Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system

Abstract

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month’s samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch. Graphical abstract

Multi-modal profiling of biostabilized human skin modules reveals a coordinated ecosystem response to injected mRNA-1273 COVID-19 vaccine

“…A variance stabilization was performed on gene count using the vst function, to plot samples on PCA. Donor and gender biases were corrected using Combat-seq 31 from R library sva 32 version 3.48 with default settings. Corrected count matrices were analyzed with DESeq2 version 1.40.2.…”

“…Taken together, these findings strongly suggest that immune and/or structural cells, which naturally reside in natural human skin, maintain the ability to release immunomodulatory substances in response We next performed bulk RNAseq analyses to characterize sequential changes in gene expression profiles at the tissue level, at 8h and 24h after s.c. administration of the COVID-19 vaccine or control clinicalgrade water. As potential inter-individual variations and gender-based differences can introduce experimental biases, we applied a well-established Combat-Seq batch effect adjustment tool from R library sva version 3.48 31,32 . We next analyzed our datasets and detected 65 and 332 differentially expressed genes (DEGs) at 8h and 24h, respectively, after injection of the vaccine (Fig.…”