ArticleBioinformatics (Oxford, England)2022
scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- SKIM: A fast sketching strategy integrated with model's dynamic-feedback for large-scale single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Benchmarking sketching methods on spatial transcriptomics data.Nucleic acids research · 2026Article
- Leveraging large language models for rare disease named entity recognition.PLOS digital health · 2026Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Cell type and cell signalling innovations underlying mammalian pregnancy.Nature ecology & evolution · 2025Article
- Article
- scValue: value-based subsampling of large-scale single-cell transcriptomic data for machine and deep learning tasks.Briefings in bioinformatics · 2025Article
- Building and analyzing metacells in single-cell genomics data.Molecular systems biology · 2024Review
- Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer.Research square · 2024Article
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4 authors.
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Abstract
summaryThe number of cells measured in single-cell transcriptomic data has grown fast in recent years. For such large-scale data, subsampling is a powerful and often necessary tool for exploratory data analysis. However, the easiest random subsampling is not ideal from the perspective of preserving rare cell types. Therefore, diversity-preserving subsampling is required for fast exploration of cell types in a large-scale dataset. Here, we propose scSampler, an algorithm for fast diversity-preserving subsampling of single-cell transcriptomic data. AVAILABILITY AND IMPLEMENTATION: scSampler is implemented in Python and is published under the MIT source license. It can be installed by "pip install scsampler" and used with the Scanpy pipline. The code is available on GitHub: https://github.com/SONGDONGYUAN1994/scsampler. An R interface is available at: https://github.com/SONGDONGYUAN1994/rscsampler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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