MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics

Abstract

BackgroundInterpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve.ResultsICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained.ConclusionsCorrelating the components obtained with prior biological knowledge allows understanding how non-targeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://github.com/daniellyz/MetICA.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0970-4) contains supplementary material, which is available to authorized users.

Granular Transcriptomic Signatures Derived from Independent Component Analysis of Bulk Nervous Tissue for Studying Labile Brain Physiologies

“…For complex biological processes such as those of the CNS or systemic aging, it behooves researchers to disentangle these processes into more manageable pieces; pieces emerging from the inherent structure of the data. Mutually independent "omic-modules" defined by their feature to input data relationships may prove essential for synthesizing a holistic description of biology (Liu et al, 2016). We struggled to define the role of some of our fly modules in part due to poor documentation of sample preparation procedures and the exact genotypes utilized; deficiencies that could have been attenuated (data not shown).…”

Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

“…A new ICA approach called MetICA was applied to experimental mass spectrometry (MS)-based, non-targeted metabolomics data, which allowed to understand how non-targeted metabolomics data reflect biological nature and technical phenomena. The authors concluded that an optimal ICA model should be selected by optimizing the number of reliable components instead of just trying to fit the data [54].…”

“…It is clear that the range of applications of ICA to different types of chromatographic profiles should increase in the near future similarly to the alternative technique, MCR-ALS, which is widely used in chromatographic data analysis [45][46][47][48][49][50][51][52][53][54][55][56][57].…”

Towards a holistic and solution-oriented monitoring of chemical status of European water bodies: how to support the EU strategy for a non-toxic environment?

“…Open digital repositories can be used to archive current-state full scan HRMS analysis for retrospective and joint exploitation [ 53 , 54 ]. At the same time, a bunch of bio-informatics tools including innovative clustering algorithms from metabolomics and genomics including independent component analysis (ICA) and cytoscape/ClueGO pathway analysis [ 55 , 56 ], hierarchical clustering, K -means, self-organizing maps and fuzzy clustering [ 57 , 58 ] are available and help unravel big and complex analytical datasets. It may be assumed that complex environmental mixtures in water bodies are not just randomly composed but the result of confluence of source-related patterns modified by environmental fate.…”