Correcting for experiment-specific variability in expression compendia can remove underlying signals

Abstract

Motivation

In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or "batch effects," may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined.

Objective

We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments.

Method

We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability.

Results

The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal.

Conclusion

When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.

Now published in GigaScience doi: 10.1093/gigascience/giaa117

Alexandra J. Lee 1Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA2Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Alexandra J. LeeYoSon Park 2Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for YoSon ParkGeorgia Doing 3Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth, Hanover, NH, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Georgia DoingDeborah A. Hogan 3Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth, Hanover, NH, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Deborah A. HoganCasey S. Greene 2Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA4Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Casey S. GreeneFor correspondence: greenescientist@gmail.com

A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa117 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

These peer reviews were as follows:

Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102456 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102454

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