TY - JOUR
T1 - CLIMB
T2 - High-dimensional association detection in large scale genomic data
AU - Koch, Hillary
AU - Keller, Cheryl A.
AU - Xiang, Guanjue
AU - Giardine, Belinda
AU - Zhang, Feipeng
AU - Wang, Yicheng
AU - Hardison, Ross C.
AU - Li, Qunhua
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85141661384&partnerID=8YFLogxK
U2 - 10.1038/s41467-022-34360-z
DO - 10.1038/s41467-022-34360-z
M3 - Article
C2 - 36371401
AN - SCOPUS:85141661384
SN - 2041-1723
VL - 13
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 6874
ER -