Comprehensive Statistical Evaluation of LC-MS Data

LC-MS-based metabolomics applications in drug discovery and development, clinical research, proteomics and food science all share the need for quick analysis of a large number of highly complex samples. These techniques require pattern recognition techniques that filter the relevant information from different sample groups and detect compounds that differentiate samples.

ProfileAnalysis enables smooth and easy analysis using fully unsupervised methods like Principal Component Analysis (PCA) and supervised methods such as PLS or student’s t-test. It provides a complete set of tools for data pre-processing, in-depth statistical analysis, identification and feedback experiments.

ProfileAnalysis has been designed to give beginners a head start in metabolomics and also to meet the needs of expert users.