ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information

SUBMITTER: Heydari AA 

PROVIDER: S-EPMC9004654 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature