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

Comparisons among experimental results with large amounts of data can be more precise and meaningful when done across multiple different conditions simultaneously. Koch et al. introduce a method, called CLIMB, that does this, and captures interpretable and biologically meaningful information. 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.

Automated summary

Comparing experimental results across multiple different conditions can be more precise and meaningful. Researchers have introduced a method called CLIMB that allows for this type of comparison and captures interpretable and biologically meaningful information.