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Network medicine and single cell for Alzheimer
Commit cd9227e2 authored by Andrea's avatar Andrea
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code/functions/functions_proximity.py 0 → 100644
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#! /usr/bin/env python
"""
# ---------------------------------------------------------------------------
# functions_proximity.py
# File with all the functions that I have used to calculate the distance
# and the proximity between diseases and drugs and between the pathologies
#included in three groups of conditions: neuro-neuro, neuro-noneuro and
#noneuro-noneuro.
#
#
# ----------------------------------------------------------------------------
"""
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import networkx as nx
from scipy.stats import norm
from itertools import combinations
from tqdm import tqdm
import re
from itertools import product
from statannotations.Annotator import Annotator
# =================================================================================
def genes_dis(enf, file):
"""
This function creates a list with the genes associated with the disease "enf" in the dis_gen file
"""
genes=[]
for i, dis in enumerate(file["cui"]):
if dis == enf:
gen = file["gene_id"][i]
genes.append(gen)
return genes
# =================================================================================
def pro_gen_dict(gene_list, file):
"""
This function creates a dictionary from the list of genes associated with the disease with:
key: protein associated with each gene in the gen_pro file
value: gene related to the key protein in the gen_pro file
"""
result_dict = {}
for i, gen in enumerate(file["gene_id"]):
# Looping through gen_pro, which relates genes and proteins.
# I'm storing the position of the gene (i) and the gene id (gen).
if gen in gene_list:
# Searching each gene in gen_pro within the corresponding gene list of each disease.
prot = file["protein_id"][i]
# If that gene is in the gene list of each disease, I find the associated protein at the same position.
result_dict[prot] = gen
# Adding to each disease's dictionary the protein as key and the related gene as value.
return result_dict
# =================================================================================
def gen_pro_PPI(dict1, file):
"""
From a dictionary with the relationships between proteins and genes associated with each of our diseases,
this function retains the prot:gen relationship from the dictionary only if such prot appears in the PPI network of the pro_pro file.
key: proteins appearing in the PPI network
value: genes related to the key protein
"""
result_dict = {}
for prot in dict1.keys():
# Iterating over all proteins in the general prot:gen dictionary.
if prot in file["prA"].tolist() or prot in file["prB"].tolist():
# Selecting proteins that appear in the PPI network.
result_dict[prot] = dict1[prot]
# Adding to the PPI prot:gen dictionary only the prot:gen relationships for proteins that are in the PPI.
return result_dict
# =================================================================================
def lcc(SG):
"""
This function gives us the LCC of the proteins from the PPI network associated with a disease from a subgraph
formed only with the proteins associated with the disease.
"""
lcc = max(nx.connected_components(SG), key=len)
# Calculating the LCC (module comprising the largest number of proteins associated with a disease).
# Our goal is to obtain the number of genes that are part of the LCC of the disease:
# The number of proteins from the disease in the LCC is the same number as the genes in the LCC
# (because we have extracted the list of proteins from the dictionary where they form a tuple with their associated genes).
return lcc
# =================================================================================
def nodes_by_degree(G):
"""
This function returns a dictionary where we will obtain the degrees as keys and, in the values, all the nodes of the network that contain that degree.
"""
degree_dict = {}
for node in G.nodes():
degree = G.degree(node)
if degree not in degree_dict:
degree_dict[degree] = []
degree_dict[degree].append(node)
return degree_dict
# =================================================================================
def degrees_list(G):
"""
This function returns a list with the nodes and another list with their degrees.
"""
nodes = list(G.nodes())
degrees = list(dict(G.degree()).values())
return nodes, degrees
# =================================================================================
def targets(drug_list, arch):
"""
Input data: list of drugs treating a disease (drug_list) and a file with drug-target relationships (arch).
This function allows us to obtain a DataFrame with drugs in the "Drugs" column and their targets in the "Targets" column.
"""
targets_total = []
# List to store targets for each drug separated by commas
drugs = []
# List to store drugs found in the 'arch' file
for drug1 in drug_list:
# Iterate over each drug in the drug list
targets = []
# Empty list to store targets for each drug
for i, drug2 in enumerate(arch["dru"]):
# Iterate over drugs in the 'arch' file, keeping track of the drug (drug2) and its row (i) in the drug column
if drug1 == drug2:
# If a drug for a disease is found in the drug-target file
targets.append(arch["pro"][i])
# Add its target to the targets list, which will be in the same row (i) in the target column
if len(targets) > 0:
# Check if the targets list is not empty
drugs.append(drug1)
# Add the drug to the list of drugs, so only drugs appearing in the 'arch' file are stored
targets_total.append(targets)
# Add the list of targets for that drug to the list of targets for all drugs
data = {"Drugs": drugs, "Targets": targets_total}
# Combine the data and classify them into Drugs and Targets
df = pd.DataFrame(data)
# Create a DataFrame with the results
return
# =================================================================================
def targets_dict(drug_list, arch):
"""
Input data: list of drugs treating a disease (drug_list) and a file with drug-target relationships (arch).
This function allows us to obtain a dictionary with drugs as keys and their targets as values.
"""
drug_target_dict = {}
# Dictionary to store drugs and their associated targets
for drug1 in drug_list:
# Iterate over each drug in the drug list
targets = []
# Empty list to store targets for each drug
for i, drug2 in enumerate(arch["dru"]):
# Iterate over drugs in the 'arch' file, keeping track of the drug (drug2) and its row (i) in the drug column
if drug1 == drug2:
# If a drug for a disease is found in the drug-target file
targets.append(arch["pro"][i])
# Add its target to the targets list, which will be in the same row (i) in the target column
if targets:
# Check if the targets list is not empty
drug_target_dict[drug1] = targets
# Add the drug and its list of targets to the dictionary
return drug_target_dict
# =================================================================================
def calculate_random_drug_target(num_iterations, drug_target_dict, PPI):
"""
This function performs the random selection of targets for each drug.
:param num_iterations: Number of random iterations
:param drug_target_dict: Dictionary with drugs as keys and their list of targets as values
:param PPI: Protein-protein interaction network
:return: Dictionary with drugs as keys and lists of 1000 random targets as values
"""
results = {drug: [] for drug in drug_target_dict.keys()} # Initialize results dictionary with empty lists
group_nodes_by_degree = nodes_by_degree(PPI) # Group nodes by their degree
for drug, target_list in tqdm(drug_target_dict.items(), total=len(drug_target_dict)): # for each drug
drug_random_targets = [] # To store the lists of random targets
for _ in range(num_iterations): # Create lists of random targets
drug_targets = []
for target in target_list: # for each target in the target list
if target in PPI.nodes():
degree_target = PPI.degree(target) # calculate its degree
available_targets = group_nodes_by_degree.get(degree_target,
[]) # choose proteins from the PPI with the same degree
if available_targets:
random_target = np.random.choice(
available_targets) # randomly select a target with the same degree
drug_targets.append(random_target)
drug_random_targets.append(drug_targets) # Add the list of random targets
results[drug].append(drug_random_targets) # Store the lists of random targets in the results
return results
# =================================================================================
def calculate_dc_drug(target_list, dist_matrix, disease_module_proteins, PPI):
"""
Input data: list of targets of the drug (target_list), proteins of the disease module (disease_module_proteins),
matrix file with distances between all nodes of the PPI (dist_matrix), PPI network (PPI).
This function returns the closest measure (dc) of a drug and a disease.
"""
targets_in_ppi = set(target_list) & set(PPI.nodes())
# List of targets of the drug that are also in the PPI network
targets_in_disease_module = set(targets_in_ppi) & set(disease_module_proteins)
# List of targets of the drug that are also part of the disease module
if not targets_in_ppi:
return np.nan
distances_disease_target = dist_matrix.loc[list(targets_in_ppi), list(disease_module_proteins)].values
# Generate a matrix with the shortest path lengths (SPLs) between all drug targets and all proteins in the disease module according to the distance file
if np.isnan(distances_disease_target).all():
return np.nan # No path between an drug target and the disease
elif len(targets_in_disease_module) == len(targets_in_ppi):
# If all drug targets are part of the disease module
return 0
# The dc value will be 0
else:
non_empty_rows = ~np.isnan(distances_disease_target).all(axis=1)
return np.nanmean(np.nanmin(distances_disease_target[non_empty_rows], axis=1))
# Otherwise, calculate the mean of the minimum SPLs of targets that have a path to the disease module (the mean of the minimum values of each row of the matrix)
# =================================================================================
def proximity(df, arch_dist, prots_enf, PPI):
"""
Input data: DataFrame (df) with "Drugs" and their "Targets"; file with distances between all nodes of the network (arch_dist);
list of proteins related to a disease (prots_enf); and PPI network (PPI).
Function that allows us to obtain a DataFrame with the proximity values (dc) for a list of drugs and a disease.
"""
dc_total = [] # List to store the dc value of all drugs
diseases = [] # List to add whether the drug belongs to a specific disease
for i, drug in enumerate(df["Drugs"]): # For each drug in the drugs and targets DataFrame, I keep the row (i)
targets_list = df["Targets"][i] # Get the list of targets for that drug, which will be in the same row as the drug but in the targets column
dc_total.append(calculate_dc_drug(targets_list, arch_dist, prots_enf, PPI))
data = {'Drugs': list(df["Drugs"]), 'dc': dc_total} # Save the relationship between the list of drugs and the list of dcs
result_table = pd.DataFrame(data) # Convert the results into a DataFrame
return result_table
# =================================================================================
def proximity_random(df, arch_dist, prots_enf, PPI, num_iterations, df_results):
"""
Input data: DataFrame (df) with "Drugs" and their "Targets", file with distances between all nodes of the network (arch_dist),
list of proteins related to a disease (prots_enf), PPI network (PPI), and the number of iterations (num_iterations)
to calculate the dc (num_iterations), DataFrame (df_results) with the results of random target modules iterations for each drug.
This function allows us to obtain a DataFrame with the average proximity values (dc) calculated from:
- 1000 random modules of proteins with the same number of proteins and the same degree distribution as the disease module.
