How Quality Control and Normalization Shape Reliable Metabolomics Data Analysis
If you’ve ever worked with metabolomics data, you already know the truth: collecting samples and running instruments is only half the battle. The real story begins after the spectra are generated when thousands of peaks, signals, and variables need to be cleaned, corrected, and interpreted. Without careful quality control and normalization, even the most advanced…