Mass spectrometry (MS)-based lipidomics is revolutionizing lipid research with high throughput identification and quantification of hundreds to thousands of lipids with the goal of elucidating lipid metabolism and function. Estimates of statistical confidence in lipid identification are essential for downstream data interpretation in a biological context. In the related field of proteomics, a variety of methods for estimating false-discovery are available, and understanding the statistical confidence of identifications is typically required for data analysis and hypothesis testing. However, there is no current method for estimating the false discovery rate (FDR) or statistical confidence for MS-based lipid identifications. This has slowed the adoption of MS-based lipidomics research, as all identifications require manual inspection and validation to ensure their accuracy. We present here the first generalizable method for FDR estimation, a target/decoy approach, that allows those conducting MS-based lipidomics research to confidently adjust spectral score thresholds to minimize false discovery and to enable full automation of data analysis.

A generalizable method for false-discovery rate estimation in mass spectrometry-based lipidomics