MetCohort: Precise Feature Detection and Correspondence for Untargeted Metabolomics in Large-Scale Cohort Studies

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics is becoming increasingly popular in large-scale cohort studies. However, its data processing is complex and challenging. We present MetCohort, a computational tool for performing metabolomics raw data alignment for large-scale sample analysis, and accurate feature detection and quantification. By combining chromatogram profile alignment and local anchor matching with an outlier removal algorithm, the retention times of the raw data were aligned. With aligned retention times across all the samples, regions of interest (ROIs) are detected and stacked among samples to form a two-dimensional (2D) ROI-matrix. This 2D ROI-matrix, resembling an image with rows representing samples and columns corresponding to the time, allows the application of image processing techniques. Since the peaks are already aligned in the alignment step, features can be accurately detected and quantified with automatic correspondence of all the samples. Based on the 2D image processing technique, holistic scale feature detection is performed, which not only significantly decreases the number of false-positives and improves the detection of low-intensity compounds, but also avoids tricky peak matching and quantification uncertainty. Overall, MetCohort has potential to enhance the accuracy and efficiency of data processing in large-scale LC-HRMS.