Analysing modern multi-omics datasets require substantial programming expertise and practical knowledge. Analysis is limited by technical expertise, making it challenging for many researchers to apply modern statistical best practices. MultiScholaR is a package in R that addresses this challenge by providing a novel pipeline aiming to enable users to perform comprehensive multi-omics analyses, including single-omic analyses (e.g. transcriptomics, proteomics, metabolomics, and lipidomics datasets) and integrative multi-omics analysis. Through well-documented workflow templates, researchers can systematically apply best practice to all steps of integrative multi-omics analysis.
MultiScholaR implements stringent quality control measures for multi-omics analysis by incorporating criteria such as false discovery rate thresholds, filtering criteria, and missing value limitations across samples. It integrates several sophisticated analytical tools: the IQ tool for peptide-to-protein quantitative data summarization¹, RUV-III-C for removing unwanted variation², and edgeR3 or limma4 for sample normalization and linear modelling. Pathway analysis can be performed either using custom annotations via clusterProfiler5 or through automated analysis with gProfiler26. Multivariates and integrative multi-omics analyses were implemented using MOFA+7.
Structured on modular, object-oriented components, MultiScholaR's architecture facilitates easy integration of new tools as they emerge. The graphical user interface is easy to use and provides step-by-step guides for user to go through each analytical step, ad provide analysis reports and configuration files to support reproducibility and enabling public sharing of analyses. We demonstrate the pipeline's capabilities through the analysis of published data examining the analysis of quantitative transcriptomics, proteomics, and metabolomics data from bacteria that causes sepsis as they adapt to growth in sera.8
By streamlining complex multi-omics analyses, MultiScholaR makes advanced analytical techniques accessible to researchers across all levels of programming expertise. The complete library and GUI will be available as an R package via https://github.com/APAF-bioinformatics/MultiScholaR
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2) Poulos et al. 2020 Nature communications 11(1):3793.
3) Ritchie et al. 2015 Nucleic Acids Res. 43(7):e47.
4) Chen et al. 2025 Nucleic Acids Research, 53(2):gkaf018.
5) Wu et al. The Innovation, 2(3):100141.
6) Kolberg et al. 2020 F1000Research, 9(ELIXIR):709.
7) Argelaguet et al. 2020 Genome Biol. 21(1):111.
8) Mu et al. 2023 Nat Commun. 14(1):1530