Similarly to other high-throughput technologies, metabolomics usually faces a data mining challenge to provide an understandable and useful output to advance in biomarker discovery and precision medicine. Biological interpretation of the results is one of the hard points and several bioinformatics tools have emerged to simplify and improve this step. However, sometimes these tools accept only very simplistic data structures and, for example, they do not even accept data with several covariates.
POMA is a free, friendly and fast Shiny interface for analysing and visualization data after an analytical targeted metabolomics process and its hosted on https://polcastellano.shinyapps.io/POMA/.
POMA allows the user to go from the raw data to statistical analysis. The analysis is organized in three blocks: “Load Data” (where user can upload metabolite data and a covariates file), “Pre-processing” (value imputation and normalization) and “Statistical analysis” (univariate and multivariate methods, limma, correlation analysis, feature selection methods, random forest, etc.). These steps include multiple types of interactive data visualization integrated in an intuitive user interface that requires no programming skills. Finally, POMA also generates different automatic statistical and exploratory reports to facilitate the analysis and interpretation of the results.
