Saez-Rodriguez Group

CARNIVAL (CAusal Reasoning for Network identification using Integer VALue programming) allows to derive perturbed signalling pathways topology after drugs perturbation based on gene expression data. CARNIVAL is available as an R package, also in Bioconductor.

CellNetOptimizer (CellNOpt) is a toolbox for creating logic-based models of signal transduction networks, and training them against high-throughput biochemical data, and is freely available both for R and matlab.

COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets. COSMOS leverages extensive prior knowledge of signaling pathways, metabolic networks, and gene regulation with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. This pipeline can provide mechanistic explanations for experimental observations across multiple omic data sets.

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources. decoupleR is a Bioconductor and Python package containing different statistical methods to extract these signatures within a unified framework. It can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomic, gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites targeted by a kinase.

A collection of genes controlled by Transcription Factors (regulons) obtained from different resources from manually curated to inferred from transcriptomic. It can be used to estimate Transcription Factors activities using decoupler.

FUNKI (FUNctional analysis KIt) is an user-friendly interface developed in R, and designed using Shiny, to analyze multiomic data (mainly transcriptomics, phosphoproteomics and metabolomics) using footprint methods. FUNKI provides an interface for DoRothEA, PROGENy, KinAct, CARNIVAL and COSMOS. All methods run on multiple or differential analysis of bulk data for human samples. Besides, DoRothEA, PROGENy and CARNIVAL can also handle data coming from single-cell experiments and mouse.

The continuous developments of single-cell RNA-Seq (scRNA-Seq) have sparked an immense interest in understanding intercellular crosstalk. Multiple tools and resources aiming the investigation of cell-cell communication (CCC) were published recently. However, these methods and resources are usually fixed combinations of a tool and its corresponding resource, although potentially any resource could be combined with any method. To this end, we built LIANA—a framework to decouple the tools from their corresponding resources. LIANA is able to infer cell-cell communication from scRNA-Seq using 6 tools and 15 resources in a single framework. This way not only it makes the methods more accessible, but also facilitates their comparison and benchmark.

The Multiview Intercellular SpaTial modeling framework (MISTy) is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.

OCEAN: a method that defines metabolic enzyme footprint from a curated reduced version of the recon2 reaction network. The metabolic enzyme footprints are used to explore coordinated deregulations of metabolite abundances with respect to their position relative to metabolic enzymes. This is similar to Kinase-substrate and TF-targets enrichment analyses.

OmniPath is a comprehensive collection of molecular prior knowledge such as literature curated human and rodent signaling pathways, enzyme-substrate interactions, protein complexes, molecular annotations (function, localization and many others) and inter-cellular communication roles. OmniPath is built by pypath, a powerful Python module for molecular networks and pathways analysis. It is available by a web service at https://omnipathdb.org/, an R/Bioconductor package OmnipathR, a Python client and the OmniPath Cytoscape app.

PHONEMEs  builds logic models from discovery mass-spectrometry based Phosphoproteomic data.

PROGENy (Pathway RespOnsive GENes) is a collection of  target genes of diverse signaling pathways. It can be used to  infer activity of signaling pathways from  transcriptomics data using decoupler.