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, Qunhua Li

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Article number6874
JournalNature communications
Issue number1
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy


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