BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.
Publication Year:
2023
PubMed ID:
36695565
Funding Grants:
Public Summary:
Big studies that measure proteins using mass spectrometry often face technical issues called "batch effects" that can make the data unreliable. These effects come from differences in how samples are prepared or measured and can hide the true biological results. This study introduces BIRCH, an easy-to-use online tool that helps detect and fix these batch effects automatically, making the data cleaner and more trustworthy. The tool also handles missing data and checks if correction is possible. Using examples from stem cell research and COVID vaccine studies, BIRCH proves useful for improving protein data analysis, helping scientists better understand diseases and develop treatments.
Scientific Abstract:
Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.