Batch Effect Correction to Enhance Utility of a Publicly Available ALS Proteomics Dataset

Abstract

Large, publicly available databases of patient data have been successful in identifying associations between neurodegenerative diseases and biological features that might otherwise go unnoticed. The Answer ALS Consortium is one such database that has generated a rich repository of multi-omic data from people with amyotrophic lateral sclerosis (ALS). However, large studies can suffer from batch effects that obscure genuine biological signal and prevent experiment reproducibility. In this study, we identified a batch effect in the Answer ALS 1-2 proteomic dataset (508 samples x 3,643 proteins) and corrected the effect using both the internal controls provided by Answer ALS and the batch effect-correction algorithm (BECA) ComBat. We identified a batch effect in the Answer ALS Dataset 1-2 proteomic dataset that, uncorrected, may obscure biology. Proteins MOS and H4 were outliers of expression in the dataset that correlated with the batch effect. Removing MOS and H4 did not correct the effect fully but did reveal new principal component (PC) contributing proteins related to motor neurons and potentially ALS: AINX, NFL, and NFM. The batch differential controls (BDCs) provided by Answer ALS were used to correct samples in their respective batches. ComBat, a well-characterized BECA, was applied to the dataset both before (ComBat-BDC) and after (BDC-ComBat) correction with the BDCs. BDC-ComBat was the only method that successfully reduced batch effect across the entire dataset. This method also preserved four of the top six PC-contributing proteins from the original dataset, including three neuronal proteins, suggesting that the corrected dataset recapitulates biology as well as the variability of the original dataset. Altogether, we produced a version of the Answer ALS proteomic dataset (354 samples by 3,641 proteins) with globally reduced batch effect and protein-level reduced batch effect. This version of the dataset recapitulates the individual protein variability and important neuronal proteins of the original. We also created a workflow for identifying and correcting batch effects that can be applied to other large data repositories.