- 1000 random target modules with the same number of proteins and the same degree distribution as each drug in the Drugs and Targets DataFrame.
"""
group_nodes_degree = nodes_by_degree(PPI) # Group nodes by their degree
# Initialize a matrix with null values that has the same number of rows as the total number of drugs in the DataFrame
# and the same number of columns as the number of iterations
proximity_matrix = np.full((len(df["Drugs"]), num_iterations), None, dtype=object)
for i in range(num_iterations): # For each iteration
# Random disease module
random_prots = set() # Create a set of random proteins
for prot in prots_enf: # For each protein in the disease module
degree_prot = PPI.degree(prot) # Calculate its degree
available_prots = group_nodes_degree[degree_prot] # Choose proteins from the PPI with the same degree
random_prots.add(np.random.choice(available_prots)) # Choose the same number of nodes as the disease module, taken from the total list of PPI proteins randomly
# Random target module
random_targets = {} # Create a dictionary to store random targets for each drug
for j, drug in enumerate(df["Drugs"]): # For each drug in the DataFrame
random_target = df_results.iloc[j, i] # Get the random target corresponding to this iteration for this drug
random_targets[drug] = random_target # Store the random target in the dictionary
# Calculate the dc of each drug with the random disease module and the random target
drug_dc_total = calculate_dc_drug(random_target, arch_dist, random_prots, PPI)
proximity_matrix[j, i] = drug_dc_total # Add the dc of each drug (in row j) in the column of the matrix corresponding to the iteration (i)
drug_mean_proximity = [] # List to add the mean of dc after 1000 iterations for each drug
deviation = [] # List to add the deviation of dc after 1000 iterations for each drug
for row in proximity_matrix: # For each row (for each drug)
if all(x is np.nan for x in row): # If the entire row is None
drug_mean_proximity.append(np.nan) # Add None to the drug_mean_proximity list
deviation.append(np.nan) # Add None to the deviation list
else:
drug_mean_proximity.append(np.nanmean(row)) # Add the mean of that row
deviation.append(np.nanstd(row)) # Standard deviation
data = {'Drugs': list(df["Drugs"]), 'dc_mean': drug_mean_proximity, 'dc_std' : deviation}
result_table = pd.DataFrame(data)
return result_table
# =================================================================================
def proximity_random_modules(df, arch_dist, random_modules_noneuro, target_random_module, PPI, num_iterations):
"""
Input data:
- DataFrame (df) with "Drugs" and their "Targets"
- Dictionary (random_modules_noneuro) with disease_id as keys and values composed of 1000 lists of random proteins of their module
- Dictionary (target_random_module) with drug_id as keys and values composed of 1000 lists of random targets of the corresponding drug
- File with distances between all nodes of the network (arch_dist)
- PPI network (PPI)
- Number of iterations (num_iterations) to calculate the dc
This function calculates the average proximity values (dc) between random disease modules and random drug target modules.
"""
# Initialize a matrix with null values that has the same number of rows as the total number of drugs in the DataFrame
# and the same number of columns as the number of iterations
proximity_matrix = np.full((len(df["Drugs"]), num_iterations), np.nan, dtype=float)
for i in range(0, num_iterations): # For each iteration
for j, row in df.iterrows():
# Select the random disease module for this iteration
disease_id = row["ID"] # Assuming a single disease per df, otherwise, adjust accordingly
drug_id = row['Drugs']
# Get the random disease module and random target module for this iteration
random_prots = set(random_modules_noneuro[disease_id][i]) #
random_target = target_random_module[drug_id][0][i]
# random_target_list = [item[0] for item in random_target]
# Random target module
# Calculate the dc of each drug with the random disease module and the random target
drug_dc_total = calculate_dc_drug(random_target, arch_dist, random_prots, PPI)
proximity_matrix[j, i] = drug_dc_total # Store the dc value in the matrix
drug_mean_proximity = np.nanmean(proximity_matrix,
axis=1) # List to add the mean of dc after num_iterations for each drug
deviation = np.nanstd(proximity_matrix,
axis=1) # List to add the deviation of dc after num_iterations for each drug
# Prepare the result as a DataFrame
data = {'Drugs': list(df["Drugs"]), 'dc_mean': drug_mean_proximity, 'dc_std': deviation}
result_table = pd.DataFrame(data)
return result_table
# =================================================================================
def calculate_random_drug_target_modules(num_iterations, df, PPI):
results = {}
group_nodes_by_degree = nodes_by_degree(PPI) # Group nodes by their degree
for i in range(num_iterations): # for each iteration
iteration_results = {}
for j, drug in enumerate(df["Drugs"]): # for each drug, keep its row (j)
target_list = df["Targets"][j] # get the list of targets for that drug
target_list_PPI = set(target_list) & set(PPI.nodes()) # get the drug's targets that are in the PPI
drug_targets = []
for target in target_list_PPI: # for each target in the target list
degree_target = PPI.degree(target) # calculate its degree
available_targets = group_nodes_by_degree[degree_target] # choose proteins from the PPI with the same degree
random_target = np.random.choice(available_targets) # same number of nodes as the disease module, taken from the total list of PPI proteins randomly
drug_targets.append(random_target)
iteration_results[drug] = drug_targets
results[i] = iteration_results
df_results = pd.DataFrame(results)
return df_results
# =================================================================================
def determine_treatment(row, dis_dru_the):
disease = row['ID']
drug = row['Drugs']
treatments = dis_dru_the[(dis_dru_the['dis'] == disease) & (dis_dru_the['dru'] == drug)]
treatment = 'yes' if treatments.shape[0] > 0 else 'unknown'
return treatment
# =================================================================================
def rep_prox_dis_drug(df_combined):
"""
This function represents in a boxplot the distribution of proximity to a disease and
the distribution of its z-score for the group of drugs used to treat the disease
and the group of drugs not used for its treatment (unknown).
Input:
1. df_combined: DataFrame combined with drugs, their observed proximity, their average random proximity,
the standard deviation of random proximity, their z-score, a column indicating the disease, and another column indicating if the drug
is used for the treatment of the disease in the same row.
"""
# Combine the two datasets into a single subplot
fig, axes = plt.subplots(1, 2, figsize=(16, 6)) # Create a subplot with 1 row and 2 columns
# Plot the boxplot with both proximity distributions
sns.boxplot(x='Dataset', y='Closest distance', data=df_combined, hue='Treatment', ax=axes[0], palette={'yes': '#FF7A7A', 'unknown': '#79C4FF'}, dodge=True, medianprops=dict(linewidth=2))
axes[0].set_ylabel('Closest distance ($\mathregular{d_c}$)', fontsize=12)
axes[0].set_xlabel('')
for label in axes[0].get_xticklabels():
label.set_fontsize(12)
axes[0].set_xticks([0,1],['Neuro', 'No Neuro'])
axes[0].legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize=12)
# Plot the boxplot with both proximity distributions
sns.boxplot(x='Dataset', y='Dc_zscore', data=df_combined, hue='Treatment', ax=axes[1], palette={'yes': '#FF7A7A', 'unknown': '#79C4FF'}, dodge=True, medianprops=dict(linewidth=2))
axes[1].set_ylabel('Proximity [z-score ($\mathregular{d_c}$)]', fontsize=12)
axes[1].set_xlabel('')
for label in axes[1].get_xticklabels():
label.set_fontsize(12)
axes[1].set_xticks([0,1],['Neuro', 'No Neuro'])
axes[1].legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize=12)
plt.tight_layout() # Adjust the layout of the subplot to avoid overlap
plt.show()
# =================================================================================
def rep_prox_dis_dis(df_combined):
"""
This function represents in a boxplot the distribution of the closest distance to a disease and
the distribution of its z-score for the group of drugs used to treat the disease
and the group of drugs not used for its treatment (unknown).
Input:
1. df_combined: DataFrame combined with drugs, their observed proximity, their average random proximity,
the standard deviation of random proximity, their z-score, a column indicating the disease, and another column indicating if the drug
is used for the treatment of the disease in the same row.
"""
# Define custom colors
colors = ['dodgerblue','steelblue', 'skyblue']
# Combine the two datasets into a single subplot
fig, axes = plt.subplots(1, 2, figsize=(14, 6)) # Create a subplot with 1 row and 2 columns
# Plot the boxplot with both proximity distributions for closest distance
sns.boxplot(x='dataset', y='Closest distance', data=df_combined, palette=colors, ax=axes[0], dodge=False, medianprops=dict(linewidth=2))
axes[0].set_ylabel('Closest distance ($\mathregular{d_c}$)', fontsize=12)
axes[0].set_xlabel('')
axes[0].set_xticklabels(['Neuro - Neuro', 'Neuro - No neuro', 'No neuro - No neuro'])
# Plot the boxplot with both proximity distributions for z-score
sns.boxplot(x='dataset', y='Dc_zscore', data=df_combined, palette=colors, ax=axes[1], dodge=False, medianprops=dict(linewidth=2))
axes[1].set_ylabel('Proximity [z-score ($\mathregular{d_c}$)]', fontsize=12)
axes[1].set_xlabel('')
axes[1].set_xticklabels(['Neuro - Neuro', 'Neuro - No neuro', 'No neuro - No neuro'])
plt.tight_layout() # Adjust the layout of the subplot to avoid overlap
plt.show()
# =================================================================================
def calculate_dc_dis(lcc_A, dist_matrix, lcc_B, PPI):
"""
Input data: list of proteins in disease A LCC (lcc_A), proteins of the disease module of disease B (lcc_B),
matrix file with distances between all nodes of the PPI (dist_matrix), PPI network (PPI).
This function returns the closest measure (dc) of two disease modules in the interactome.
"""
distances_disease_disease = dist_matrix.loc[list(lcc_A), list(lcc_B)].values
# Generate a matrix with the shortest path lengths (SPLs) between all proteins in the disease module of A and B according to the distance file
non_empty_rows = ~np.isnan(distances_disease_disease).all(axis=1)
# Keep rows that are not empty, i.e., remove proteins of the disease module A that have no path to any protein in the disease module B
if np.isnan(distances_disease_disease).all():
# If the previous matrix is empty
return np.nan
# There is no path between any protein of the diseases
else:
return np.nanmean(np.nanmin(distances_disease_disease[non_empty_rows], axis=1))
# Otherwise, calculate the mean of the minimum SPLs of targets that have a path to the disease module (the mean of the minimum values of each row of the matrix)
# =================================================================================
def proximity_dis(dict_dis, df, arch_dist, prots_dis, PPI):
"""
Input data: Dictionary (dict_dis) with neurological disease and their proteins in its lcc; dataframe (df) with
all neurological disease cui, file with distances between all nodes of the network (arch_dist);
list of proteins related to a disease (prots_dis); and PPI network (PPI).
Function that allows us to obtain a DataFrame with the proximity values (dc) for a list of drugs and a disease.
"""
dc_total = [] # List to store the dc value
for i, dis in enumerate(df["cui"]): # For each disease in the disease and LCC DataFrame, I keep the row (i)
lcc_list = dict_dis[dis] # Get the list of targets for that drug, which will be in the same row as the drug but in the targets column
dc_total.append(calculate_dc_dis(lcc_list, arch_dist, prots_dis, PPI))
data = {'Disease B': df["cui"], 'dc': dc_total} # Save the relationship between the list of drugs and the list of dcs
result_table = pd.DataFrame(data) # Convert the results into a DataFrame
return result_table
# =================================================================================
def proximity_random_dis(dis, drug, dict_targets, dict_dis, arch_dist, PPI):
"""
Input data: DataFrame (df) with "Drugs" and their "Targets", file with distances between all nodes of the network (arch_dist),
list of proteins related to a disease (prots_enf), PPI network (PPI), and the number of iterations (num_iterations)
to calculate the dc (num_iterations), DataFrame (df_results) with the results of random target modules iterations for each drug.
This function allows us to obtain a DataFrame with the average proximity values (dc) calculated from:
- 1000 random modules of proteins with the same number of proteins and the same degree distribution as the disease module.
- 1000 random target modules with the same number of proteins and the same degree distribution as each drug in the Drugs and Targets DataFrame.
"""
# Initialize a matrix with null values that has the same number of rows as the total number of drugs in the DataFrame
# and the same number of columns as the number of iterations
proximity_matrix = np.full((1, 1000), None, dtype=object)
# dict of disease
dict_random_dis = dict_dis[dis]
# dict of drugs
dict_random_drug = dict_targets[drug]
# Random disease module
for index in range(len(dict_random_dis)):
random_lcc = dict_random_dis[index]
random_targets = dict_random_drug[index]
# Calculate the dc of each drug with the random disease module and the random target
drug_dc_total = calculate_dc_dis(random_targets, arch_dist, random_lcc, PPI)
proximity_matrix[0, index] = drug_dc_total # Add the dc of each drug (in row j) in the column of the matrix corresponding to the iteration (i)
drug_mean_proximity = [] # List to add the mean of dc after 1000 iterations for each drug
deviation = [] # List to add the deviation of dc after 1000 iterations for each drug
for row in proximity_matrix: # For each row (for each drug)
if all(x is np.nan for x in row): # If the entire row is None
drug_mean_proximity.append(np.nan) # Add None to the drug_mean_proximity list
deviation.append(np.nan) # Add None to the deviation list
else:
drug_mean_proximity.append(np.nanmean(row)) # Add the mean of that row
deviation.append(np.nanstd(row)) # Standard deviation
data = {'Disease B': disB, 'dc_mean': drug_mean_proximity, 'dc_std' : deviation}
result_table = pd.DataFrame(data)
return result_table
# =================================================================================
def generate_log_bins(graph, num_bins=10):
"""
This function generates logarithmic bins to group nodes of a graph based on
the degree distribution of the nodes.
Input:
1. graph: The graph whose nodes' degrees are to be binned.
2. num_bins: Number of bins to be obtained.
Returns:
A numpy array of logarithmically spaced bins.
"""
degrees = [degree for _, degree in graph.degree()]
min_degree = max(min(degrees), 1) # Para evitar log(0)
max_degree = max(degrees)
return np.logspace(np.log10(min_degree), np.log10(max_degree), num_bins)
# =================================================================================
def group_nodes_by_bins(graph, log_bins):
"""
This function groups nodes of a graph in logarithmic bins based on its degree.
Input:
1.graph
2. log_bins: logarithmic bins
"""
nodes_bins = {}
for node, degree in graph.degree():
bin_index = np.digitize(degree, log_bins) - 1 # Ajustar índice para Python (basado en 0)
nodes_bins.setdefault(bin_index, []).append(node)
return nodes_bins
# =================================================================================
def random_subset_generator(prot_dict, graph_ppi, num_iterations, file_path):
"""
This function generates random subsets of proteins in the disease module
of each of the analyzed pathologues
Input:
1.prot_dict: dictionary with the proteins in the disease module of each disease
2. graph_ppi: interactome
3. num_iterations: number of iterations
"""
# Generation of logarithmic bins
num_bins = 10
bin_edges = generate_log_bins(graph_ppi, num_bins)
# Group nodes in logarithmic bins
group_nodes_bins = group_nodes_by_bins(graph_ppi, bin_edges)
results_dict = {disease: [] for disease in prot_dict.keys()} # dict with random proteins for each disease
for dis in tqdm(list(prot_dict.keys())):
prots = prot_dict[dis]
for _ in range(num_iterations): # For each iterations
results = [] # list to append proteins for each disease
for prot in prots:
# degree of the node
degree_node = graph_ppi.degree(prot)
# bin of the node based on its degree
bin_index = np.digitize(degree_node, bin_edges) - 1
# nodes of the same bin
available_nodes = group_nodes_bins.get(bin_index, [])
if available_nodes:
random_node = np.random.choice(available_nodes) #choose randomly a node from available nodes
if len(available_nodes) == 1 and random_node == prot:
pass
else:
while random_node == prot:
random_node = np.random.choice(available_nodes)
results.append(random_node)
results_dict[dis].append(results)
# Append the new results to the JSON file
with open(file_path, 'w') as file:
json.dump(results_dict, file, indent=4)
return results_dict
# =================================================================================
def rep_prox_dis_dis(df_combined):
"""
This function represents in a boxplot the distribution of the closest distance to a disease and
the distribution of its z-score for the group of drugs used to treat the disease
and the group of drugs not used for its treatment (unknown).
Input:
1. df_combined: DataFrame combined with drugs, their observed proximity, their average random proximity,
the standard deviation of random proximity, their z-score, a column indicating the disease, and another column indicating if the drug
is used for the treatment of the disease in the same row.
"""
# Define custom colors
colors = ['steelblue','dodgerblue', 'skyblue']
# Combine the two datasets into a single subplot
fig, axes = plt.subplots(1, 2, figsize=(14, 6)) # Create a subplot with 1 row and 2 columns
# Plot the boxplot with both proximity distributions for closest distance
sns.boxplot(x='dataset', y='Closest distance', data=df_combined, palette=colors, ax=axes[0], dodge=False, medianprops=dict(linewidth=2))
axes[0].set_ylabel('Closest distance ($\mathregular{d_c}$)', fontsize=12)
axes[0].set_xlabel('')
axes[0].set_xticklabels(['Neuro - Neuro', 'Neuro - No neuro', 'No neuro - No neuro'])
# Plot the boxplot with both proximity distributions for z-score
sns.boxplot(x='dataset', y='Dc_zscore', data=df_combined, palette=colors, ax=axes[1], dodge=False, medianprops=dict(linewidth=2))
axes[1].set_ylabel('Proximity [z-score ($\mathregular{d_c}$)]', fontsize=12)
axes[1].set_xlabel('')
axes[1].set_xticklabels(['Neuro - Neuro', 'Neuro - No neuro', 'No neuro - No neuro'])
plt.tight_layout() # Adjust the layout of the subplot to avoid overlap
plt.show()
# =================================================================================
code/scrna_ppi_analysis.ipynb
View file @ cd9227e2
......@@ -5,7 +5,7 @@
"source": [
"# Integrating scRNA-seq data with PPI networks to study cell type-specific expression patterns in Alzheimer\n",
"\n",
"This study aims to construct cell type-specific PPI networks, analyze differentially expressed genes (DEGs), and evaluate how gene expression patterns vary across cell types in Alzheimer."
"This study aims toanalyze differentially expressed genes (DEGs), integrate the expression values in the PPI network and evaluate how gene expression patterns vary across cell types in Alzheimer."
],
"metadata": {
"collapsed": false,
......@@ -29,6 +29,7 @@
},
"source": [
"import scanpy as sc\n",
"import sys\n",
"import pandas as pd\n",
"import networkx as nx\n",
"import seaborn as sns\n",
......@@ -41,14 +42,9 @@
"import numpy as np\n",
"from scipy.stats import norm\n",
"from matplotlib.patches import Rectangle\n",
"from adjustText import adjust_text\n",
"from scipy.stats import zscore\n",
"import random\n",
"import glob\n",
"import gseapy as gp\n",
"import os\n",
"import json\n",
"\n",
"sys.path.append('functions/')\n",
"import functions_proximity"
],
"outputs": [],
......@@ -64,7 +60,9 @@
"source": [
"## 1. Load data\n",
"\n",
"The original dataset includes cells from multiple conditions (Normal, Alzheimer's, FTD, PSP). For this analysis, we are only interested in comparing Normal vs Alzheimer's, so these two conditions are filtered out."
