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Reply to ‘Inflated performance measures in enhancer–promoter interaction-prediction methods’

Nature Genetics volume 51, pages 1198–1200 (2019)Cite this article

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First, let us elaborate on how enhancer–promoter interaction data can violate the IID assumption. The unit of measurement here is an enhancer–promoter pair, upon which many genomic features are observed. Dependence arises from (i) promoters interacting with more than one enhancer, (ii) enhancers interacting with more than one promoter and (iii) genomic features, such as broad histone modification peaks, that overlap more than one enhancer–promoter pair, primarily because of proximal clusters of regulatory elements. In the published TargetFinder training data, promiscuous promoters interacting with many enhancers are the major contributor to dependence between pairs. Regarding the identically distributed assumption, we note that genomic loci have different types and levels of genomic features, as do different cell lines. Consequently, enhancer–promoter pairs that are independent (for example, on different chromosomes) may not be identically distributed. Thus, both parts of the IID assumption may be violated, and data more consistent with one part of the assumption may be less consistent with the other.

Violations of the IID assumption are important for several reasons. At the most fundamental level, the mathematics underlying statistical models—including parameter estimators, hypothesis tests and optimality of cross-validation for performance evaluation—depends on the IID assumption. Furthermore, when evaluating a machine-learning model by using standard k-fold CV, in which samples are randomly shuffled before repeated splitting into training and test sets, biological correlation causes information to cross the training and test divide. This process can inflate performance metrics and obscure over-fitting, thus resulting in models that may not generalize to new contexts. These problems are serious when the primary aim of the machine learning is to provide a tool for novel predictions (for example, in silico Hi-C). When the goal is to learn something about relationships between variables and to shed light on mechanisms underlying patterns in the data, as in our TargetFinder paper, the importance of over-fitting and inflated performance depends on whether these problems obscure the biological signal.

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Fig. 1: Corrected performance across and within cell types.
Fig. 2: Corrected performance within cell types only.

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Authors and Affiliations

  1. Gladstone Institutes, San Francisco, CA, USA

    Sean Whalen & Katherine S. Pollard

  2. Department of Epidemiology and Biostatistics, Institute for Human Genetics, Quantitative Biology Institute, and Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA

    Katherine S. Pollard

  3. Chan-Zuckerberg Biohub, San Francisco, CA, USA

    Katherine S. Pollard

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Correspondence to Katherine S. Pollard.

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Whalen, S., Pollard, K.S. Reply to ‘Inflated performance measures in enhancer–promoter interaction-prediction methods’. Nat Genet 51, 1198–1200 (2019). https://doi.org/10.1038/s41588-019-0473-0

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