Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat

Abstract

BackgroundGene expression profiling (GEP) via microarray analysis is a widely used tool for assessing risk and other patient diagnostics in clinical settings. However, non-biological factors such as systematic changes in sample preparation, differences in scanners, and other potential batch effects are often unavoidable in long-term studies and meta-analysis. In order to reduce the impact of batch effects on microarray data, Johnson, Rabinovic, and Li developed ComBat for use when combining batches of gene expression microarray data.We propose a modification to ComBat that centers data to the location and scale of a pre-determined, ‘gold-standard’ batch. This modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a ‘gold-standard’ batch.ResultsWe combined data from MIRT across two batches (‘Old’ and ‘New’ Kit sample preparation) as well as external data sets from the HOVON-65/GMMG-HD4 and MRC-IX trials into a combined set, first without transformation and then with both ComBat and M-ComBat transformations. Fixed and validated gene risk signatures developed at MIRT on the Old Kit standard (GEP5, GEP70, and GEP80 risk scores) were compared across these combined data sets.Both ComBat and M-ComBat eliminated all of the differences among probes caused by systematic batch effects (over 98% of all untransformed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero significant probes with ComBat and M-ComBat). The agreement in mean and distribution of risk scores, as well as the proportion of high-risk subjects identified, coincided with the ‘gold-standard’ batch more with M-ComBat than with ComBat. The performance of risk scores improved overall using either ComBat or M-Combat; however, using M-ComBat and the original, optimal risk cutoffs allowed for greater ability in our study to identify smaller cohorts of high-risk subjects.ConclusionM-ComBat is a practical modification to an accepted method that offers greater power to control the location and scale of batch-effect adjusted data. M-ComBat allows for historical models to function as intended on future samples despite known, often unavoidable systematic changes to gene expression data.

Low-Intensity Pulsed Ultrasound Enhances Angiogenesis and Ameliorates Left Ventricular Dysfunction in a Mouse Model of Acute Myocardial Infarction

“…We also analyzed the pooled data of HUVECs and human coronary artery endothelial cells after revising with ComBat analysis to eliminate the batch effects between the 2 cell types. 28 The ComBat analysis showed that a total of 1050 genes, including those of the VEGF signaling and focal adhesion pathways, were significantly altered by the LIPUS treatment ( Figure 3C; Table II in the online-only Data Supplement). The data analyzed by Ingenuity Pathway Analysis tools (Ingenuity Systems, Mountain View, CA) represent the possible involvements of focal adhesion pathway and its downstream signaling pathways ( Figure IIID in the online-only Data Supplement).…”

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

“…Alignment of raw data was performed using Novoalign (http://novocraft.com/) on mouse miRNA sequences from mirBase. Quantification was performed using multiBamCov 44 46 and batch effects corrected by ComBat 43 . After batch correction, technical replicates were averaged for further analyses.…”

“…Data were normalized using cqn 45 , which corrects for GC content and gene-length. A non-parametric version of Combat methodology 46 was used after cqn to correct for library-preparation effects.…”

“…PCA analysis indicated that, although Control and Ikaros samples separated well above batches, a batch effect was observed for different Ikaros time points (not shown). This bias was corrected by ComBat 46 and technical replicates were used to avoid confounding batches with experimental conditions. After batch correction, technical replicates were averaged for further analyses.…”

Visualization and normalization of drift effect across batches in metabolome-wide association studies

“…In particular, such drift can significantly compromise the technical precision and signal stability in large-scale studies, where the data acquisition for several hundred to thousands of biological samples needs to be done in different analytical blocks (i.e. batches) over several weeks or even months 14,15 . In this case the largest variance in the dataset may be assigned to the batch effect or experimental run order, thus hindering the real biological difference and true functional signals, leading to data misinterpretation 14,16,17 .…”