An Effective Biclustering-based Framework for Identifying Cell Subpopulations from scRNA-seq Data.

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ID: 101183
2020
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Ranked #15 of 27 articles by views in ieee/acm transactions on computational biology and bioinformatics

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Abstract
The advent of single-cell RNA sequencing (scRNA-seq) techniques opens up new opportunities for studying the cell-specific changes in the transcriptomic data. An important research problem related with scRNA-seq data analysis is to identify cell subpopulations with distinct functions. However, the expression profiles of individual cells are usually measured over tens of thousands of genes, and it remains a difficult problem to effectively cluster the cells based on the high-dimensional profiles. An additional challenge of performing the analysis is that, the scRNA-seq data are often noisy and sometimes extremely sparse due to technical limitations and sampling deficiencies. In this paper, we propose a biclustering-based framework called DivBiclust that effectively identifies the cell subpopulations based on the high-dimensional noisy scRNA-seq data. Compared with nine state-of-the-art methods, DivBiclust excels in identifying cell subpopulations with high accuracy as evidenced by our experiments on ten real scRNA-seq datasets with different size and diverse dropout rates. The supplemental materials of DivBiclust, including the source codes, data, and a supplementary document, are available at https://www.github.com/Qiong-Fang/DivBiclust.
Reference Key
fang2020anieeeacm Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fang, Qiong;Su, Dewei;Ng, Wilfred;Feng, Jianlin;
Journal ieee/acm transactions on computational biology and bioinformatics
Year 2020
DOI
10.1109/TCBB.2020.2979717
URL
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