Biology

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data

B. Ranjan, W. Sun, et al.

Discover DUBStepR, a groundbreaking feature selection algorithm that enhances the accuracy of single-cell data clustering by utilizing gene-gene correlations. This innovative research, conducted by renowned authors, outperforms existing methods and expertly deconvolves cell heterogeneity in rheumatoid arthritis patient data. Its scalability makes it an essential tool for analyzing large datasets across various data types.

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Abstract

Feature selection is crucial for accurate single-cell data clustering. Existing methods are inconsistent and ignore gene-gene correlations. DUBStepR, a novel feature selection algorithm, leverages gene-gene correlations and a Density Index (DI) to select informative genes. It outperforms existing methods in various benchmarks and robustly deconvolves T and NK cell heterogeneity in rheumatoid arthritis patient data. DUBStepR is scalable to large datasets and applicable to other data types like single-cell ATAC-seq.

Publisher

NATURE COMMUNICATIONS

Published On

Oct 06, 2021

Authors

Bobby Ranjan, Wenjie Sun, Jinyu Park, Kunal Mishra, Florian Schmidt, Ronald Xie, Fatemeh Alipour, Vipul Singhal, Ignasius Joanito, Mohammad Amin Honardoost, Jacy Mei Yun Yong, Ee Tzun Koh, Khai Pang Leong, Nirmala Arul Rayan, Michelle Gek Liang Lim, Shyam Prabhakar

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