CASi: A Framework for Cross-Timepoint Analysis of Single-Cell RNA Sequencing Data

Authors

Yizhuo Wang
Christopher R Flowers
Michael Wang
Xuelin Huang
Ziyi Li

Publication Date

5-9-2024

Journal

Scientific Reports

Abstract

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes.

Keywords

Single-Cell Analysis, Sequence Analysis, RNA, Humans, Gene Expression Profiling, Software, Computational Biology

DOI

10.1038/s41598-024-58566-x

PMID

38724550

PMCID

PMC11082156

PubMedCentral® Posted Date

5-9-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes