Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.

ABSTRACT:

Motivation

Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can elimin

SUBMITTER: Lei H 

PROVIDER: S-EPMC9235482 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature