Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.

ABSTRACT: Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3' ends. The rapid development of single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, conventional gene-level analysis tools are not fully applicable to APA data, and commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as cell type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically used for APA analysis from both single-cell and spatial transcriptomics data. First, an iterative imputation method based on

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC11724721 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature