Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.

ABSTRACT: Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a

SUBMITTER: Franchini M 

PROVIDER: S-EPMC9985338 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature