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).
Team Huber
Team Saez-Rodriguez
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.
Team Klingmüller
mspypeline was developed for the analysis of proteomic data generated by MaxQuant to provide a tool to analyze complex datasets in a standardized manner with minimal effort and to eliminate the chance of human error or obscuring variability during data analysis.
Designed with an intuitive and concise graphical user interface (GUI), mspypeline offers researchers, unfamiliar with programming or data analysis, the opportunity to explore and visualize their data independently and in a time-effective manner. A structured workflow (see figure to the right) can be followed to perform a comprehensive and conclusive analysis that starts with quality control of the data, followed by the assessment and choice of data preprocessing operations to finally allow optimal exploratory analysis. By automizing the calculations and the generation of versatile figures, mspypeline supports data analysis within minutes. Simultaneously, the more experienced user may interact closer with the mspypeline package to perform advanced analysis exploiting the plethora of customization options.
Standardization and reproducibility of the analysis are ensured by the automated logging of all analysis settings and saving them to a separate configuration file.
Thus, mspypeline provides a platform that supports users in their proteomics data analysis generated by mass spectrometry by giving insight into the data, offering parameter adaptation when needed, generating custom figures and performing differential expression analysis and hypothesis testing to reach biologically relevant conclusions.