"The original dataset includes cells from multiple conditions (Normal, Alzheimer's, FTD, PSP). For this analysis, we are only interested in comparing Normal vs Alzheimer's, so these two conditions are filtered out.\n",
"\n",
"These dataset comes from CellXGene platform, and its available at: https://cellxgene.cziscience.com/collections/c53573b2-eff4-4c5e-9ad0-b24d422dfd9b"
],
"id": "48bc73ea77a550de"
},
......@@ -433,9 +431,7 @@
"\n",
"Scanpy, when running sc.tl.rank_genes_groups(), automatically prioritises adata.raw if it exists. In this case, we saved the raw array in adata.raw to begin with (which is correct), but Scanpy is using that raw array instead of the normalised, log-transformed .X array.\n",
"\n",
"\n",
"\n",
"**sc.tl.rank_genes_groups te da los genes más diferencialmente expresados entre los grupos comparados. El signo de logfoldchanges indica si el gen está más expresado en el grupo de interés o en el grupo de referencia. Si comparas \"normal\" vs \"Alzheimer\", un gen sobreexpresado en normal tendrá un logFC positivo para \"normal\" y un logFC negativo para \"Alzheimer\". Lo mismo al revés. El set de genes DEGs es el mismo, solo cambia el signo según qué grupo tomes como referencia.**"
"Files **degs_{cell_type}_total.csv** stores all differentially expressed genes found per each cell type, identified by the ENSEMBL ID."
],
"metadata": {
"collapsed": false,
......@@ -605,7 +601,7 @@
"\n",
" print(f'{len(degs_disease_filtered)} DEGs found for {type} for Alzheimer disease')\n",
"\n",
" degs_disease_filtered.to_csv(f'CellXGene/cross-dementia/complete/data/degs_{type}_total.csv', index=False)"
" degs_disease_filtered.to_csv(f'../data/complete/degs_{type}_total.csv', index=False)"
],
"metadata": {
"collapsed": false,
......@@ -621,7 +617,9 @@
"\n",
"### 4.1. Gene-protein mapping\n",
"\n",
"In this case, scRNA-seq data uses ENSEMBL ID to identify genes, and our mapping file uses Entrez ID. First we perform a step in order to transform the identifiers using NCBI API.\n"
"In this case, scRNA-seq data uses ENSEMBL ID to identify genes, and our mapping file uses Entrez ID. First we perform a step in order to transform the identifiers using NCBI API.\n",
"\n",
"Files **degs_{cell_type}_mapped.csv** stores all the differentially expressed genes in each cell type, identified by Protein Accession Number, Gene Entrez ID, gene symbol, and ENSEMBL ID.\n"
],
"metadata": {
"collapsed": false,
......@@ -635,8 +633,8 @@
"execution_count": 148,
"outputs": [],
"source": [
"gen = pd.read_csv('CellXGene/gen.tsv', sep = '\\t')\n",
"gen_pro = pd.read_csv('CellXGene/gen_pro.tsv', sep = '\\t')"
"gen = pd.read_csv('../data/disnet/gen.tsv', sep = '\\t')\n",
"gen_pro = pd.read_csv('../data/disnet/gen_pro.tsv', sep = '\\t')"
],
"metadata": {
"collapsed": false,
......@@ -690,8 +688,6 @@
" \"\"\"\n",
" symbol_to_entrez = dict(zip(gen_file['gene_symbol'], gen_file['gene_id']))\n",
"\n",
" # Mapear los Gene Symbols a Entrez IDs\n",
" #entrez_ids = {ensembl_id: symbol_to_entrez.get(gene_symbol, None) for ensembl_id, gene_symbol in gene_symbols.items()}\n",
" entrez_ids = {}\n",
" for ensembl_id, gene_symbol in gene_symbols.items():\n",
" entrez_ids[ensembl_id] = (gene_symbol, symbol_to_entrez.get(gene_symbol, None))\n",
......@@ -705,33 +701,6 @@
}
}
},
{
"cell_type": "code",
"execution_count": 151,
"outputs": [
{
"data": {
"text/plain": "{'pericyte': AnnData object with n_obs × n_vars = 925 × 28215\n obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n obsm: 'X_pca', 'X_tsne', 'X_umap'}"
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_type = 'pericyte'\n",
"\n",
"# Obtén el valor correspondiente a esa key\n",
"data_for_sample_type = {sample_type: data_per_type[sample_type]}\n",
"data_for_sample_type"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 152,
......@@ -860,11 +829,9 @@
}
],
"source": [
"#for type, adata_type in data_per_type.items():\n",
"for type, adata_type in tqdm(data_for_sample_type.items(), desc=f'Mapping DEGs for {sample_type}...'):\n",
"\n",
"for type, adata_type in data_per_type.items():\n",
" # Load DEGs for the cellular type\n",
" degs_disease_filtered = pd.read_csv(f'CellXGene/cross-dementia/complete/data/degs_{type}_total.csv')\n",
" degs_disease_filtered = pd.read_csv(f'../data/complete/degs_{type}_total.csv')\n",
"\n",
" # Obtain ENSEMBL IDs from the DEGs\n",
" ensembl_ids_disease = degs_disease_filtered['names'].tolist()\n",
......@@ -884,7 +851,7 @@
" column_order = ['gene_id', 'gene_symbol', 'names', 'logfoldchanges', 'pvals', 'pvals_adj', 'scores']\n",
" degs_disease_filtered = degs_disease_filtered[column_order]\n",
"\n",
" degs_disease_filtered.to_csv(f'CellXGene/cross-dementia/complete/data/degs_{type}_mapped.csv', index=False)"
" degs_disease_filtered.to_csv(f'../data/complete/degs_{type}_mapped.csv', index=False)"
],
"metadata": {
"collapsed": false,
......@@ -893,6 +860,18 @@
}
}
},
{
"cell_type": "markdown",
"source": [
"#### Add Protein Accession Number"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 162,
......@@ -912,6 +891,21 @@
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"gene_to_protein = dict(zip(gen_pro['gene_id'], gen_pro['protein_id']))\n",
"keys = ['astrocyte', 'glutamatergic neuron', 'oligodendrocyte precursor cell', 'oligodendrocyte', 'inhibitory interneuron', 'microglial cell']"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 164,
......@@ -925,10 +919,6 @@
}
],
"source": [
"gene_to_protein = dict(zip(gen_pro['gene_id'], gen_pro['protein_id']))\n",
"# keys = ['astrocyte', 'glutamatergic neuron', 'oligodendrocyte precursor cell', 'oligodendrocyte', 'inhibitory interneuron', 'microglial cell']\n",
"keys = ['pericyte']\n",
"\n",
"for type in keys:\n",
" print(type)\n",
" mapped_file_path = f'CellXGene/cross-dementia/complete/data/degs_{type}_mapped.csv'\n",
......@@ -946,13 +936,9 @@
"source": [
"### 4.2. Cell type-specific PPI construction and integration of DEGs expression values\n",
"\n",
"Values for logfoldchange, p-value and p-value_adj for each differentially expressed gene is going to keep stored as metadata in each node protein of the network. In this networks, all DEGs detected in the differential expression analysis are included\n",
"\n",
"**Las PPI específicas de tipo celular y condición (sana/enferma) van a ser iguales en estructura (mismos nodos y edges) porque usas el mismo set de DEGs, solo que cambia el contexto de up/down-regulation. Se está filtrando el interactoma general (pro_pro.tsv) para quedarte con las interacciones entre DEGs.**\n",
"\n",
"Si los mismos genes aparecen como DEGs en ambas condiciones (solo cambia el signo del logFC), el set de proteínas mapeadas será el mismo.\n",
"Eso implica que la topología (estructura) de la red será igual entre sano y enfermo.\n",
"\n",
"Problema: No estás capturando cambios estructurales reales entre las redes de condiciones sanas y enfermas. Solo podrías distinguirlas después (en análisis como enrichment o modularidad) considerando el estado up/down de los nodos, pero no en la conectividad."
"**Important: there will be cases in which one gene encode more than one protein.**"
],
"metadata": {
"collapsed": false,
......@@ -966,25 +952,7 @@
"execution_count": 221,
"outputs": [],
"source": [
"gen_pro = pd.read_csv(\"CellXGene/gen_pro.tsv\", sep='\\t')\n",
"pro_pro = pd.read_csv('CellXGene/pro_pro.tsv', sep = '\\t')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 222,
"outputs": [],
"source": [
"sample_type = 'astrocyte'\n",
"\n",
"# Obtén el valor correspondiente a esa key\n",
"data_for_sample_type = {sample_type: data_per_type[sample_type]}"
"pro_pro = pd.read_csv(\"../data/disnet/pro_pro.tsv\", sep = '\\t')"
],
"metadata": {
"collapsed": false,
......@@ -1000,37 +968,22 @@
"source": [
"def build_ppi_with_expression(cell_type, gen_pro, pro_pro):\n",
" \"\"\"\n",
" Construye la red PPI específica de un tipo celular, filtrada por DEGs,\n",
" e integra los valores de expresión diferencial directamente en los nodos.\n",
"\n",
" Parámetros:\n",
" - cell_type: str, nombre del tipo celular.\n",
" - gen_pro: DataFrame, mapeo gene_id → protein_id.\n",
" - pro_pro: DataFrame, interacciones proteína-proteína.\n",
"\n",
" Output:\n",
" - G: Grafo NetworkX con expresión diferencial integrada.\n",
" Constructs the cell type-specific PPI network, filtered by DEGs, and integrates the differential expression values directly at the nodes.\n",
" \"\"\"\n",
" # Cargar los DEGs para este tipo celular\n",
" degs = pd.read_csv(f\"CellXGene/cross-dementia/complete/data/degs_{cell_type}_mapped.csv\")\n",
"\n",
" # Mapear genes DEGs a proteínas (usando Entrez IDs)\n",
" degs = degs.merge(gen_pro, on=\"gene_id\", how=\"left\")\n",
"\n",
" # Lista de proteínas DEGs\n",
" proteins_degs_disease = degs[\"protein_id\"].dropna().unique()\n",
"\n",
" # Filtrar la red PPI para solo conservar interacciones entre proteínas DEGs y eliminar self-interactions\n",
" # Filter the PPI network to retain only interactions between DEG proteins and remove self-interactions\n",
" ppi_filtered_disease = pro_pro[\n",
" (pro_pro[\"prA\"].isin(proteins_degs_disease)) &\n",
" (pro_pro[\"prB\"].isin(proteins_degs_disease)) &\n",
" (pro_pro[\"prA\"] != pro_pro[\"prB\"])\n",
" ]\n",
"\n",
" # Construir la red PPI\n",
" G = nx.from_pandas_edgelist(ppi_filtered_disease, \"prA\", \"prB\")\n",
"\n",
" # Agregar la expresión diferencial a los nodos de la red\n",
" # Adding the differential expression to the nodes of the network\n",
" for _, row in degs.iterrows():\n",
" if row[\"protein_id\"] in G:\n",
" G.nodes[row[\"protein_id\"]][\"gene_id\"] = row[\"gene_id\"]\n",
......@@ -1039,9 +992,7 @@
" G.nodes[row[\"protein_id\"]][\"pval\"] = row[\"pvals\"]\n",
" G.nodes[row[\"protein_id\"]][\"pval_adj\"] = row[\"pvals_adj\"]\n",
"\n",
" # Guardar la red en un único archivo con expresión integrada\n",
" output_path = f\"CellXGene/cross-dementia/complete/graphs/{cell_type}_network.graphml\"\n",
" nx.write_graphml(G, output_path)\n",
" nx.write_graphml(G, f\"../data/complete/graphs/{cell_type}_network.graphml\")\n",
"\n",
" return G"
],
......@@ -1066,7 +1017,7 @@
],
"source": [
"cell_networks = {\n",
" cell: build_ppi_with_expression(cell, gen_pro, pro_pro)\n",
" cell: build_ppi_with_expression(cell, pro_pro)\n",
" for cell in tqdm(data_per_type.keys(), desc=\"Building cell type-specific PPIs with expression values...\")\n",
"}"
],
......@@ -1109,9 +1060,9 @@
"execution_count": 10,
"outputs": [],
"source": [
"dis_gen = pd.read_csv('CellXGene/dis_gen.tsv', sep = '\\t')\n",
"gen_pro = pd.read_csv('CellXGene/gen_pro.tsv', sep = '\\t')\n",
"pro_pro = pd.read_csv('CellXGene/pro_pro.tsv', sep = '\\t')"
"dis_gen = pd.read_csv('../data/disnet/dis_gen.tsv', sep = '\\t')\n",
"gen_pro = pd.read_csv('../data/disnet/gen_pro.tsv', sep = '\\t')\n",
"pro_pro = pd.read_csv('../data/disnet/pro_pro.tsv', sep = '\\t')"
],
"metadata": {
"collapsed": false,
......@@ -1172,19 +1123,22 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 1,
"outputs": [
{
"data": {
"text/plain": "{'P52209',\n 'O43189',\n 'Q9NZQ7',\n 'Q9NZV6',\n 'P51843',\n 'Q16795',\n 'Q9HCE0',\n 'P35638',\n 'P04406',\n 'O15427',\n 'P03891',\n 'Q9ULV8',\n 'P35232',\n 'P00813',\n 'Q9UIK4',\n 'P63244',\n 'O75689',\n 'O15105',\n 'P51688',\n 'Q9BXY0',\n 'Q9BYF1',\n 'Q53EL6',\n 'P24387',\n 'P42768',\n 'P15529',\n 'P60228',\n 'Q9UHC9',\n 'Q9P2Y5',\n 'P12004',\n 'Q6ZW49',\n 'Q4V9L6',\n 'P49716',\n 'P04179',\n 'Q9Y2A7',\n 'Q13200',\n 'Q9NV58',\n 'O94813',\n 'Q05086',\n 'P12314',\n 'Q8N5C8',\n 'P08311',\n 'O95452',\n 'P78556',\n 'O15379',\n 'P25942',\n 'P46937',\n 'P16520',\n 'P60983',\n 'P02794',\n 'Q5SNT2',\n 'Q9UBW5',\n 'O14578',\n 'Q13133',\n 'Q92847',\n 'Q9GZX9',\n 'P26599',\n 'O15121',\n 'Q9Y5N1',\n 'Q9UMD9',\n 'P20138',\n 'Q15027',\n 'Q9NTU7',\n 'O75716',\n 'O14595',\n 'Q9H6U6',\n 'P22033',\n 'O14734',\n 'Q8IXL6',\n 'P48147',\n 'P46734',\n 'P35354',\n 'Q14573',\n 'P24863',\n 'P05112',\n 'Q9UKI9',\n 'P04062',\n 'P04439',\n 'Q9Y3D2',\n 'P23945',\n 'Q9Y4C0',\n 'P38936',\n 'P10145',\n 'Q8IYK4',\n 'Q86TM6',\n 'Q9NXA8',\n 'Q9Y6H1',\n 'Q02246',\n 'Q9NZC7',\n 'P63220',\n 'Q9UNU6',\n 'Q9UJY1',\n 'Q9Y485',\n 'P15502',\n 'O94907',\n 'Q9C0B1',\n 'O00499',\n 'Q9H190',\n 'Q9NP56',\n 'P68400',\n 'O75914',\n 'P42356',\n 'P33681',\n 'P12644',\n 'Q96DT6',\n 'P01860',\n 'Q07820',\n 'P60484',\n 'Q8WXG6',\n 'O43184',\n 'P84103',\n 'Q16548',\n 'Q9BYT8',\n 'P03905',\n 'O00418',\n 'Q6IN85',\n 'P39880',\n 'Q8TDQ7',\n 'O60895',\n 'Q7Z698',\n 'P10071',\n 'P01008',\n 'Q8IVL1',\n 'O75475',\n 'Q9UQB3',\n 'Q9Y6C9',\n 'P36955',\n 'Q92575',\n 'Q86UG4',\n 'Q9ULV1',\n 'P11712',\n 'Q96EY1',\n 'Q99700',\n 'Q9Y2C9',\n 'O15287',\n 'P49281',\n 'P22307',\n 'P51587',\n 'Q13393',\n 'P19429',\n 'P68036',\n 'Q13488',\n 'O43474',\n 'P49768',\n 'Q92570',\n 'Q8NA29',\n 'Q5EBL2',\n 'Q13619',\n 'Q92731',\n 'Q92993',\n 'P00749',\n 'O14874',\n 'Q9UNQ0',\n 'P00747',\n 'Q15276',\n 'P19875',\n 'P54253',\n 'P03886',\n 'P08294',\n 'Q9UMY4',\n 'P19419',\n 'Q9P2B7',\n 'Q96I99',\n 'O60493',\n 'Q5TCY1',\n 'P49675',\n 'Q9BYI3',\n 'Q05655',\n 'Q9NR12',\n 'P19367',\n 'Q8WTV0',\n 'Q8WWX9',\n 'P55786',\n 'Q86VK4',\n 'O14773',\n 'Q13867',\n 'Q9UEE9',\n 'Q9H2A3',\n 'P41182',\n 'P29372',\n 'A6NDG6',\n 'Q12774',\n 'Q6IQ55',\n 'Q9NQ75',\n 'P42892',\n 'P21709',\n 'Q01130',\n 'Q8NF50',\n 'P08172',\n 'P01185',\n 'Q9HC10',\n 'P15498',\n 'Q9UIF7',\n 'P19652',\n 'Q8NDV7',\n 'P04141',\n 'Q96B96',\n 'P56159',\n 'O60814',\n 'P15391',\n 'P34810',\n 'Q92673',\n 'Q9Y5X9',\n 'Q6UWF3',\n 'O14746',\n 'Q9UIC8',\n 'P28222',\n 'Q08722',\n 'Q9UPY5',\n 'Q6XR72',\n 'Q96QV1',\n 'O00148',\n 'Q16775',\n 'O75473',\n 'Q92542',\n 'Q5VWX1',\n 'Q9Y520',\n 'P46089',\n 'Q13510',\n 'Q92915',\n 'P78504',\n 'Q96I59',\n 'Q15762',\n 'O15540',\n 'Q14190',\n 'P45983',\n 'P60953',\n 'O96020',\n 'Q96KG7',\n 'Q9NRW4',\n 'P55317',\n 'P31930',\n 'P27797',\n 'P62760',\n 'P49760',\n 'Q15818',\n 'Q30154',\n 'P14410',\n 'O14756',\n 'P11226',\n 'P04626',\n 'Q6H8Q1',\n 'Q9Y6X6',\n 'O43464',\n 'Q8N998',\n 'O14744',\n 'Q99720',\n 'Q99259',\n 'P15153',\n 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'P78417',\n 'Q6ZVD7',\n 'P56270',\n 'Q9NP64',\n 'Q9UQ90',\n ...}"
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
"ename": "NameError",
"evalue": "name 'lcc_alz' is not defined",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)",
"\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_1308\\1865455995.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlcc_alz\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
"\u001B[1;31mNameError\u001B[0m: name 'lcc_alz' is not defined"
]
}
],
"source": [
"lcc_alz"
"print(len(lcc_alz))"
],
"metadata": {
"collapsed": false,
......@@ -1198,7 +1152,11 @@
"source": [
"### 5.2. Alzheimer module proteins filtering\n",
"\n",
"Keep only those proteins which are present in the Alzheimer disease module for subsequent analysis.\n"
"Next step consists on filtering the previously built cell-type-specific network by keeping only those nodes which belong to the general AD module.\n",
"\n",
"**{cell_type}_network-graphml** file store the PPI subgraphs of each cell type built only with those genes beloging to the AD module.\n",
"\n",
"**degs{cell_type}_mapped_filt.csv** files store all the data from the proteins and their corresponding DEGs which are present in the AD module."
