MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC-MS/MS Data Analysis

Untargeted mass spectrometry is a robust tool for biology,butit usually requires a large amount of time on data analysis, especiallyfor system biology. A framework called Multiple-Chemical nebula (MCnebula)was developed herein to facilitate the LC-MS data analysisprocess by focusing on critical chemical classes and visualizationin multiple dimensions. This framework consists of three vital stepsas follows: (1) abundance-based classes (ABC) selection algorithm,(2) critical chemical classes to classify "features"(corresponding to compounds), and (3) visualization as multiple Child-Nebulae(network graph) with annotation, chemical classification, and structure.Notably, MCnebula can be used to explore the classification and structuralcharacteristic of unknown compounds beyond the limit of the spectrallibrary. Moreover, it is intuitive and convenient for pathway analysisand biomarker discovery because of its function of ABC selection andvisualization. MCnebula was implemented in the R language. A seriesof tools in R packages were provided to facilitate downstream analysisin an MCnebula-featured way, including feature selection, homologytracing of top features, pathway enrichment analysis, heat map clusteringanalysis, spectral visualization analysis, chemical information query,and output analysis reports. The broad utility of MCnebula was illustratedby a human-derived serum data set for metabolomics analysis. The resultsindicated that "Acyl carnitines" were screened out bytracing structural classes of biomarkers, which was consistent withthe reference. A plant-derived data set was investigated to achievea rapid annotation and discovery of compounds in E. ulmoides.