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

Author

Listed:
  • 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
    Pennsylvania State University)

  • Qunhua Li

    (Pennsylvania State University
    Pennsylvania State University)

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.

Suggested Citation

  • Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34360-z
    DOI: 10.1038/s41467-022-34360-z
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    References listed on IDEAS

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