LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data

Abstract

LSH-GAN is a locality-sensitive hashing based generative adversarial model that can produce realistic cell samples from small sample single-cell scRNA-seq data. The generated cells can be utilized for downstream analysis, like gene selection and cell clustering.