A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the kn

SUBMITTER: Wilkinson DC 

PROVIDER: S-EPMC11457179 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature