Rxivist logo

One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data

By Chloe Xueqi Wang, Lin Zhang, Bo Wang

Posted 13 May 2021
bioRxiv DOI: 10.1101/2021.05.12.443814

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.

Download data

  • Downloaded 389 times
  • Download rankings, all-time:
    • Site-wide: 76,854
    • In genomics: 5,160
  • Year to date:
    • Site-wide: 10,799
  • Since beginning of last month:
    • Site-wide: 29,862

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

News