Our paper ‘Novel Multivariate Methods for Integration of Genomics and Proteomics Data: Applications in a Kidney Transplant Rejection Study‘ has just been accepted in OMICS: a journal of integrative Biology, from a collaboration with scientists from the PRevention Of Organ Failure (PROOF), University of British Columbia.
It provides a nice case study with the application of PCA, IPCA, sPLS-DA and sGCCA (now implemented in mixOmics with the function wrapper.sgcca()).
Contact us for more details if needed.
Abstract
Multi-omics research is a key ingredient of data-intensive life sciences research, permitting measurement of biological molecules at different functional levels in the same individual. For a complete picture at the biological systems level, appropriate statistical techniques must however be developed to integrate different ‘omics’ data sets (e.g., genomics and proteomics). We report here multivariate projection-based analyses approaches to genomics and proteomics data sets, using the case study of and applications to observations in kidney transplant patients who experienced an acute rejection event (n = 20) versus non-rejecting controls (n = 20). In this data sets, we show how these novel methodologies might serve as promising tools for dimension reduction and selection of relevant features for different analytical frameworks. Unsupervised analyses highlighted the importance of post transplant time-of-rejection, while supervised analyses identified gene and protein signatures that together predicted rejection status with little time effect. The selected genes are part of biological pathways that are representative of immune responses. Gene enrichment profiles revealed increases in innate immune responses and neutrophil activities and a depletion of T lymphocyte related processes in rejection samples as compared to controls. In all, this article offers candidate biomarkers for future detection and monitoring of acute kidney transplant rejection, as well as ways forward for methodological advances to better harness multi-omics data sets.