Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using Co

SUBMITTER: Aubin RG 

PROVIDER: S-EPMC11705773 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature