/
SRT Gene Expression.py
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SRT Gene Expression.py
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# -*- coding: utf-8 -*-
"""
Created on Sept 07 2021
@author: BIONICS_LAB
@company: DZNE
"""
import matplotlib.pyplot as plt
from anndata import AnnData
from sklearn.decomposition import PCA
import scanpy as sc
from scipy import stats
import h5py
import numpy as np
import pandas as pd
import json
import os
import seaborn as sns
import scipy.sparse as sp_sparse
import matplotlib.image as mpimg
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.axes._axes import _log as matplotlib_axes_logger
matplotlib_axes_logger.setLevel('ERROR')
"""
The following input parameters are used for specific gene list and select gene plotting.
To compare conditions put the path for datasets in the input parameters and label condition name i.e. SD and ENR and assign desired color
"""
rows = 64
cols = 64
column_list = ["IEGs"] ### "Hippo Signaling Pathway","Synaptic Vescicles_Adhesion","Receptors and channels","Synaptic plasticity","Hippocampal Neurogenesis","IEGs"
select_genes = ['Arc', 'Bdnf', 'Egr1', 'Egr3', 'Egr4', 'Fosb'] ### >5 genes needed
conditions = ['SD', 'ENR']
condition1_path = r'Z:/ANALYSES/SPATIOSCALES- 10X genomics/Data/SD/'
condition2_path = r'Z:/ANALYSES/SPATIOSCALES- 10X genomics/Data/ENR/'
color = ['silver', 'dodgerblue'] # color for pooled plotting of conditions
class MEASeqX_Project:
def __init__(self, srcfilepath):
self.srcfilepath = srcfilepath # main path
self.clusters = ['DG', 'Hilus', 'CA3', 'CA1', 'EC', 'PC']
def get_filename_path(self, filepath, filetype):
"""
Search the provided path for all files that match the filetype specified.
Parameters
----------
filepath : string
The folder path.
filetype: string
The file type(e.g. .bxr, .xlsx).
Returns
-------
Returns the paths for all files math the filetype.
"""
filename = []
Root = []
for root, dirs, files in os.walk(filepath):
for i in files:
if filetype in i:
filename.append(i)
Root.append(root)
return filename, Root
def read_related_files(self):
"""
Read the related files.
File input needed:
-------
- 'filtered_feature_bc_matrix.h5' (spaceranger_count pipeline output)
- 'scalefactors_json.json' (spaceranger_count pipeline output)
- 'tissue_positions_list.csv' (spaceranger_count pipeline output)
- 'tissue_lowres_image.png' (spaceranger_count pipeline output)
- 'Loupe Clusters.csv' (independently generated tissue structural clusters using Loupe Browser)
Returns
-------
csv_file: pandas.DataFrame tissue_positions_list.xlsx
'filtered_feature_bc_matrix.h5': parameters as followed
-tissue_lowres_scalef.
-features_name.
-matr_raw
-barcodes
img: png 'tissue_lowres_image.png'
csv_file_cluster:pandas.DataFrame 'Loupe Clusters.csv'
"""
##########################
h5_file_name = 'filtered_feature_bc_matrix.h5'
print(self.srcfilepath)
h5_file, json_Root = self.get_filename_path(self.srcfilepath, h5_file_name)
print(json_Root)
for i in range(len(h5_file)):
if h5_file[i][0] != '.':
h5_root = json_Root[i] + '/' + h5_file[i]
#############################################
filehdf5_10x = h5py.File(h5_root, 'r')
matrix = np.asarray(filehdf5_10x["matrix"])
shape = np.asarray(filehdf5_10x["matrix"]['shape'])
barcodes = np.asarray(filehdf5_10x["matrix"]["barcodes"])
# print(len(barcodes))
indices = np.asarray(filehdf5_10x["matrix"]["indices"])
indptr = np.asarray(filehdf5_10x["matrix"]["indptr"])
data = np.asarray(filehdf5_10x["matrix"]["data"])
features_name = np.asarray(filehdf5_10x["matrix"]["features"]['name'])
matr_raw = sp_sparse.csc_matrix((data, indices, indptr), shape=shape).toarray()
# Read json file to get the tissue_hires_scalef values to transfor the dots in csv to images
json_file_name = 'scalefactors_json.json'
json_file, json_Root = self.get_filename_path(self.srcfilepath, json_file_name)
for i in range(len(json_file)):
if json_file[i][0] != '.':
json_root = json_Root[i] + '/' + json_file[i]
with open(json_root) as json_file:
data = json.load(json_file)
spot_diameter_fullres = data['spot_diameter_fullres']
tissue_hires_scalef = data['tissue_hires_scalef']
fiducial_diameter_fullres = data['fiducial_diameter_fullres']
tissue_lowres_scalef = data['tissue_lowres_scalef']
column_list = ["barcode", "selection", "y", "x", "pixel_y", "pixel_x"]
######################
csv_file_name = 'tissue_positions_list.csv'
csv_file, csv_Root = self.get_filename_path(self.srcfilepath, csv_file_name)
for i in range(len(csv_file)):
if csv_file[i][0] != '.':
csv_root = csv_Root[i] + '/' + csv_file[i]
csv_file = pd.read_csv(csv_root, names=column_list)
csv_file.to_excel(self.srcfilepath + "tissue_positions_list.xlsx", index=False)
##################################
img_file_name = 'tissue_lowres_image.png'
img_file, img_Root = self.get_filename_path(self.srcfilepath, img_file_name)
for i in range(len(img_file)):
if img_file[i][0] != '.':
img_root = img_Root[i] + '/' + img_file[i]
img = mpimg.imread(img_root)
# color_map,Cluster_list = self.get_cluster_for_SRT(self, id = ix_filter, csv_file=csv_file)
csv_file_cluster_name = 'Loupe Clusters.csv'
csv_file_cluster_file, csv_file_cluster_Root = self.get_filename_path(self.srcfilepath, csv_file_cluster_name)
for i in range(len(csv_file_cluster_file)):
if csv_file_cluster_file[i][0] != '.':
csv_file_cluster_root = csv_file_cluster_Root[i] + '/' + csv_file_cluster_file[i]
#############################################
csv_file_cluster = pd.read_csv(csv_file_cluster_root)
return csv_file, tissue_lowres_scalef, features_name, matr_raw, barcodes, img, csv_file_cluster
