Speedup (~20x) of scanpy.pp.regress_out function using Linear Least Square method.

[ashish615]

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• [ ] Tests included or not required because:

• [ ] Release notes not necessary because:

Hi, We are submitting PR for speed up of the regress_out function. Here we finding coefficient using Linear regression (Linear Least Squares) rather then GLM for non categorical data.

	Time(sec)
Original	297
Updated	14.91
Speedup	19.91

experiment setup : AWS r7i.24xlarge

import time
import numpy as np

import pandas as pd

import scanpy as sc
from sklearn.cluster import KMeans

import os
import wget

import warnings



warnings.filterwarnings('ignore', 'Expected ')
warnings.simplefilter('ignore')
input_file = "./1M_brain_cells_10X.sparse.h5ad"

if not os.path.exists(input_file):
    print('Downloading import file...')
    wget.download('https://rapids-single-cell-examples.s3.us-east-2.amazonaws.com/1M_brain_cells_10X.sparse.h5ad',input_file)


# marker genes
MITO_GENE_PREFIX = "mt-" # Prefix for mitochondrial genes to regress out
markers = ["Stmn2", "Hes1", "Olig1"] # Marker genes for visualization

# filtering cells
min_genes_per_cell = 200 # Filter out cells with fewer genes than this expressed
max_genes_per_cell = 6000 # Filter out cells with more genes than this expressed

# filtering genes
min_cells_per_gene = 1 # Filter out genes expressed in fewer cells than this
n_top_genes = 4000 # Number of highly variable genes to retain

# PCA
n_components = 50 # Number of principal components to compute

# t-SNE
tsne_n_pcs = 20 # Number of principal components to use for t-SNE

# k-means
k = 35 # Number of clusters for k-means

# Gene ranking

ranking_n_top_genes = 50 # Number of differential genes to compute for each cluster

# Number of parallel jobs
sc._settings.ScanpyConfig.n_jobs = os.cpu_count()

start=time.time()
tr=time.time()
adata = sc.read(input_file)
adata.var_names_make_unique()
adata.shape
print("Total read time : %s" % (time.time()-tr))



tr=time.time()
# To reduce the number of cells:
USE_FIRST_N_CELLS = 1300000
adata = adata[0:USE_FIRST_N_CELLS]
adata.shape

sc.pp.filter_cells(adata, min_genes=min_genes_per_cell)
sc.pp.filter_cells(adata, max_genes=max_genes_per_cell)
sc.pp.filter_genes(adata, min_cells=min_cells_per_gene)
sc.pp.normalize_total(adata, target_sum=1e4)
print("Total filter and normalize time : %s" % (time.time()-tr))


tr=time.time()
sc.pp.log1p(adata)
print("Total log time : %s" % (time.time()-tr))


# Select highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=n_top_genes, flavor = "cell_ranger")

# Retain marker gene expression
for marker in markers:
        adata.obs[marker + "_raw"] = adata.X[:, adata.var.index == marker].toarray().ravel()

# Filter matrix to only variable genes
adata = adata[:, adata.var.highly_variable]

#Regress out confounding factors (number of counts, mitochondrial gene expression)
mito_genes = adata.var_names.str.startswith(MITO_GENE_PREFIX)
n_counts = np.array(adata.X.sum(axis=1))
adata.obs['percent_mito'] = np.array(np.sum(adata[:, mito_genes].X, axis=1)) / n_counts
adata.obs['n_counts'] = n_counts

ts=time.time()
sc.pp.regress_out(adata, ['n_counts', 'percent_mito'])
print("Total regress out time : %s" % (time.time()-ts))