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Abstract
BackgroundCorrecting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher.ResultsWe describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framework includes three steps: (1) data adjustment with the desired methods (2) calculating gene-gene co-expression measurements for adjusted datasets (3) evaluating the performance of the co-expression measurements against a gold standard. Using the framework, we evaluated five batch correction methods applied to RNA-seq data of six representative tissue datasets derived from the GTEx project.ConclusionsOur framework enables the evaluation of batch correction methods to better preserve the original biological signal. We show that using a multiple linear regression model to correct for known confounders outperforms factor analysis-based methods that estimate hidden confounders. The code is publicly available as an R package.
Highlights
Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task
Batch correction approaches can be roughly divided into three categories: (1) those aimed at removing known covariates, e.g., ComBat [4], which applies an Somekh et al BMC Bioinformatics (2019) 20:268 empirical Bayes approach, (2) those aimed at removing unknown covariates, e.g., inferring hidden covariates using principal components [5] or factor analysis [6], and (3) those aimed at removing both known and unknown covariates
The B-CeF assessment framework uses a-priori gene-gene true and false associations to evaluate the effectiveness of batch correction methods to preserve meaningful biological signals
Summary
Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task Attempting to remove these batch effects will result in some biologically meaningful signals being lost. The RUV-2 method [11] suggested adjusting for batch effects using the variation between conditions of a-priori negative control genes known not to be altered and related to the biological factor of interest (i.e., not differentially expressed). The negative control genes were incorporated into a linear regression model to adjust for unwanted variation in a dataset resulting from batch effects. These methods are dedicated to a downstream differential expression analysis that takes into account the differential biological variation between the contrasted groups supervising their computation. This makes it less than intuitive to be utilized for the unsupervised batch correction computation required for a downstream co-expression analysis
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