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,
but
it usually requires a large amount of time on data analysis, especially
for system biology. A framework called Multiple-Chemical nebula (MCnebula)
was developed herein to facilitate the LC–MS data analysis
process by focusing on critical chemical classes and visualization
in multiple dimensions. This framework consists of three vital steps
as 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 structural
characteristic of unknown compounds beyond the limit of the spectral
library. Moreover, it is intuitive and convenient for pathway analysis
and biomarker discovery because of its function of ABC selection and
visualization. MCnebula was implemented in the R language. A series
of tools in R packages were provided to facilitate downstream analysis
in an MCnebula-featured way, including feature selection, homology
tracing of top features, pathway enrichment analysis, heat map clustering
analysis, spectral visualization analysis, chemical information query,
and output analysis reports. The broad utility of MCnebula was illustrated
by a human-derived serum data set for metabolomics analysis. The results
indicated that “Acyl carnitines” were screened out by
tracing structural classes of biomarkers, which was consistent with
the reference. A plant-derived data set was investigated to achieve
a rapid annotation and discovery of compounds in E. ulmoides.