ArticleCommunications biology2022
LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A graph-attentive GAN for rare-cell-aware single-cell RNA-seq data generation.PLoS computational biology · 2026Article
- RareCapsNet: An explainable capsule network enables robust discovery of rare cell populations from large-scale single-cell transcriptomics.PLoS computational biology · 2026Article
- Review
- A framework for privacy-preserving similarity search of massive multi-party genomic data.Frontiers in plant science · 2025Article
- cfDiffusion: diffusion-based efficient generation of high quality scRNA-seq data with classifier-free guidance.Briefings in bioinformatics · 2024Article
- scDiffusion: conditional generation of high-quality single-cell data using diffusion model.Bioinformatics (Oxford, England) · 2024Article
- Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans.BMC bioinformatics · 2024Article
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Abstract
A fundamental problem of downstream analysis of scRNA-seq data is the unavailability of enough cell samples compare to the feature size. This is mostly due to the budgetary constraint of single cell experiments or simply because of the small number of available patient samples. Here, we present an improved version of generative adversarial network (GAN) called LSH-GAN to address this issue by producing new realistic cell samples. We update the training procedure of the generator of GAN using locality sensitive hashing which speeds up the sample generation, thus maintains the feasibility of applying the standard procedures of downstream analysis. LSH-GAN outperforms the benchmarks for realistic generation of quality cell samples. Experimental results show that generated samples of LSH-GAN improves the performance of the downstream analysis such as feature (gene) selection and cell clustering. Overall, LSH-GAN therefore addressed the key challenges of small sample scRNA-seq data analysis.
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