In STATegra, and aiming for an optimal Dissemination of methodologies, we agreed in sharing generated methodologies as Bioconductor R packages. We have generated an initial version of STATegRa package including two methodologies: OmicsPCA and OmicsClustering. See below a short description of them. The STATegra package will become public during the coming Bioconductor release, but please contact us if you are interested to use it before that.

OmicsPCA (a Data Fusion Method) implements several methodologies to analyse jointly the overall common and distinct variability of different omics data types measured over the same set of samples. The results of this analysis are multiple PCA models where clustering of samples for the common variability component and clustering for omics-specific variability can be visualised. The approximation is valuable to explore relationships across multi-omics dataset.

OmicsClustering is a clustering method based on the combined and weighted distances between genes calculated on the basis of several omics measurements. The algorithm requires a mapping strategy to assign non-gene features (such as ChiP-seq peaks) to genes. The approach is interesting to see gene associations due to different regulatory characteristics of genes.


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