This case study shows how to use DIABLO to integrate mRNA, miRNA, and proteomics data from breast cancer patients. DIABLO helps identify a multi-omics signature that discriminates cancer subtypes (Basal, Her2, and LumA) by finding correlated and predictive features across datasets. You’ll learn how to tune the model, explore the relationships between features, and interpret sample groupings.
🔍 More on DIABLO
📄 Download R script
Data used on this page:breast.TGCA
Key functions used on this page:pls()block.splsda()
perf()
tune()
selectVar()
plotDiablo()plotIndiv()
plotArrow()plotVar()
circosPlot()
network()
plotLoadings()
cimDiablo()
auroc()
predict()
Note:
This case study uses a subset of the breast TGCA dataset, the full data set which includes methylation data and 4 breast cancer subtypes can be downloaded here.
References:
1. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, Van De Rijn M, Jeffrey SS, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences, 98(19):10869–10874, 2001.
2. Cancer Genome Atlas Network et al. Comprehensive molecular portraits of human breast tumours. Nature, 490(7418):61–70, 2012.
3. Gonzalez, I, Le Cao, K.A., Davis, M.J. and Dejean, S., 2012. Visualising associations between paired ‘omics’ data sets. BioData mining, 5(1)
4. Singh, A, Gautier, B, Shannon, CP, Vacher, M, Rohart, F, Tebutt, SJ. and Le Cao, KA, 2016. DIABLO-an integrative, multi-omics, multivariate method for multi-group classification. bioRxiv, p.067611.
5. Rohart F, Gautier B, Singh A, Lê Cao K-A (2017). mixOmics: an R package for ‘omics feature selection and multiple data integration.
6. Rohart F, Mason EA, Matigian N, Mosbergen R, Korn O, Chen T, Butcher S, Patel J, Atkinson K, Khosrotehrani K, Fisk NM, Lê Cao K-A&, Wells CA& (2016). A Molecular Classification of Human Mesenchymal Stromal Cells, PeerJ 4:e1845