scsampler 1.0.2

A package for fast diversity-preserving subsampling of large-scale single-cell transcriptomic data

scSampler

Overview

scSampler is a Python pacakge for fast diversity-preserving subsampling of large-scale single-cell transcriptomic data.

Installation

Please install it from PyPI:

pip install scsampler

Quick start

First we load all modules.

import numpy as np
import pandas as pd
import scanpy as sc
from time import time
from scsampler import scsampler
sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')

Read in data

The example data can be downloaded from https://doi.org/10.5281/zenodo.5811787 in the anndata format by scanpy. Here we use the ~68'000 PBMC cells. Please modify the path as your own path.

adata = sc.read_h5ad('/home/dongyuan/scSampler/data/final_h5ad/pbmc68k.h5ad')

anndata as input

Subsample 10% cells and return a new anndata. The space is top PCs.

adata_sub = scsampler(adata, fraction = 0.1, copy = True) 

If you want to speed it up, you can use the random_split. It will lead to slightly less optimal result, of course.

start = time()
adata_sub = scsampler(adata, fraction = 0.1, obsm = 'X_pca', copy = True, random_split = 16)
end = time()
print(end - start)

matrix as input

You can also use the numpy.ndarray as the input.

mat = adata.obsm['X_pca']
print(type(mat))
res = scsampler(mat, fraction = 0.1, copy = True, random_split = 16)
subsample_index = res[1]
subsample_mat = res[0]

Contact

Any questions or suggestions on scSampler are welcomed! If you have any questions, please report it on issues or contact Dongyuan ([e-mail]).