CLIMB: High-dimensional association detection in large scale genomic data

Hillary Koch, Cheryl A. Keller, Guanjue Xiang, Belinda Giardine, Feipeng Zhang, Yicheng Wang, Ross C. Hardison () and Qunhua Li ()
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Hillary Koch: Pennsylvania State University
Cheryl A. Keller: Pennsylvania State University
Guanjue Xiang: Pennsylvania State University
Belinda Giardine: Pennsylvania State University
Feipeng Zhang: Xi’an Jiaotong University
Yicheng Wang: University of British Columbia
Ross C. Hardison: Pennsylvania State University
Qunhua Li: Pennsylvania State University

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

Date: 2022
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DOI: 10.1038/s41467-022-34360-z

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