Removes unwanted variations at feature-level in large-scale metabolomics and proteomics data. QC-RFSC is an algorithm that integrates the random forest (RF) based ensemble learning approach to learn the unwanted variations from quality control (QC) samples. It also predicts the correction factor in the neighboring real samples responses. Beside metabolomics data analysis, this method significantly improves the data quality for the proteomics.
Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China; Max Planck Institute for Terrestrial Microbiology & LOEWE Research Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
QC-RFSC funding source(s)
Supported by Hong Kong Baptist University (IRMC/13-14/03-CHE) and the National Sciences Foundation of China (NSFC21675176 and NSFC91543202).