],
"metadata": {
"collapsed": false,
......@@ -1214,18 +1172,18 @@
"source": [
"for cell_type in tqdm(cell_types, desc='Processing cell types...'):\n",
"\n",
" # Cargar red PPI específica para este tipo celular\n",
" G = nx.read_graphml(f'CellXGene/cross-dementia/complete/graphs/{cell_type}_network.graphml')\n",
" G = nx.read_graphml(f\"../data/complete/graphs/{cell_type}_network.graphml\")\n",
" ppi_proteins = set(G.nodes())\n",
"\n",
" # Proteínas del módulo de Alzheimer presentes en la PPI de este tipo celular\n",
" # Alzheimer's module proteins present in the PPI of this cell type.\n",
" alz_in_ppi = lcc_alz.intersection(ppi_proteins)\n",
"\n",
" # Built the filtered subgraph with only the proteins resulting form the intersection\n",
" G_alz = G.subgraph(alz_in_ppi).copy()\n",
" nx.write_graphml(G_alz, f'CellXGene/cross-dementia/filtered/graphs/{cell_type}_network.graphml')\n",
" nx.write_graphml(G_alz, f\"../data/filtered/graphs/{cell_type}_network.graphml\")\n",
"\n",
" # Archivo filtrado de mapped genes\n",
" df_complete = pd.read_csv(f'CellXGene/cross-dementia/complete/data/degs_{cell_type}_mapped.csv')\n",
" # Extract all the data for these genes of the intersection in a separate file\n",
" df_complete = pd.read_csv(f\"../data/complete/data/degs_{cell_type}_mapped.csv\")\n",
"\n",
" alz_data = [{\n",
" \"protein_id\": protein,\n",
......@@ -1240,30 +1198,7 @@
" df_filtered = df_filtered.drop(columns=[\"gene_symbol_x\"], errors='ignore')\n",
" df_filtered = df_filtered.rename(columns={\"gene_symbol_y\": \"gene_symbol\"})\n",
"\n",
" df_filtered.to_csv(f'CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv', index=False)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 359,
"outputs": [
{
"data": {
"text/plain": "2697"
},
"execution_count": 359,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(lcc_alz)"
" df_filtered.to_csv(f\"../data/filtered/data/degs_{cell_type}_mapped_filt.csv\", index=False)"
],
"metadata": {
"collapsed": false,
......@@ -1362,10 +1297,6 @@
"def group_nodes_by_bins(graph, log_bins):\n",
" \"\"\"\n",
" This function groups nodes of a graph in logarithmic bins based on its degree.\n",
"\n",
" Input:\n",
" 1.graph\n",
" 2. log_bins: logarithmic bins\n",
" \"\"\"\n",
" nodes_bins = {}\n",
" for node, degree in graph.degree():\n",
......@@ -1404,9 +1335,6 @@
"\n",
" if available_nodes:\n",
" random_node = np.random.choice(available_nodes) #choose randomly a node from available nodes\n",
" # if len(available_nodes) == 1 and random_node == prot:\n",
" # pass\n",
" # else:\n",
" while random_node == prot:\n",
" random_node = np.random.choice(available_nodes)\n",
"\n",
......@@ -1415,7 +1343,6 @@
" else:\n",
" iteration_results.append(None)\n",
"\n",
" # results.append(results)\n",
" if any(iteration_results):\n",
" results.append(iteration_results)\n",
"\n",
......@@ -1565,28 +1492,28 @@
"\n",
"for cell_type in tqdm(cell_types, desc=\"Processing cell types...\"):\n",
"\n",
" # Solapamiento DEGs en módulo de Alzheimer: número de proteínas DEGs que están en el módulo de Alzheimer.\n",
" degs_cell_type = pd.read_csv(f'CellXGene/cross-dementia/complete/data/degs_{cell_type}_mapped.csv')\n",
" # Overlapping DEGs in Alzheimer's module: number of DEG proteins that are in the Alzheimer's module.\n",
" degs_cell_type = pd.read_csv(f'../data/complete/data/degs_{cell_type}_mapped.csv')\n",
" degs_total = degs_cell_type.dropna(subset=['gene_id'])['gene_id']\n",
" degs_protein = degs_cell_type.dropna(subset=['protein_id'])['protein_id']\n",
"\n",
" G = nx.read_graphml(f'CellXGene/cross-dementia/complete/graphs/{cell_type}_network.graphml')\n",
" G = nx.read_graphml(f'../data/complete/graphs/{cell_type}_network.graphml')\n",
" ppi_proteins = set(G.nodes())\n",
"\n",
" # Proteínas del módulo de Alzheimer presentes en la PPI de este tipo celular\n",
" # Alzheimer's module proteins present in the PPI of this cell type.\n",
" alz_in_ppi = lcc_alz.intersection(ppi_proteins)\n",
"\n",
" # Calcular el LCC de la red de enfermedad para el tipo celular. Para ello utilizamos los DEGs que se encuentran en el LCC general de la enfermedad\n",
" degs_cell_type_filt = pd.read_csv(f'CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv')\n",
" # Calculate the LCC of the disease network for the cell type. For this we use the DEGs found in the overall disease LCC.\n",
" degs_cell_type_filt = pd.read_csv(f'../data/filtered/data/degs_{cell_type}_mapped_filt.csv')\n",
" lcc_cell_type = calculate_lcc_for_cell_type(degs_cell_type_filt['gene_id'], gen_pro, pro_pro, G_ppi)\n",
"\n",
" # Generar 1,000 redes aleatorias\n",
" # Generate 1,000 random networks\n",
" random_networks = random_subset_generator(lcc_cell_type, G_ppi, 1000)\n",
"\n",
" # Calcular el tamaño del LCC para cada red aleatoria\n",
" # Calculate the size of the LCC for each random network\n",
" random_lcc_sizes = [len(calculate_lcc_from_prots(network, pro_pro, G_ppi)) for network in random_networks]\n",
"\n",
" # Calcular estadísticas de los módulos aleatorios\n",
" # Calculate statistics of random modules\n",
" random_lcc_mean = np.mean(random_lcc_sizes)\n",
" random_lcc_std = np.std(random_lcc_sizes)\n",
"\n",
......@@ -1598,13 +1525,11 @@
" plt.title(f\"LCC size distribution in random networks - {cell_type}\")\n",
" plt.legend(loc = 'best')\n",
" plt.tight_layout()\n",
" plt.savefig(f'CellXGene/cross-dementia/plots/significance/random_modules_{cell_type}.svg', format='svg', dpi=1200)\n",
" plt.show()\n",
" plt.savefig(f'../figures/significance/random_modules_{cell_type}.svg', format='svg', dpi=1200)\n",
" # plt.show()\n",
"\n",
" # Agregar la fila correspondiente\n",
" table.append([cell_type, len(degs_total), len(degs_protein), len(alz_in_ppi), len(lcc_cell_type), random_lcc_mean, random_lcc_std])\n",
"\n",
"# Crear el DataFrame de la tabla\n",
"table_df = pd.DataFrame(table, columns=[\"Cell Type\", \"Total DEGs\", \"DEGs mapped to protein\", \"Cell Type proteins in main LCC\", \"Cell Type LCC size\", \"LCC mean\", \"LCC std\"])"
],
"metadata": {
data/complete/degs_T cell_total.csv 0 → 100644
View file @ cd9227e2
names,scores,logfoldchanges,pvals,pvals_adj
ENSG00000198846,4.811137,1.7838787,1.5007386566395886e-06,0.04234334119708599
data/complete/degs_astrocyte_total.csv 0 → 100644
View file @ cd9227e2
This source diff could not be displayed because it is too large. You can view the blob instead.
data/complete/degs_endothelial cell of vascular tree_total.csv 0 → 100644
View file @ cd9227e2
names,scores,logfoldchanges,pvals,pvals_adj
ENSG00000204389,16.825916,2.4445856,1.576048729127379e-63,4.4468214892329e-59
ENSG00000106211,16.579802,2.3521888,9.755175041694643e-62,1.3762113190070718e-57
ENSG00000080824,13.622513,1.269916,2.9426643130860597e-42,2.7675757864574393e-38
ENSG00000204388,12.839762,2.8006985,9.817635058555169e-38,6.9251143294283525e-34
ENSG00000186352,12.149157,2.2913055,5.7960199919004915e-34,3.2706940814294476e-30
ENSG00000196950,12.073232,1.2146878,1.4627411025709465e-33,6.878540034839876e-30
ENSG00000120694,12.02845,2.1945572,2.518457729204452e-33,1.0151183547071946e-29
ENSG00000065054,9.535691,1.1629804,1.4888966118319355e-21,4.2009217902838064e-18
ENSG00000258667,9.449484,1.2962058,3.405045852665826e-21,8.733942612087845e-18
ENSG00000109906,8.852285,0.9783372,8.574542868857419e-19,2.0160893920401005e-15
ENSG00000073849,8.806369,0.9701311,1.2926482233206135e-18,2.8055438169993162e-15
ENSG00000040341,8.679387,1.0377384,3.9790681800486756e-18,8.01924347857667e-15
ENSG00000148488,8.169529,0.9246422,3.0959633065007277e-16,5.459537793307377e-13
ENSG00000109971,8.140065,1.2835685,3.9506430608342765e-16,6.556905527143477e-13
ENSG00000086061,8.070164,1.1777052,7.020411386739461e-16,1.1004494848714106e-12
ENSG00000128294,7.7468853,1.0768578,9.417404277392481e-15,1.3285603084331443e-11
ENSG00000090060,7.700025,1.0141777,1.3603957287803012e-14,1.7808877709510822e-11
ENSG00000134294,7.697402,0.901904,1.3886064490846643e-14,1.7808877709510822e-11
ENSG00000110958,7.6734996,1.0884552,1.6736600349882587e-14,2.0531442559649443e-11
ENSG00000120129,7.5820246,0.7886909,3.402032513748326e-14,3.999514473975376e-11
ENSG00000128731,7.497748,0.68953896,6.492361526719836e-14,7.045460787553852e-11
ENSG00000132002,7.468956,1.9478366,8.083350849637728e-14,8.447101637871425e-11
ENSG00000105854,7.3613434,0.698899,1.820683134464224e-13,1.834663379961003e-10
ENSG00000064042,7.088535,0.730251,1.3553923176795023e-12,1.318703249769902e-09
ENSG00000110172,6.9788446,1.6922036,2.9761703633908126e-12,2.658975605932487e-09
ENSG00000163762,6.9745007,1.1982391,3.0695870836425474e-12,2.658975605932487e-09
ENSG00000185650,6.9692965,0.83010936,3.1852977527020473e-12,2.658975605932487e-09
ENSG00000105993,6.9680376,0.8854717,3.213928584481676e-12,2.658975605932487e-09
ENSG00000116161,6.964386,2.0156648,3.298392564509553e-12,2.658975605932487e-09
ENSG00000138772,6.9162035,0.8347701,4.639071353182147e-12,3.5376053575684937e-09
ENSG00000096384,6.8795633,0.7063304,6.003626598559218e-12,4.457692749430219e-09
ENSG00000168209,6.851653,1.310385,7.300146644259293e-12,5.2813753222506654e-09
ENSG00000101187,6.8109627,0.6985206,9.694788312834605e-12,6.671669566990936e-09
ENSG00000124440,6.763683,0.8826399,1.3452755271715897e-11,9.037368809320572e-09
ENSG00000004478,6.6613584,1.8447479,2.7130803562768594e-11,1.7011013833855907e-08
ENSG00000196104,6.657497,0.93023896,2.7852979836139173e-11,1.708417013210145e-08
ENSG00000179104,6.4632363,0.87683177,1.0248700075035289e-10,6.024314012856681e-08
ENSG00000055813,6.4498687,0.8229831,1.1194705008992433e-10,6.446093914871867e-08
ENSG00000130303,6.421413,0.8273381,1.3501542060694875e-10,7.618920184850117e-08
ENSG00000144445,6.3147864,0.965733,2.705347094990968e-10,1.4966934957876502e-07
ENSG00000165810,6.3031187,0.7728336,2.9171535100882365e-10,1.5828362747526845e-07
ENSG00000109846,6.260246,1.4574996,3.843712849120227e-10,2.0462331705269284e-07
ENSG00000122884,6.2220526,1.0417746,4.906926873147863e-10,2.5638692912197583e-07
ENSG00000109458,6.18239,0.89539665,6.313816856577873e-10,3.125339344006047e-07
ENSG00000157107,6.078933,0.71860665,1.209849731525254e-09,5.885501754307766e-07
ENSG00000158825,6.017425,1.4935253,1.7721334146178481e-09,8.474702422617387e-07
ENSG00000114416,5.985884,0.9775797,2.1521751950528905e-09,1.0120603854736216e-06
ENSG00000177879,5.944795,0.68246096,2.7680310724879153e-09,1.280327814922074e-06
ENSG00000108219,5.9104,0.71726483,3.412778043488893e-09,1.5284370237625257e-06
ENSG00000119522,5.843457,0.6070564,5.112849055418952e-09,2.219369786133011e-06
ENSG00000102760,5.8371196,0.70875883,5.311101178869054e-09,2.2704957539665203e-06
ENSG00000092531,5.776493,1.1157777,7.62736767167811e-09,3.1965130218635254e-06
ENSG00000112335,5.774814,0.8747631,7.703805971530028e-09,3.1965130218635254e-06
ENSG00000168439,5.750471,1.4755228,8.899504081812222e-09,3.639123299541041e-06
ENSG00000072736,5.696392,0.81817,1.2236922522649622e-08,4.932353842522273e-06
ENSG00000114166,5.686718,1.2426151,1.2950407625098874e-08,5.084299563145958e-06
ENSG00000110841,5.6864033,0.48361278,1.2974289156353322e-08,5.084299563145958e-06
ENSG00000137831,5.6595,0.6353115,1.5181461377559245e-08,5.8677388050388235e-06
ENSG00000116106,5.6517143,0.68536216,1.588552879767913e-08,6.05689452738536e-06
ENSG00000115170,5.561016,0.76350725,2.6820844039822346e-08,9.918148844190546e-06
ENSG00000120071,5.511176,0.57327557,3.564439588321449e-08,1.263896631841255e-05
ENSG00000051108,5.5106726,0.60787183,3.574654684886739e-08,1.263896631841255e-05
ENSG00000153048,5.5046916,0.7996309,3.6981505714751876e-08,1.2881891157305236e-05
ENSG00000158201,5.489792,1.6847259,4.024072171651783e-08,1.380839987902878e-05
ENSG00000108100,5.4881344,0.5054299,4.0620137868488e-08,1.380839987902878e-05
ENSG00000198668,5.3955894,0.56518525,6.829908165889458e-08,2.2941173678639412e-05
ENSG00000159335,5.3904476,1.1373512,7.028235882990508e-08,2.3329608875126726e-05
ENSG00000127483,5.3737435,0.6669579,7.711856693533839e-08,2.5250261187151582e-05
ENSG00000177311,5.357753,0.5880543,8.426348900275939e-08,2.701698116150973e-05
ENSG00000157514,5.3364315,0.8531887,9.479360531537752e-08,3.0051703078352547e-05
ENSG00000112576,5.3302617,0.723589,9.807121609060338e-08,3.074532624440416e-05
ENSG00000053254,5.302687,0.58842254,1.1411030888774237e-07,3.5380465552391766e-05
ENSG00000144381,5.2962656,1.0832922,1.1819477592842111e-07,3.624853916109132e-05
ENSG00000166295,5.2639065,0.8040545,1.4102621668611048e-07,4.2504751797983844e-05
ENSG00000145555,5.263151,0.6503947,1.4160718302358606e-07,4.2504751797983844e-05
ENSG00000167797,5.2370872,1.4763783,1.631305645375552e-07,4.7945092483615834e-05
ENSG00000128849,5.228861,0.57085323,1.705574922346687e-07,4.961113034434203e-05
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ENSG00000162599,5.1564407,0.5353712,2.51687963931671e-07,7.058683863333302e-05
ENSG00000152601,5.1557064,0.4794592,2.526766153452644e-07,7.058683863333302e-05
ENSG00000117318,5.1523905,0.55833423,2.5718655622074096e-07,7.11423400369432e-05
ENSG00000198722,5.064756,0.6272604,4.0892349794284536e-07,0.00010988358566149888
ENSG00000123636,5.0202675,0.524279,5.159961788229626e-07,0.00013480400171749898
ENSG00000185291,5.005997,1.113968,5.557352539000427e-07,0.00014385385494302482
ENSG00000189079,4.996638,0.66375107,5.833846242874153e-07,0.00014963815612972203
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ENSG00000122778,4.927974,0.8420002,8.308664760658021e-07,0.00020547730526172274
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ENSG00000115756,4.8434663,0.8872333,1.275933589866958e-06,0.0003020073984209328
ENSG00000104067,4.842144,0.49666807,1.2844546450651051e-06,0.0003020073984209328
ENSG00000178764,4.8331623,0.722384,1.3438097797476473e-06,0.000313352007732065
ENSG00000203709,4.8109603,0.75543875,1.5020699296588011e-06,0.0003445601875229518
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ENSG00000148498,4.8013277,0.5368612,1.5761695881190673e-06,0.0003532824952216536
ENSG00000139514,4.801139,0.49938244,1.5776570759499682e-06,0.0003532824952216536
ENSG00000100380,4.7950115,0.88912725,1.626656620317527e-06,0.000360030186030607
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ENSG00000157827,4.7775936,0.5601592,1.7740555295647938e-06,0.0003850382828205435
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ENSG00000140396,4.6049266,0.5655906,4.126113588853815e-06,0.000786610100739935
ENSG00000105939,4.6001,0.76270187,4.222881931056682e-06,0.000794324091231762
ENSG00000123243,4.590447,0.6388985,4.422982727942776e-06,0.0008156500501235649
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ENSG00000171862,4.5651593,0.50814,4.991147273795052e-06,0.0008912988628489076
ENSG00000153944,4.544237,0.47462305,5.513455436196432e-06,0.0009722634070767647
ENSG00000083642,4.5326953,0.5399187,5.823580173500271e-06,0.001020573382579566
ENSG00000111077,4.504407,0.66540366,6.655850705134314e-06,0.0011381504705779678
ENSG00000080503,4.461597,0.49471265,8.135106212408942e-06,0.0013827230227898691
ENSG00000136111,4.4252715,0.43259233,9.632102222025277e-06,0.0016176771678240664
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ENSG00000132549,4.418787,0.49256817,9.925634390468133e-06,0.001647363378394461
ENSG00000104365,4.4153037,0.7100136,1.008683306301303e-05,0.0016616038119268576
ENSG00000127922,4.4141493,0.6165204,1.0140791525705017e-05,0.0016616038119268576
ENSG00000079950,4.413142,0.76500136,1.018810772508759e-05,0.0016616038119268576
ENSG00000147316,4.40884,0.59267706,1.0392570974037285e-05,0.0016852091381175977
ENSG00000185305,4.3982215,0.45187223,1.0914152949943715e-05,0.0017527222168222424
ENSG00000120802,4.397844,0.946054,1.0933160026961356e-05,0.0017527222168222424
ENSG00000158195,4.390814,0.50213206,1.129272352083786e-05,0.001800136690058984
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ENSG00000150753,4.354845,0.99748135,1.3316111649443877e-05,0.00209896139770424
