Workflows

Workflow for gene set enrichment analsysis (GSEA) and co-expression analysis (WGCNA) on transcriptomics data to analyze pathways affected in Porto-Sinusoidal Vascular Disease.

Workflow for Creating a large disease network from various datasets and databases for IBM, and applying the active subnetwork identification method MOGAMUN.

Joint multi-omics dimensionality reduction approaches for CAKUT data using peptidome and proteome data

Brief description In (Cantini et al. 2020), Cantini et al. evaluated 9 representative joint dimensionality reduction (jDR) methods for multi-omics integration and analysis and . The methods are Regularized Generalized Canonical Correlation Analysis (RGCCA), Multiple co-inertia analysis (MCIA), Multi-Omics Factor Analysis (MOFA), Multi-Study Factor Analysis (MSFA), iCluster, Integrative NMF ...

For integrative analysis of CAKUT multi-omics data DIABLO method of the mixOmics package (version 6.10.9. Singh et. al. 2019) was used with sPLS-DA (sparse Partial Least Squares Discriminant Analysis Discriminant Analysis) and PLS-DA classification.

In this analysis, we created an extended pathway, using the WikiPathways repository (Version 20210110) and the three -omics datasets. For this, each of the three -omics datasets was first analyzed to identify differentially expressed elements, and pathways associated with the significant miRNA-protein links were detected. A miRNA-protein link is deemed significant, and may possibly be implying causality, if both a miRNA and its target are significantly differentially expressed.

The peptidome and ...