def UMIs_Gene_plot(self, img_cut=None, extent=None, x_filter=None, y_filter=None, genes_per_cell=None, umis_per_cell=None, cdict=None, g=2, label=None,path=None):
fig, (ax, ax1, ax2) = plt.subplots(1, 3, figsize=(15, 8))
ax.imshow(img_cut)
ax.scatter(x_filter - extent[0], y_filter - extent[2], c=cdict[g], label=label[g], s=2, alpha=1)
######################
ax.legend(fontsize='small')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
ax.set_xticks([])
ax.set_yticks([])
#######################
ax1.imshow(img_cut, alpha=0.7)
################
pt = ax1.scatter(x_filter - extent[0], y_filter - extent[2], c=umis_per_cell, cmap='jet', s=2, alpha=1)
######################
pt.set_clim(vmax=80000)
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax1)
cax = divider.append_axes("bottom", size="3%", pad=0.2)
cbar = fig.colorbar(pt, shrink=.1, label='UMIs per cell', cax=cax, orientation="horizontal")
# plt.colorbar(pt,shrink=.3,label = 'UMIs per cell',ax=ax1)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['bottom'].set_visible(False)
ax1.spines['left'].set_visible(False)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax1.get_yticklabels(), visible=False)
ax1.set_xticks([])
ax1.set_yticks([])
#################################
ax2.imshow(img_cut, alpha=0.7)
pt1 = ax2.scatter(x_filter - extent[0], y_filter - extent[2], c=genes_per_cell, cmap='jet', s=2, alpha=1)
pt1.set_clim(vmax=8000)
divider = make_axes_locatable(ax2)
cax = divider.append_axes("bottom", size="3%", pad=0.2)
cbar = fig.colorbar(pt1, shrink=.1, label='Genes per cell', cax=cax, orientation="horizontal")
# plt.colorbar(pt1,shrink=.3,label = 'Genes per cell',ax=ax2)
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.spines['bottom'].set_visible(False)
ax2.spines['left'].set_visible(False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.set_xticks([])
ax2.set_yticks([])
###########################################################
plt.tight_layout()
plt.subplots_adjust(wspace=0.5, hspace=0)
plt.savefig(path + 'all_gene_' + 'UMI' + ".png", format='png', dpi=600)
def PCA_Clustering(self, gene_expression_series=None, x_filter=None, extent=None, y_filter=None, new_id_filter=None,img_cut=None, path=None):
# PCA & clustering
# Do PCA
pca = PCA(n_components=3)
pca_result = pca.fit_transform(gene_expression_series)
# Store results of PCA in a data frame
result = pd.DataFrame(pca_result, columns=['PCA%i' % i for i in range(3)])
########################
from sklearn.cluster import KMeans
distortions = []
K = range(1, 15)
for k in K:
kmeanModel = KMeans(n_clusters=k)
kmeanModel.fit(pca_result)
distortions.append(kmeanModel.inertia_)
# choose the best k
interval = 0
K = 0
for i in range(1, len(distortions) - 1):
if interval < abs(distortions[i] - distortions[i - 1]) / abs(
distortions[i + 1] - distortions[i]) and abs(distortions[i] - distortions[i - 1]) - abs(
distortions[i + 1] - distortions[i]) > 0:
interval = abs(distortions[i] - distortions[i - 1]) / abs(distortions[i + 1] - distortions[i])
K = i + 2
if K == 0:
num_clus = 3
else:
num_clus = K
cluster, centers, distance = self.k_means(pca_result, num_clus)
x_coordinate, y_coordinate = x_filter - extent[0], y_filter - extent[2]
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
ax[0].scatter(pca_result[:, 0], pca_result[:, 1], c=cluster, cmap='jet', marker='o', s=2, alpha=1)
ax[0].set_xlabel('1st principal component', fontsize=20)
ax[0].set_ylabel('2nd principal component', fontsize=20)
ax[0].set_title('PCA', fontsize=23)
ax[0].grid(False)
################
cluster = [cluster[i] for i in new_id_filter]
ax[1].imshow(img_cut, alpha=1)
ax[1].scatter(x_coordinate, y_coordinate, c='black', s=2, alpha=0.7)
bul = ax[1].scatter(x_coordinate, y_coordinate, marker='o', c=cluster, cmap='jet', s=2, alpha=1)
cbar = fig.colorbar(bul, ticks=np.arange(np.min(cluster), np.max(cluster) + 1), shrink=.7)
cbar.set_label('Cluster')
ax[1].set_xlabel('Pixel')
ax[1].set_ylabel('Pixel')
ax[1].set_ylim(max(y_coordinate), 0)
ax[1].set_aspect('equal', 'box')
ax[1].grid(False)
ax[1].set_title('Clusters for electrodes', fontsize=23)
colorMapTitle = 'PCA_Clustering'
fig.savefig(path + colorMapTitle + ".png", format='png', dpi=600)
plt.close()
def k_means(self, data, num_clus=3, steps=200):
# Convert data to Numpy array
cluster_data = np.array(data)
# Initialize by randomly selecting points in the data
center_init = np.random.randint(0, cluster_data.shape[0], num_clus)
# Create a list with center coordinates
center_init = cluster_data[center_init, :]
# Repeat clustering x times
for _ in range(steps):
# Calculate distance of each data point to cluster center
distance = []
for center in center_init:
tmp_distance = np.sqrt(np.sum((cluster_data - center) ** 2, axis=1))
# Adding smalle random noise to the data to avoid matching distances to centroids
tmp_distance = tmp_distance + np.abs(np.random.randn(len(tmp_distance)) * 0.0001)
distance.append(tmp_distance)
# Assign each point to cluster based on minimum distance
_, cluster = np.where(np.transpose(distance == np.min(distance, axis=0)))
# Find center of mass for each cluster
center_init = []
for i in range(num_clus):
center_init.append(cluster_data[cluster == i, :].mean(axis=0).tolist())
return cluster, center_init, distance
def gene_expression(self, plot_UMIs=True, select_plot='select_genes', top_gene_show=20, plot_gene_expression=True, plot_mutual_information=True, top_common_gene_show=300, PCA_Clustering=True, gene_list_name=None, value=0.9):
"""
Plot specific gene lists and select genes for analysis.
File input needed:
-------
- related files
- 'gene_list_all.xlsx'
Parameters
-------
Returns
-------
File output:
-------
if plot_gene_expression = True AND select_plot = 'gene_list'
- '[gene_list]_gene_expression.png'
- '[gene_list]_gene_expression_per_cluster.xlsx'
if plot_gene_expression = True AND select_plot = 'select genes'
- 'select_gene_expression.png'
- 'select_gene_expression_per_cluster.xlsx'
if plot_gene_expression = True AND select_plot = 'top_expressed_genes'
- 'top_expressed_gene_expression.png'
- 'top_expressed_gene_expression_per_cluster.xlsx'
if plot_gene_expression = True AND select_plot = 'top_expressed_common_genes'
- 'top_expressed_common_gene_expression.png'
- 'top_expressed_common_gene_expression_per_cluster.xlsx'
if plot_mutual_information = True
- 'mutual_information.txt'
- '[gene_list]_mutual_information_paired_cluster.xlsx'
- '[gene_list]_mutual_information_paired_cluster.png'
- '[gene_list]_SRT_mutual_information_connectivity.xlsx'
- '[gene_list]_SRT_mutual_information_connectivity.png'
if plot_UMIs = True
- 'all_gene_UMI.png'
if PCA_clustering = True
- 'PCA_Clustering.png'
"""
path = self.srcfilepath[:self.srcfilepath.rfind('/')]
desfilepath = path + '/Gene_Expression_Plots/'
if not os.path.exists(desfilepath):
os.mkdir(desfilepath)
# Read related information
csv_file, tissue_lowres_scalef, features_name, matr_raw, barcodes, img, csv_file_cluster = self.read_related_files()
barcode_cluster = np.asarray(csv_file_cluster["Barcode"])
Loupe_cluster = np.asarray(csv_file_cluster["Loupe Clusters"])
color = ['red' if i == 1 else 'black' for i in csv_file['selection']]
label = {1: 'Detect points', 0: 'Background'}
cdict = {1: 'red', 0: 'black'}
scatter_x = np.asarray(csv_file["pixel_x"] * tissue_lowres_scalef)
scatter_y = np.asarray(csv_file["pixel_y"] * tissue_lowres_scalef)
group = np.asarray(csv_file["selection"])
barcode_CSV = np.asarray(csv_file["barcode"])
g = 1
ix = np.where(group == g)
#################################################################################Filters:
# Remove spots with fewer than 1000 unique genes
# Remove mitochondrial genes and ribosomal protein coding genes
import re
gene_name = [str(features_name[i])[2:-1] for i in range(len(features_name))]
filter_gene_id = [i for i in range(len(gene_name)) if
len(re.findall(r'^SRS', gene_name[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^Mrp', gene_name[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^Rp', gene_name[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^mt', gene_name[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^Ptbp', gene_name[i], flags=re.IGNORECASE)) > 0]
gene_name = [gene_name[i] for i in range(len(gene_name)) if i not in filter_gene_id]
matr = np.delete(matr_raw, filter_gene_id, axis=0)
# print(matr.sum(axis=0))# calculate UMIs and genes per cell
# calculate UMIs and genes per cell
umis_per_cell = np.asarray(matr.sum(axis=0)).squeeze() # Or matr.sum(axis=0)
genes_per_cell = np.asarray((matr > 0).sum(axis=0)).squeeze()
###############delete the nodes with less then 1000 gene count
deleted_notes = [i for i in range(len(genes_per_cell)) if genes_per_cell[i] <= 1000]
###############delete the nodes not in clusters
deleted_notes_cluster = [i for i in range(len(genes_per_cell)) if str(barcodes[i])[2:-1] not in barcode_cluster]
deleted_notes.extend(deleted_notes_cluster)
deleted_notes = list(np.unique(deleted_notes))
##########################################################
matr = np.delete(matr, deleted_notes, axis=1)
barcodes_filter = [barcodes[i] for i in range(len(barcodes)) if i not in deleted_notes]
new_id_filter = [j for i in barcode_CSV for j in range(len(barcodes_filter)) if
str(barcodes_filter[j])[2:-1] == i]
genes_per_cell_raw = [i if i > 1000 else 0 for i in genes_per_cell]
umis_per_cell_raw = [umis_per_cell[i] if genes_per_cell[i] > 1000 else 0 for i in range(len(umis_per_cell))]
new_id = [j for i in barcode_CSV for j in range(len(barcodes)) if str(barcodes[j])[2:-1] == i]
umis_per_cell = [umis_per_cell_raw[new_id[i]] for i in range(len(new_id)) if i not in deleted_notes]
genes_per_cell = [genes_per_cell_raw[new_id[i]] for i in range(len(new_id)) if i not in deleted_notes]
x_filter = [scatter_x[ix][i] for i in range(len(scatter_x[ix])) if new_id[i] not in deleted_notes]
y_filter = [scatter_y[ix][i] for i in range(len(scatter_y[ix])) if new_id[i] not in deleted_notes]
ix_filter = [ix[0][i] for i in range(len(scatter_x[ix])) if new_id[i] not in deleted_notes]
# print(len(new_id_filter),len(x_filter),max(new_id_filter))
Barcodes_deleted_filter = barcodes_filter
mask_id = [i for i in range(len(group)) if group[i] == 1]
extent = [min([scatter_x[i] for i in mask_id]), max([scatter_x[i] for i in mask_id]),
min([scatter_y[i] for i in mask_id]), max([scatter_y[i] for i in mask_id])]
img_cut = img[int(extent[2]):int(extent[3]) + 2, int(extent[0]):int(extent[1]) + 3,
:] # x and y value set to cut the areas interested
if plot_UMIs == True:
self.UMIs_Gene_plot(img_cut=img_cut, extent=extent, x_filter=x_filter, y_filter=y_filter,
genes_per_cell=genes_per_cell, umis_per_cell=umis_per_cell, cdict=cdict, g=g,
label=label,path=desfilepath)
adata = AnnData(np.array(matr))
sc.pp.normalize_total(adata, inplace=True)
gene_expression = adata.X
genes_expression_count = np.asarray((matr > 0).sum(axis=1)).squeeze()
##########################cross correlation based on gene expression level
gene_expression_filter = [i for i in np.asarray(gene_expression) if any(i)]
############################ choose top gene
from operator import itemgetter
indices, genes_expression_count_sorted = zip(
*sorted(enumerate(genes_expression_count), key=itemgetter(1), reverse=True))
###################################Choose gene 1 way
if select_plot == 'top_expressed_common_genes':
Selected_select_genes = self.find_common_gene(condition1_path=condition1_path, condition2_path=condition2_path,
top_common_gene_show=top_common_gene_show)
gene_name_list = [gene_name[i] for i in indices]
top_gene_indices = [i for name in Selected_select_genes for i in range(len(gene_name_list)) if
len(re.findall(gene_name_list[i], name, flags=re.IGNORECASE)) > 0]
top_gene_name = Selected_select_genes
###################################Choose gene way 2
elif select_plot == 'gene_list':
filetype_gene = 'gene_list_all.xlsx'
filename_gene, Root = self.get_filename_path(self.srcfilepath, filetype_gene)
for i in range(len(filename_gene)):
if filename_gene[i][0] != '.':
gene_root = Root[i] + '/' + filename_gene[i]
Selected_select_genes = list(pd.read_excel(gene_root)[gene_list_name])
Selected_select_genes = [i for i in Selected_select_genes if type(i) == str]
gene_name_list = [gene_name[i] for i in indices]
top_gene_indices = [i for name in Selected_select_genes for i in range(len(gene_name_list)) if
len(re.findall(gene_name_list[i], name, flags=re.IGNORECASE)) > 0]
top_gene_name = Selected_select_genes
elif select_plot == 'select_genes':
Selected_select_genes = select_genes
gene_name_list = [gene_name[i] for i in indices]
top_gene_indices = [i for name in Selected_select_genes for i in range(len(gene_name_list)) if
len(re.findall(gene_name_list[i], name, flags=re.IGNORECASE)) > 0]
top_gene_name = Selected_select_genes
else:
top_gene_indices = indices[:top_gene_show] # choose the top 300 expression genes
df_gene_count = pd.DataFrame({'Gene Name': [gene_name[i] for i in indices],
'Gene Count sorted': list(genes_expression_count_sorted)})
top_gene_name = list(df_gene_count['Gene Name'][:top_gene_show])
print('check2', top_gene_name)
#####################################
gene_expression_filter = [np.asarray(gene_expression)[i] for i in top_gene_indices]
gene_expression_series = np.asarray(gene_expression_filter).T
id_no_expression = [i for i in range(len(gene_expression_series)) if sum(gene_expression_series[i]) == 0]
gene_expression_series = np.delete(gene_expression_series, id_no_expression, axis=0)
# Channel_ID = [i for i in range(len(genes_per_cell)) if i not in id_no_expression]
Channel_ID = [new_id_filter[i] for i in range(len(new_id_filter)) if i not in id_no_expression]
# x_filter = [x_filter[i] for i in range(len(x_filter)) if i not in id_no_expression]
# y_filter = [y_filter[i] for i in range(len(y_filter)) if i not in id_no_expression]
barcodes_filter_1 = [barcodes_filter[i] for i in range(len(barcodes_filter)) if i not in id_no_expression]
new_id_filter_1 = [j for i in barcode_CSV for j in range(len(barcodes_filter_1)) if
str(barcodes_filter_1[j])[2:-1] == i]
# choose the highest top expression gene
if plot_gene_expression == True:
Cluster_list = list(self.clusters)
Cluster_list.append('Not in Cluster')
color_map = []
barcode_list = []
for i in ix_filter:
bar_code = csv_file['barcode'][i]
barcode_list.append(bar_code)
try:
clu = Loupe_cluster[list(barcode_cluster).index(bar_code)]
for j in range(len(Cluster_list)):
if Cluster_list[j] == clu:
color_map.append(j)
except:
color_map.append(len(Cluster_list) - 1)
cluster_list = [Cluster_list[i] for i in color_map]
print(top_gene_name)
self.plot_top_expressed_gene(top_gene_name=top_gene_name, gene_name=gene_name,
gene_expression=gene_expression,
new_id_filter=new_id_filter, img_cut=img_cut, x_filter=x_filter,
y_filter=y_filter, extent=extent, cluster_list=cluster_list,
barcode_list=barcode_list, gene_list_name=gene_list_name,
gene_from_option=select_plot, path=desfilepath)
if plot_mutual_information == True:
self.plot_mutual_information(gene_expression_series=gene_expression_series,
x_filter=x_filter, extent=extent, y_filter=y_filter,
img_cut=img_cut, Channel_ID=Channel_ID,
gene_list_name=gene_list_name,
Barcodes=Barcodes_deleted_filter,
new_id_filter=new_id_filter_1, value=value,
barcode_filter_for_cluster=barcodes_filter_1,
barcode_cluster=barcode_cluster,
Loupe_cluster=Loupe_cluster)
if PCA_Clustering == True:
self.PCA_Clustering(gene_expression_series=gene_expression, x_filter=x_filter, extent=extent,
y_filter=y_filter, new_id_filter=new_id_filter, img_cut=img_cut, path=desfilepath)
print('Done')
def find_common_gene(self, condition1_path=None, condition2_path=None, top_common_gene_show=300):
##########################
h5_file_name_condition1 = 'filtered_feature_bc_matrix.h5'
h5_file_condition1, h5_file_Root_condition1 = self.get_filename_path(condition1_path, h5_file_name_condition1)
for i in range(len(h5_file_condition1)):
if h5_file_condition1[i][0] != '.':
h5_root_condition1 = h5_file_Root_condition1[i] + '/' + h5_file_condition1[i]
#############################################
filehdf5_10x = h5py.File(h5_root_condition1, 'r')
features_name = np.asarray(filehdf5_10x["matrix"]["features"]['name'])
gene_name_condition1 = [str(features_name[i])[2:-1] for i in range(len(features_name))]
shape = np.asarray(filehdf5_10x["matrix"]['shape'])
indices = np.asarray(filehdf5_10x["matrix"]["indices"])
indptr = np.asarray(filehdf5_10x["matrix"]["indptr"])
data = np.asarray(filehdf5_10x["matrix"]["data"])
matr_raw = sp_sparse.csc_matrix((data, indices, indptr), shape=shape).toarray()
adata = AnnData(np.array(matr_raw))
sc.pp.normalize_total(adata, inplace=True)
genes_expression_count = np.asarray((adata.X > 0).sum(axis=1)).squeeze()
from operator import itemgetter
indices, genes_expression_count_sorted = zip(
*sorted(enumerate(genes_expression_count), key=itemgetter(1), reverse=True))
df_gene_count_condition1 = pd.DataFrame({'Gene Name': [gene_name_condition1[i] for i in indices],
'Gene Count sorted': list(genes_expression_count_sorted)})
##########################
##########################
h5_file_name_condition2 = 'filtered_feature_bc_matrix.h5'
h5_file_condition2, h5_file_Root_condition2 = self.get_filename_path(condition2_path, h5_file_name_condition2)
for i in range(len(h5_file_condition2)):
if h5_file_condition2[i][0] != '.':
h5_root_condition2 = h5_file_Root_condition2[i] + '/' + h5_file_condition2[i]
#############################################
filehdf5_10x = h5py.File(h5_root_condition2, 'r')
features_name = np.asarray(filehdf5_10x["matrix"]["features"]['name'])
gene_name_condition2 = [str(features_name[i])[2:-1] for i in range(len(features_name))]
shape = np.asarray(filehdf5_10x["matrix"]['shape'])
indices = np.asarray(filehdf5_10x["matrix"]["indices"])
indptr = np.asarray(filehdf5_10x["matrix"]["indptr"])
data = np.asarray(filehdf5_10x["matrix"]["data"])
matr_raw = sp_sparse.csc_matrix((data, indices, indptr), shape=shape).toarray()
adata = AnnData(np.array(matr_raw))
sc.pp.normalize_total(adata, inplace=True)
genes_expression_count = np.asarray((adata.X > 0).sum(axis=1)).squeeze()
from operator import itemgetter
indices, genes_expression_count_sorted = zip(
*sorted(enumerate(genes_expression_count), key=itemgetter(1), reverse=True))
df_gene_count_condition2 = pd.DataFrame({'Gene Name': [gene_name_condition1[i] for i in indices],
'Gene Count sorted': list(genes_expression_count_sorted)})
common_gene = list(set(gene_name_condition1) & set(gene_name_condition2))
import re
filter_gene_id = [i for i in range(len(common_gene)) if
len(re.findall(r'^SRS', common_gene[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^Mrp', common_gene[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^Rp', common_gene[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^mt', common_gene[i], flags=re.IGNORECASE)) > 0 or len(
re.findall(r'^Ptbp', common_gene[i], flags=re.IGNORECASE)) > 0]
common_gene = [common_gene[i] for i in range(len(common_gene)) if i not in filter_gene_id]
##############
############################ choose top gene
set_gene_condition1 = [i for i in df_gene_count_condition1['Gene Name'] if i in common_gene]
set_gene_condition2 = [i for i in df_gene_count_condition2['Gene Name'] if i in common_gene]
for k in range(100, len(common_gene), 100):
if len(list(set(set_gene_condition1[:top_common_gene_show + k]) & set(
set_gene_condition2[:top_common_gene_show + k]))) >= top_common_gene_show:
common_gene_top = list(
set(set_gene_condition1[:top_common_gene_show + k]) & set(set_gene_condition2[:top_common_gene_show + k]))[
:top_common_gene_show]
break
else:
continue
return common_gene_top
def plot_top_expressed_gene(self, top_gene_name=None, gene_name=None, gene_from_option=None, gene_expression=None,
new_id_filter=None, img_cut=None, x_filter=None, y_filter=None, extent=None,
cluster_list=None, barcode_list=None, gene_list_name=None, path=None):
# from operator import itemgetter
# indices, genes_expression_count_sorted = zip(*sorted(enumerate(genes_expression_count), key=itemgetter(1), reverse=True))
# df_gene_count = pd.DataFrame({'Gene Name': [gene_name[i] for i in indices], 'Gene Count sorted': list(genes_expression_count_sorted)})
# top_gene_name = df_gene_count['Gene Name'][:top_gene_show]
# print(top_gene_name)
#########################################################
cluster_all = []
gene_expression_values_all = []
gene_name_all = []
channel_position = []
barcode_list_all = []
# print(list(top_gene_name))
fig, ax = plt.subplots(nrows=int(len(list(top_gene_name)) / 5) + 1, ncols=5,
figsize=(20, 20)) # , facecolor='None'
k = 0
import re
for gene_name_choose in list(top_gene_name):
id_find = -1
for i in range(len(gene_name)):
if len(re.findall(gene_name_choose, gene_name[i], flags=re.IGNORECASE)) > 0:
# if gene_name_choose == gene_name[i]:
id_find = i
if id_find != -1:
gene_expression_find = gene_expression[id_find]
gene_expression_find = [gene_expression_find[i] for i in new_id_filter]
gene_expression_values_all.extend(gene_expression_find)
gene_name_all.extend([gene_name_choose] * len(gene_expression_find))
cluster_all.extend(cluster_list)
barcode_list_all.extend(barcode_list)
channel_position.extend(
[((x_filter - extent[0])[i], (y_filter - extent[2])[i]) for i in range(len(x_filter))])
#######################
# print('k',k)
ax[int(k / 5), int(k % 5)].imshow(img_cut, alpha=1)
################
pt = ax[int(k / 5), int(k % 5)].scatter(x_filter - extent[0], y_filter - extent[2],
c=gene_expression_find, cmap='jet', s=2, alpha=0.7)
######################
# pt.set_clim(vmax=80000)
divider = make_axes_locatable(ax[int(k / 5), int(k % 5)])
cax = divider.append_axes("bottom", size="3%", pad=0.2)
cbar = fig.colorbar(pt, shrink=.1, label='Gene Expression', cax=cax, orientation='horizontal')
ax[int(k / 5), int(k % 5)].spines['top'].set_visible(False)
ax[int(k / 5), int(k % 5)].spines['right'].set_visible(False)
ax[int(k / 5), int(k % 5)].spines['bottom'].set_visible(False)
ax[int(k / 5), int(k % 5)].spines['left'].set_visible(False)
plt.setp(ax[int(k / 5), int(k % 5)].get_xticklabels(), visible=False)
plt.setp(ax[int(k / 5), int(k % 5)].get_yticklabels(), visible=False)
ax[int(k / 5), int(k % 5)].set_xticks([])
ax[int(k / 5), int(k % 5)].set_yticks([])
ax[int(k / 5), int(k % 5)].set_title(gene_name_choose)
# ax[int(k / 5), int(k % 5)].set_box_aspect(1)
k += 1
fig.tight_layout()
fig.subplots_adjust(wspace=0.1, hspace=1)
# select_plot = 'select_genes', 'top_expressed_common_genes','gene_list','top_expressed_genes'
if gene_from_option == 'gene_list':
colorMapTitle = gene_list_name
elif gene_from_option == 'top_expressed_common_genes':
colorMapTitle = 'top_expressed_common'
elif gene_from_option == 'select_genes':
colorMapTitle = 'select'
else:
colorMapTitle = 'top_expressed'
fig.savefig(path + colorMapTitle + '_gene_expression' + ".png", format='png', dpi=600)
plt.close()
###########################
# cluster_all = []
# gene_expression_values_all = []
# gene_name_all = []
df = pd.DataFrame(
{'Barcode': barcode_list_all, 'Channel Position': channel_position, 'gene Name': gene_name_all,
'Gene Expression Level': gene_expression_values_all, 'Cluster': cluster_all})
df.to_excel(path + colorMapTitle + "_gene_expression_per_cluster" + ".xlsx", index=False)
def plot_mutual_information(self, gene_expression_series=None, x_filter=None, extent=None, y_filter=None, img_cut=None, Channel_ID=None,gene_list_name=None, Barcodes=None, new_id_filter=None,value=0.9, barcode_filter_for_cluster=None, barcode_cluster=None, Loupe_cluster=None):
################################
desfilepath = self.srcfilepath + 'Mutual_Information/'
if not os.path.exists(desfilepath):
os.mkdir(desfilepath)
barcode_filter_for_cluster = [str(i)[2:-1] for i in barcode_filter_for_cluster]
cluster_gene_expression = \
[Loupe_cluster[list(barcode_cluster).index(bar)] if bar in barcode_cluster else 'Not in Cluster' for bar in
barcode_filter_for_cluster]
gene_expression_series_sorted = []
cluster_gene_expression_sorted = []
for clu in self.clusters:
gene_expression_series_sorted.extend(
[gene_expression_series[i] for i in range(len(cluster_gene_expression)) if
cluster_gene_expression[i] == clu])
cluster_gene_expression_sorted.extend(
[cluster_gene_expression[i] for i in range(len(cluster_gene_expression)) if
cluster_gene_expression[i] == clu])
###################################
# [2: -1]
file_path = desfilepath + 'mutual_information' + ".txt"
f = open(file_path, "a")
f.seek(0)
f.truncate()
cluster_gene_expression_sorted_uni, cluster_index = np.unique(cluster_gene_expression_sorted, return_index=True)
cluster_group = [(cluster_gene_expression_sorted_uni[i], cluster_index[i]) for i in range(len(cluster_index))]
def takeSecond(elem):
return elem[1]
cluster_group.sort(key=takeSecond)
cluster_gene_expression_sorted_uni = [cluster_group[i][0] for i in range(len(cluster_group))]
cluster_index = [cluster_group[i][1] for i in range(len(cluster_group))]
for i in range(len(cluster_gene_expression_sorted_uni)):
if i == len(cluster_index) - 1:
msg = cluster_gene_expression_sorted_uni[i] + ' goes from ' + str(cluster_index[i]) + ' to ' + str(
len(cluster_gene_expression_sorted))
f.write(msg + '\n')
else:
msg = cluster_gene_expression_sorted_uni[i] + ' goes from ' + str(cluster_index[i]) + ' to ' + str(
cluster_index[i + 1] - 1)
f.write(msg + '\n')
f.close()
# else:
import math
import sklearn.metrics as sm
paired_cluster = []
mi_values = []
Per_cor = np.zeros(
(len(np.asarray(gene_expression_series_sorted)), len(np.asarray(gene_expression_series_sorted))),
dtype=np.float)
for i in range(len(np.asarray(gene_expression_series_sorted))):
clu_1 = str(cluster_gene_expression_sorted[i])
for j in range(i, len(np.asarray(gene_expression_series_sorted))):
clu_2 = str(cluster_gene_expression_sorted[j])
Per_cor[i, j] = sm.normalized_mutual_info_score(np.asarray(gene_expression_series_sorted)[i],
np.asarray(gene_expression_series_sorted)[j])
Per_cor[j, i] = Per_cor[i, j]
paired_cluster.append(clu_1 + '_' + clu_2)
mi_values.append(Per_cor[i, j])
# Per_cor = np.reshape(Per_cor, (len(gene_expression_series), len(gene_expression_series)))
where_are_NaNs = np.isnan(Per_cor)
Per_cor[where_are_NaNs] = 0
n = Per_cor.shape[0]
Per_cor[range(n), range(n)] = 0
# print(pd.DataFrame(Per_cor))
fig = plt.figure()
jet = plt.get_cmap('cool')
ax = fig.add_subplot(111)
cax = ax.imshow(Per_cor, cmap=jet)
cax.set_clim(0, 1.0)
ax.set_xlabel('Channel ID Sorted')
ax.set_ylabel('Channel ID Sorted')
ax.grid(False)
fig.colorbar(cax, label='Mutual Information', ax=ax, shrink=.3)
from matplotlib.ticker import MaxNLocator
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
##############################
colorMapTitle = gene_list_name + "_mutual_information_paired_cluster"
fig.savefig(desfilepath + colorMapTitle + ".png", format='png', dpi=600)
d = {'Paired Cluster': paired_cluster, 'MI values': mi_values}
df = pd.DataFrame(data=d)
df.to_excel(desfilepath + colorMapTitle + ".xlsx", index=False)
####################################################
Channel_ID = [i for i in range(len(Per_cor))]
Channel_ID_new = []
Barcodes_Channel_ID = []
Corr_id = []
Barcodes_Corr_id = []
coor_Value = []
###############################
for i in range(len(Per_cor)):
Corr_id.extend([Channel_ID[new_id_filter[j]] for j in range(len(Per_cor[i, :])) if Per_cor[i, j] >= value])
Barcodes_Corr_id.extend(
[Barcodes[new_id_filter[j]] for j in range(len(Per_cor[i, :])) if Per_cor[i, j] >= value])
coor_Value.extend([j for j in Per_cor[i, :] if j >= value])
Channel_ID_new.extend(
[Channel_ID[new_id_filter[i]]] * len([j for j in range(len(Per_cor[i, :])) if Per_cor[i, j] >= value]))
Barcodes_Channel_ID.extend(
[Barcodes[new_id_filter[i]]] * len([j for j in range(len(Per_cor[i, :])) if Per_cor[i, j] >= value]))
x_coordinate, y_coordinate = x_filter - extent[0], y_filter - extent[2]
dataframe = pd.DataFrame(
{'Channel ID': Channel_ID_new, 'Barcodes_Channel_ID': Barcodes_Channel_ID, 'Corr_id': Corr_id,
'Barcodes_Corr_id': Barcodes_Corr_id, 'coor_Per_data': coor_Value})
dataframe.to_excel(self.srcfilepath + gene_list_name + '_SRT_mutual_information_connectivity' + ".xlsx", index=False)
# print(dataframe)
fig, ax = plt.subplots()
ax.imshow(img_cut, alpha=1)
################
ax.scatter(x_coordinate, y_coordinate, c='black', s=2, alpha=0.7)
ax.set_xlabel('Pixel')
ax.set_ylabel('Pixel')
count = 0
for i in range(len(Channel_ID_new)):
if count % 100 == 1: # choose 50% correlation to plot
df = pd.DataFrame({'x': (x_coordinate[Channel_ID_new[i]], x_coordinate[Corr_id[i]]),
'y': (y_coordinate[Channel_ID_new[i]], y_coordinate[Corr_id[i]])})
ax.plot('x', 'y', data=df, color='blue', linewidth=0.5, alpha=0.3)
count += 1
ax.set_ylim(max(y_coordinate), 0)
ax.set_aspect('equal', 'box')
ax.grid(False)
colorMapTitle = gene_list_name + "_SRT_mutual_information_connectivity"
fig.savefig(self.srcfilepath + colorMapTitle + ".png", format='png', dpi=600)
def mutual_information_statistics(self, gene_list_choose='IEGs'):
"""
Prepare mutual information distance score statistics
File input needed:
-------
- '[gene_list]_mutual_information_paired_cluster.xlsx'
Parameters
-------
Returns
-------
File output:
-------
- '[gene_list]_mutual_information_paired_cluster_statistics.xlsx
"""
desfilepath = self.srcfilepath + 'Mutual_Information/'
if not os.path.exists(desfilepath):
os.mkdir(desfilepath)
filetype_excel = gene_list_choose + '_mutual_information_paired_cluster.xlsx'
filename_excel, Root = self.get_filename_path(self.srcfilepath, filetype_excel)
for i in range(len(filename_excel)):
if filename_excel[i][0] != '.':
Cluster_con = []
Mean_for_clusters = []
Sem_for_clusters = []
data = pd.read_excel(Root[i] + '/' + filename_excel[i])
paired_cluster_uni = np.unique(data['Paired Cluster'])
for clu in paired_cluster_uni:
Cluster_con.append(clu)
data_clu = data.copy()
data_clu = data_clu[data_clu['Paired Cluster'] == clu]
s = pd.Series(range(len(data_clu)))
data_clu = data_clu.set_index(s)
Mean_for_clusters.append(np.mean(data_clu['MI values']))
Sem_for_clusters.append(stats.sem(data_clu['MI values']))
df = pd.DataFrame(
{'Clusters': Cluster_con, 'Mean for MI values': Mean_for_clusters,
'SEM for MI values': Sem_for_clusters})
df.to_excel(desfilepath + filename_excel[i][:-5] + "_statistics" + ".xlsx", index=False)
def mutual_information_pooled_statistics(self, gene_list_choose='IEGs'):
"""
Compare mutual information distance scores between input conditions i.e. SD and ENR
File input needed:
-------
- '[gene_list]_mutual_information_paired_cluster.xlsx'
Parameters
-------
Returns
-------
File output:
-------
- '[gene_list]_mutual_information_paired_cluster_pooled_statistics.xlsx'
- '[gene_list]_mutual_information_paired_cluster_pooled_statistics_p_values.xlsx'
"""
path = self.srcfilepath[:self.srcfilepath.rfind('/')]
desfilepath = path + '/Mutual_Information_Pooled_Statistics/'
if not os.path.exists(desfilepath):
os.mkdir(desfilepath)
filetype_excel = gene_list_choose + '_mutual_information_paired_cluster.xlsx'
filename_excel, Root = self.get_filename_path(self.srcfilepath, filetype_excel)
Cluster_con = []
final_Gene_expression_clusters = pd.DataFrame()
for i in range(len(filename_excel)):
if filename_excel[i][0] != '.':
data = pd.read_excel(Root[i] + '/' + filename_excel[i])
final_Gene_expression_clusters = pd.concat([final_Gene_expression_clusters, data], axis=0).reset_index()
for con_name in conditions:
if con_name in Root[i]:
Cluster_con.extend([con_name] * len(data))
final_Gene_expression_clusters['Condition'] = Cluster_con
final_Gene_expression_clusters = final_Gene_expression_clusters.drop(['level_0', 'index'], axis=1)
final_Gene_expression_clusters.to_excel(
desfilepath + gene_list_choose + '_mutual_information_paired_cluster_pooled_statistics.xlsx', index=False)
p_values = []
Paired_Cluster = []
paired_cluster_uni = np.unique(final_Gene_expression_clusters['Paired Cluster'])
for clu in paired_cluster_uni:
Cluster_con.append(clu)
data_clu = final_Gene_expression_clusters.copy()
data_clu = data_clu[data_clu['Paired Cluster'] == clu]
s = pd.Series(range(len(data_clu)))
data_clu = data_clu.set_index(s)
#####################################
value_con = data_clu.copy()
value_con = value_con[value_con['Condition'] == conditions[1]]
s = pd.Series(range(len(value_con)))
value_con = value_con.set_index(s)
condition2_Values = list(value_con['MI values'])
value_con = data_clu.copy()
value_con = value_con[value_con['Condition'] == conditions[0]]
s = pd.Series(range(len(value_con)))
value_con = value_con.set_index(s)
condition1_Values = list(value_con['MI values'])
if len(condition2_Values) > 0 and len(condition1_Values) > 0:
p_values.append(float(stats.ttest_ind(condition2_Values, condition1_Values, equal_var=False, nan_policy='omit')[1]))
Paired_Cluster.append(clu)
df = pd.DataFrame({'Clusters': Paired_Cluster, 'p_values': p_values})
df.to_excel(desfilepath + gene_list_choose + '_mutual_information_paired_cluster_pooled_statistics_p_values.xlsx',
index=False)
def gene_expression_pooled_statistics(self, gene_list_choose='IEGs'):
"""
Compare gene expression values within a given gene list between conditions i.e. SD and ENR
File input needed:
-------
- '[gene_list]_gene_expression_per_cluster.xlsx'
Parameters
-------
Returns
-------
File output:
-------
- '[gene_list]_gene_expression_per_cluster_pooled.xlsx'
- '[gene_list]_gene_expression_pooled_statistics.png'
- '[gene_list]_gene_expression_pooled_statistics_p_values.xlsx'
"""
path = self.srcfilepath[:self.srcfilepath.rfind('/')]
desfilepath = path + '/Gene_Expression_Pooled_Statistics/'
if not os.path.exists(desfilepath):
os.mkdir(desfilepath)
if os.path.exists(desfilepath + gene_list_choose + "_gene_expression_per_cluster_pooled" + ".xlsx"):
print('Find ' + gene_list_choose + "_gene_expression_per_cluster_pooled" + ".xlsx")
else:
filetype_excel = gene_list_choose + '_gene_expression_per_cluster.xlsx'
filename_excel, Root = self.get_filename_path(self.srcfilepath, filetype_excel)
Barcode = []
Channel_Position = []
gene_Name = []
Gene_Expression_Level = []
Cluster = []
Condition_all = []
file_name_all = []
for i in range(len(filename_excel)):
if filename_excel[i][0] != '.':
data = pd.read_excel(Root[i] + '/' + filename_excel[i])
gene_Name.extend(data['gene Name'])
Barcode.extend(data['Barcode'])
Channel_Position.extend(data['Channel Position'])
Cluster.extend(data['Cluster'])
data['Gene Expression Level'] = data['Gene Expression Level'].fillna(0)
Gene_Expression_Level.extend(data['Gene Expression Level'])
filetype_bxr = '.bxr'
root_raw = Root[i][:Root[i].rfind('/')]
filename_bxr, Root_bxr = self.get_filename_path(root_raw + '/', filetype_bxr)
file_name_all.extend([filename_bxr[0][:-4]] * len(data['gene Name']))
name = 'No condition'
for con_name in conditions:
if con_name in Root[i]:
name = con_name
Condition_all.extend([name] * len(data['gene Name']))
df_Gene_expression_clusters_all = pd.DataFrame(
{'Barcode': Barcode, 'Channel Position': Channel_Position, 'Gene Name': gene_Name,
'Gene Expression Level': Gene_Expression_Level, 'Cluster': Cluster,
'File Name': file_name_all, 'Condition': Condition_all})
df_Gene_expression_clusters_all.to_excel(
desfilepath + gene_list_choose + "_gene_expression_per_cluster_pooled" + ".xlsx",
index=False)
filetype_excel = gene_list_choose + "_gene_expression_per_cluster_pooled" + ".xlsx"
filename_excel, Root = self.get_filename_path(path, filetype_excel)
for i in range(len(filename_excel)):
if filename_excel[i][0] != '.':