1. One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data
- Author
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Wang C, Li Zhang, and Baoju Wang
- Subjects
Variable (computer science) ,Gene selection ,Computer science ,Graph (abstract data type) ,Inference ,RNA-Seq ,Batch effect ,Data mining ,Cluster analysis ,Scale (map) ,computer.software_genre ,computer - Abstract
1AbstractThe 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.
- Published
- 2021
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