The MatrixQCvis package provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples).
COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets.
The functional insights that metabolomic data sets contain currently lies under-exploited. This is in part due to the complexity of metabolic reaction networks and the indirect relationship between reaction fluxes and metabolite abundance. Yet, footprint-based methods have been available for decades in the context of other omic data sets such as transcriptomic and phosphoproteomic. Here, we present ocEAn, a method that defines metabolic enzyme footprint from a curated reduced version of the recon2 reaction network and use them to explore coordinated deregulations of metabolite abundances with respect to their position relative to metabolic enzymes in the same manner as Kinase-substrate and TF-targets Enrichment analysis.