The P-integration framework (referred to as MINT – Multivariate INTegration) integrates several datasets measured on the same types of variables. This method enhances statistical power by controlling for batch effects before applying standard multivariate analysis techniques like (s)PLS or (s)PLS-DA. MINT is useful for integrating homogeneous `omics data across different studies, enabling data sharing and re-use. It has supervised (e.g. PLS-DA) and unsupervised (e.g. PLS) variants, allowing for both classification of novel samples and identification of correlated features between datasets. Additionally, it has sparse approaches to select the most relevant features.
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MINT-type questions:
– Can I combine the datasets whilst accounting for the variation between studies?
– Can I discriminate the samples based on their outcome category?
– Which variables are discriminative across all studies?
– Can they constitute a signature that predicts the class of unseen samples?
Data used on this page:stemcells
Key functions used on this page:mint.plsda()mint.splsda()plotIndiv()plotVar()selectVar()plotLoadings()
Related case studies:
Case Study: MINT Stem Cells
References:
1. Rohart, F., Mason, E. A., Matigian, N., Mosbergen, R., Korn, O., Chen, T., Butcher, S., Patel, J., Atkinson, K., Khosrotehrani, K., Fisk, N. M., Lê Cao, K., and Wells, C. A. (2016). A molecular classification of human mesenchymal stromal cells. PeerJ, 4, e1845.
2. Rohart, F., Eslami, A., Matigian, N. et al. MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms. BMC Bioinformatics 18, 128 (2017). https://doi.org/10.1186/s12859-017-1553-8.
Publications where MINT has been applied:
1. Poirier S, Déjean, S, Midoux, C, Lê Cao K-A, Chapleur O (2020). Integrating independent microbial studies to build predictive models of anaerobic digestion inhibition by ammonia and phenol. Bioresource Technology 316:123952
2. Tian L, Dong X, Freytag S, Lê Cao K-A, Su S, Abadi AJ, Amann-Zalcenstein D, Weber TS, Seidi S, Jabbari JS, Naik S, Ritchie ME (2019). Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nature methods. Jun;16(6):479-487