Opened.'.format(phenotype_path))\n",
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"# Phenotype description\n",
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"phenotypeinfo_path = os.path.join(s3_path, 'phenotypes_id_aligner.txt.gz')\n",
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"phenotypeinfo = pd.read_csv(phenotypeinfo_path, sep='\\t', storage_options=storage_options)\n",
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"print('File {} Opened.'.format(phenotypeinfo_path))"
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"print('File {} Opened.'.format(genotype_path))"
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]
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},
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{
@@ -128,7 +121,7 @@
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" sigma = 1\n",
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" return np.exp(- sigma * d)\n",
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" \n",
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"M = csr_matrix(geno_knn.shape)\n",
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"M = geno_knn.copy()\n",
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"M.data = distance2weight(geno_knn.data)\n",
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"\n",
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"print('A distance of 1 becomes a weight of {}.'.format(str(distance2weight(1))))"

‎tissue-graph.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Graph of gene expression similarity inside a tissue\n",
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"The idea here is to build a similarity graph between gene expression.The idea is the same as for the genotype graph, see the \"genotype graph\" notebook for more info.\n",
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"\n",
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"In this notebook, proteins or gene expression are nodes of the graph. They are connected to their k nearest neighbors. The connections are weighted by the similarity between two protein expression according to a chosen distance. To each protein is associated a vector encoding its variations over the BXD mouse dataset. Two proteins are similar if their vectors are close in term of Euclidean distance."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from scipy.sparse import csr_matrix\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import networkx as nx\n",
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"import sklearn.metrics\n",
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"import sklearn.neighbors\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Importing the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Config for accessing the data on the s3 storage\n",
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"storage_options = {'anon':True, 'client_kwargs':{'endpoint_url':'https://os.unil.cloud.switch.ch'}}\n",
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"s3_path = 's3://lts2-graphnex/BXDmice/'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the data\n",
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"# Tissue\n",
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"tissue_name = 'LiverProt_CD'\n",
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"# Other examples:\n",
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"#tissue_name = 'Eye'\n",
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"#tissue_name = 'Muscle_CD'\n",
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"#tissue_name = 'Hippocampus'\n",
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"#tissue_name = 'Gastrointestinal'\n",
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"#tissue_name = 'Lung'\n",
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"tissue_path = os.path.join(s3_path, 'expression data', tissue_name + '.txt.gz')\n",
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"tissue = pd.read_csv(tissue_path, sep='\\t', storage_options=storage_options)\n",
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"print('File {} Opened.'.format(tissue_path))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Computing the distances"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Remove the columns (mouse strains) where there are no measurement:\n",
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"tissue = tissue.dropna(axis=1)\n",
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"# Extract the data as a numpy array (drop the first columns)\n",
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"tissue_values = tissue.iloc[:,2:].values"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Normalizing\n",
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"If unormalized, the graph of gene expression may not account for correlated expressions but only for similar concentration."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import normalize\n",
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"tissue_values = normalize(tissue_values, norm='l2', axis=1)\n",
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"tissue_values.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Default distance is Euclidean\n",
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"num_neighbors = 4\n",
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"tissue_knn = sklearn.neighbors.kneighbors_graph(tissue_values, num_neighbors, mode='distance')\n",
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"# Optionally, one can use the following function to compute all the distances:\n",
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"#geno_distances = sklearn.metrics.pairwise_distances(geno_values)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Distribution of weights\n",
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"plt.hist(tissue_knn.data, bins=50)\n",
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"plt.title('Distribution of distances')\n",
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"plt.xlabel('Distance')\n",
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"plt.ylabel('Nb of edges')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Distance to weight\n",
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"# Modify the non-zero values to turn them into weights instead of distances\n",
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"def distance2weight(d):\n",
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" sigma = 1\n",
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" return np.exp(- sigma * d)\n",
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" \n",
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"M = tissue_knn.copy() #csr_matrix(tissue_knn.shape)\n",
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"M.data = distance2weight(tissue_knn.data)\n",
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"\n",
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"print('A distance of 1 becomes a weight of {}.'.format(str(distance2weight(1))))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Distribution of weights\n",
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"plt.hist(M.data, bins=20)\n",
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"plt.title('Distribution of weights')\n",
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"plt.xlabel('Weight value')\n",
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"plt.ylabel('Nb of edges')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Building the graph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"G = nx.from_scipy_sparse_matrix(M)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Adding info on the nodes of the graph\n",
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"tissueinfo_dic = tissue[['gene']].to_dict()\n",
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"nx.set_node_attributes(G, tissueinfo_dic['gene'], name='Gene') # gene name"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Saving the graph as a gexf file readable with Gephi.\n",
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"nx.write_gexf(G,tissue_name + 'graph.gexf')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Graph plotted using Gephi, colored by community (communities found automatically with Gephi). The gene expression forms two distinct clusters. \n",
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"\n",
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"![gene expression graph](liver_gene_expression.png)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Applications of the graph\n",
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"There are different possible applications of this graph, see the \"genotype graph\" notebook for examples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "venv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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