ENSG00000146376,4.348696,0.91211486,1.3694920729937478e-05,0.0021466788244176994
ENSG00000133639,4.336735,0.67301464,1.4461500859263819e-05,0.002254316280354302
ENSG00000228696,4.3311734,0.99501324,1.4831669556099894e-05,0.002299316244644827
ENSG00000168477,4.3156233,0.7768566,1.591530490194424e-05,0.002440490912001939
ENSG00000175115,4.306117,0.60633004,1.6614508647801627e-05,0.0025339370891768807
ENSG00000099715,4.294848,1.3373245,1.7481302041609847e-05,0.0026455289837167173
ENSG00000120910,4.29233,0.4963462,1.7680804920209658e-05,0.0026455289837167173
ENSG00000221869,4.2916164,0.5255881,1.7737723952519413e-05,0.0026455289837167173
ENSG00000168264,4.260768,0.9768357,2.0372566353156478e-05,0.002966633894336369
ENSG00000163320,4.259803,0.8222251,2.046074915655516e-05,0.002966633894336369
ENSG00000187244,4.259782,0.98403025,2.0462670204352378e-05,0.002966633894336369
ENSG00000074054,4.25464,0.51410544,2.0938537886266118e-05,0.0030141879921479517
ENSG00000103257,4.248429,0.43621567,2.152752228741109e-05,0.00306767192595608
ENSG00000137819,4.243455,0.8418353,2.201044271018023e-05,0.003120726839536358
ENSG00000171552,4.229542,0.5635817,2.3416770692644415e-05,0.003293530316690567
ENSG00000171843,4.227611,0.6141529,2.361854439039666e-05,0.0032989961879952563
ENSG00000162734,4.2186294,0.6969797,2.457917726005569e-05,0.003402048806013288
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ENSG00000157554,-4.5865016,-0.27632755,4.50735111850672e-06,0.0008204833019914006
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data/complete/degs_glutamatergic neuron_total.csv 0 → 100644
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data/complete/degs_inhibitory interneuron_total.csv 0 → 100644
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data/complete/degs_microglial cell_total.csv 0 → 100644
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data/complete/degs_oligodendrocyte precursor cell_total.csv 0 → 100644
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data/complete/degs_oligodendrocyte_total.csv 0 → 100644
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data/complete/degs_pericyte_total.csv 0 → 100644
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names,scores,logfoldchanges,pvals,pvals_adj
ENSG00000106211,10.203879,1.8847187,1.905079425515209e-24,5.375181599091162e-20
ENSG00000204389,8.499396,2.4507992,1.9057843538792078e-17,1.3442926386175463e-13
ENSG00000169282,7.6771245,1.7665759,1.6269912319993884e-14,7.650926268477125e-11
ENSG00000080824,7.3055577,1.1101086,2.7611934434650644e-13,1.1129581858195256e-09
ENSG00000249307,6.3864245,2.4419992,1.6980927901100702e-10,4.791168807295563e-07
ENSG00000188783,6.2328253,1.1246282,4.5809686471760224e-10,1.0993314842938347e-06
ENSG00000109846,6.1314106,1.5257127,8.710331282157684e-10,1.7554428366148505e-06
ENSG00000125868,6.012273,1.1278157,1.8294016520001416e-09,3.441104507412266e-06
ENSG00000026025,5.7661204,1.1177812,8.111714376110418e-09,1.4304501320122217e-05
ENSG00000109906,5.65449,0.97660965,1.5630948959328988e-08,2.5942777934556905e-05
ENSG00000197956,5.471353,1.3109308,4.466131319079335e-08,7.000660842656857e-05
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ENSG00000177469,5.132155,1.475945,2.864437535018991e-07,0.00035139176108939485
ENSG00000164188,5.0788627,1.8963917,3.797007747927411e-07,0.0004463857233657163
ENSG00000049323,5.0055094,1.0109398,5.571449857288402e-07,0.0006287938308935691
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ENSG00000137766,4.630373,1.9908507,3.650074265484499e-06,0.00294248129716129
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ENSG00000172216,4.436405,1.105564,9.147367736274876e-06,0.005491340014446716
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ENSG00000184347,4.397144,0.9965857,1.0968473774122214e-05,0.0062211624300885105
ENSG00000065882,4.396036,0.81891215,1.1024565709885718e-05,0.0062211624300885105
ENSG00000142279,4.3612056,1.5026994,1.2934784821474805e-05,0.0070970556189187016
ENSG00000120694,4.353698,1.6761034,1.3386021284377312e-05,0.0070970556189187016
ENSG00000137941,4.352713,0.8056885,1.3446301922327701e-05,0.0070970556189187016
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ENSG00000077943,4.331298,1.4129341,1.482329573872526e-05,0.00746855873693095
ENSG00000118689,4.312098,0.8544231,1.617127040075925e-05,0.007893088224653035
ENSG00000162599,4.285883,0.6353812,1.820149370055006e-05,0.008704324487474914
ENSG00000119950,4.2803445,0.93443614,1.8660434560560504e-05,0.008775069352103577
ENSG00000107186,4.2404675,0.89345884,2.230545580159591e-05,0.010317187466262763
ENSG00000164684,4.2141294,1.3715477,2.5074354256207306e-05,0.011054264145920143
ENSG00000185046,4.1964064,0.9782406,2.7118346891093846e-05,0.01159309329594262
ENSG00000079691,4.1900063,1.2644056,2.7894655155413113e-05,0.011746980525522104
ENSG00000153048,4.1727757,2.155557,3.0091106379423022e-05,0.012485596566109125
ENSG00000110958,4.1628065,0.8664793,3.1435963398396926e-05,0.012854575467909699
ENSG00000124788,4.1383142,0.712304,3.498666958075107e-05,0.014102126888869878
ENSG00000187955,4.1324067,1.4476955,3.5898440231553435e-05,0.01421156062314216
ENSG00000162998,4.1300683,1.2927861,3.626554544980455e-05,0.01421156062314216
ENSG00000142798,4.124653,1.4249504,3.7129416901106386e-05,0.014350773943352284
ENSG00000124440,4.1175146,1.8042517,3.829803704592784e-05,0.014602420476362891
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ENSG00000122367,4.0578227,1.5193521,4.9532373604588394e-05,0.018150076899395604
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ENSG00000156113,3.9869306,0.960263,6.693356973804249e-05,0.02390545152099834
ENSG00000243927,3.9823768,1.7596824,6.822947604545502e-05,0.02406368333278142
ENSG00000248079,3.9628077,2.2015052,7.407342999545918e-05,0.025802244781751617
ENSG00000022267,3.9251463,1.1099092,8.667699643796073e-05,0.029824286030451976
ENSG00000154380,3.916285,0.9760763,8.992395843480865e-05,0.030568728761905133
ENSG00000101605,3.8772697,1.4294349,0.00010563520327554851,0.03386928705022274
ENSG00000221869,3.8473623,0.70893383,0.00011939636024008529,0.03743075893526674
ENSG00000106624,3.8311162,0.957455,0.00012756323615662705,0.03884523852069285
ENSG00000131171,3.8300085,0.89910865,0.00012813882232022992,0.03884523852069285
ENSG00000145687,3.8257008,0.63722783,0.00013040056419279987,0.03884523852069285
ENSG00000182247,3.8249624,0.69574136,0.0001307920488912217,0.03884523852069285
ENSG00000112137,3.8005934,0.8537658,0.00014435005028238797,0.04242538196580809
ENSG00000198840,3.7976394,0.38057047,0.0001460805815010719,0.042491377392296324
ENSG00000178568,3.7917318,1.1079869,0.00014960041364138994,0.04292680102551837
ENSG00000130176,3.7892704,1.2630347,0.00015109044759217935,0.04292680102551837
ENSG00000131374,3.787547,0.71140087,0.00015214177219747784,0.04292680102551837
ENSG00000182809,3.772778,1.1283969,0.00016143987203558614,0.04390651825756212
ENSG00000133112,3.7721627,0.80236536,0.00016183866378828498,0.04390651825756212
ENSG00000067560,3.7688396,0.7855196,0.00016400820514150236,0.04407134769588085
ENSG00000188846,3.7535782,1.1112844,0.00017432811747928283,0.04640252674224495
ENSG00000104490,3.7442243,1.0537264,0.00018095187522841415,0.04746371041049145
ENSG00000128245,3.7412705,1.3005087,0.00018309227274577964,0.04746371041049145
ENSG00000120071,3.7346244,0.63409,0.0001879955043219125,0.04822084685857056
ENSG00000124942,3.7251475,0.9890392,0.00019520098001814902,0.04917496117153638
ENSG00000129116,3.720963,0.8585271,0.00019846451817885882,0.04955465823377435
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ENSG00000179603,-4.511851,-0.92918116,6.426441486826547e-06,0.00421679178025142
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ENSG00000079215,-4.5838504,-0.80862254,4.56491123630456e-06,0.0034810532576306256
ENSG00000188313,-4.604404,-1.5170913,4.136487309516697e-06,0.003241971928833711
ENSG00000073417,-4.6407113,-0.6338663,3.4721159230980276e-06,0.002881345610888554
ENSG00000099622,-4.822003,-1.4958272,1.421239807941426e-06,0.0014321528993238334
ENSG00000116016,-5.228154,-0.75209385,1.712106516774166e-07,0.00021957766077628678
ENSG00000183570,-5.3710456,-0.9864346,7.82813565233756e-08,0.00011624781443721277
ENSG00000183826,-6.1469183,-0.80622196,7.900287597958676e-10,1.714666265972339e-06
ENSG00000062716,-6.229625,-1.1125556,4.675519337772821e-10,1.0993314842938347e-06
ENSG00000171862,-6.821376,-0.7821016,9.017273639072149e-12,2.8269152858491187e-08
ENSG00000118473,-6.9567595,-0.6023907,3.481883869715881e-12,1.2280169173004197e-08
ENSG00000103196,-7.81657,-1.6378525,5.428214743150694e-15,3.0631415795599365e-11
ENSG00000163638,-8.76278,-1.6780226,1.9049343316019406e-18,1.7915907388716253e-14
ENSG00000154217,-9.620129,-1.588356,6.574882752792545e-22,9.275515843502083e-18
data/complete/graphs/T cell_network.graphml 0 → 100644
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data/complete/graphs/astrocyte_network.graphml 0 → 100644
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data/complete/graphs/endothelial cell of vascular tree_network.graphml 0 → 100644
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data/complete/graphs/inhibitory interneuron_network.graphml 0 → 100644
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data/complete/graphs/oligodendrocyte_network.graphml 0 → 100644
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data/complete/graphs/pericyte_network.graphml 0 → 100644
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