PT  - JOURNAL ARTICLE
AU  - Chloe X. Wang
AU  - Lin Zhang
AU  - Bo Wang
TI  - One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data
AID  - 10.1101/2021.05.12.443814
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.12.443814
4099  - http://biorxiv.org/content/early/2021/07/16/2021.05.12.443814.short
4100  - http://biorxiv.org/content/early/2021/07/16/2021.05.12.443814.full
AB  - The surge of single-cell RNA sequencing technologies gives rise to the abundance of large single-cell RNA-seq datasets at the scale of hundreds of thousands of single cells. Integrative analysis of large-scale scRNA-seq datasets has the potential of revealing de novo cell types as well as aggregating biological information. However, most existing methods fail to integrate multiple large-scale scRNA-seq datasets in a computational and memory efficient way. We hereby propose OCAT, One Cell At a Time, a graph-based method that sparsely encodes single-cell gene expressions to integrate data from multiple sources without most variable gene selection or explicit batch effect correction. We demonstrate that OCAT efficiently integrates multiple scRNA-seq datasets and achieves the state-of-the-art performance in cell type clustering, especially in challenging scenarios of non-overlapping cell types. In addition, OCAT efficaciously facilitates a variety of downstream analyses, such as differential gene analysis, trajectory inference, pseudotime inference and cell inference. OCAT is a unifying tool to simplify and expedite the analysis of large-scale scRNA-seq data from heterogeneous sources.Competing Interest StatementThe authors have declared no competing interest.