This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import csv | |
import gzip | |
import os | |
import scipy.io | |
import pandas as pd | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.decomposition import PCA | |
import umap | |
from sklearn.cluster import Birch, AffinityPropagation, DBSCAN, MeanShift, SpectralClustering, AgglomerativeClustering, estimate_bandwidth |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#reading 10X data as stated at support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/matrices | |
matrix_dir = "filtered_feature_bc_matrix" | |
mat = scipy.io.mmread(os.path.join(matrix_dir, "matrix.mtx")) | |
mat = np.array(mat.todense()) | |
features_path = os.path.join(matrix_dir, "features.tsv") | |
annotation = pd.read_csv(features_path,sep='\t',header=None) | |
annotation.columns = ['feature_ids','gene_names','feature_types'] | |
barcodes_path = os.path.join(matrix_dir, "barcodes.tsv") | |
barcodes = [line.strip() for line in open(barcodes_path, 'r')] | |
print('Matrix dimensionality {}'.format(mat.shape)) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
f, ax = plt.subplots(1,2,figsize=(15,5)) | |
per_cell_sum = mat.sum(axis=1) | |
ax[0].hist(np.log10(per_cell_sum+1)); | |
ax[0].set_title('Distribtion of #UMIs per cell\n min {}, max {}, mean {} +- {}'.format(min(per_cell_sum), | |
max(per_cell_sum), np.mean(per_cell_sum), | |
np.sqrt(np.std(per_cell_sum)))); | |
per_gene_sum = mat.sum(axis=0) | |
ax[1].hist(np.log10(per_gene_sum+1)); | |
ax[1].set_title('Distribtion of #UMIs per gene\n min {}, max {}, mean {} +- {}'.format(min(per_gene_sum), | |
max(per_gene_sum), np.mean(per_gene_sum), |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
low_expr_thr = 100 | |
high_expr_thr = 100000 | |
mat = mat[:,(per_gene_sum>=low_expr_thr) & (per_gene_sum<=high_expr_thr)] #just remove extreme outliers | |
mean_exp = mat.mean(axis=0) | |
std_exp = np.sqrt(mat.std(axis=0)) | |
CV = std_exp/mean_exp | |
plt.hist(CV); | |
plt.title('Distribution of CV, mean {} sd {}'.format(np.mean(CV), np.std(CV)**0.5)); |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
mat = mat[:,CV>=10] | |
f, ax = plt.subplots(1,2,figsize=(15,5)) | |
per_cell_sum = mat.sum(axis=1) | |
ax[0].hist(np.log10(per_cell_sum+1)); | |
ax[0].set_title('Distribtion of #UMIs per cell\n min {}, max {}, mean {} +- {}'.format(min(per_cell_sum), | |
max(per_cell_sum), np.mean(per_cell_sum), | |
np.sqrt(np.std(per_cell_sum)))); | |
per_gene_sum = mat.sum(axis=0) | |
ax[1].hist(np.log10(per_gene_sum+1)); | |
ax[1].set_title('Distribtion of #UMIs per gene\n min {}, max {}, mean {} +- {}'.format(min(per_gene_sum), |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
cells_expression = mat.sum(axis=1) | |
mat = mat[cells_expression>=100,:] | |
mat = np.log(mat+1) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
pca = PCA(n_components=100) | |
pca.fit(mat) | |
mat_reduce = pca.transform(mat) | |
embedding = umap.UMAP(n_neighbors=5, | |
min_dist=0.5, | |
metric='euclidean').fit_transform(mat_reduce) | |
plt.figure(figsize=(15,15)) | |
plt.scatter(embedding[:,0],embedding[:,1],s=0.2); | |
plt.title('Naive clustering'); |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#basically like at https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html, but our data is reall | |
#prepre paramets | |
params = {'quantile': .3, | |
'eps': .3, | |
'damping': .9, | |
'preference': -200, | |
'n_neighbors': 10, | |
'n_clusters': 5} | |
bandwidth = estimate_bandwidth(embedding, quantile=params['quantile']) | |
connectivity = kneighbors_graph( |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
from torch.autograd import Variable | |
from torchvision import datasets, transforms | |
from torch.optim import Optimizer | |
from torch.utils import data | |
class DataGenerator(data.Dataset): |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
from torch.autograd import Variable | |
from torchvision import datasets, transforms | |
from torch.optim import Optimizer | |
from torch.utils import data | |
import pretrainedmodels |
OlderNewer