CARNIVAL (CAusal Reasoning for Network identification using Integer VALue programming) is a method for the identification of upstream reguatory signalling pathways from downstream gene expression (GEX).
This is a tool currently being developed by the SaezLab members and is an extension of the previously implemented Causal Reasoning (Melas et al.) method. More detailed information on the CARNIVAL pipeline as well as benchmarking and applicational studies are available on BioRxiv.
Liu A., Trairatphisan P., Gjerga E. et al. From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL BioRxiv, 2019, doi: 10.1101/541888 (equal contributions).
The aim of the CARNIVAL pipeline is to identify a subset of interactions from a prior knowledge network that represent potential regulated pathways linking known or potential targets of perturbation towards active transcription factors derived from GEX data. The pipeline includes a number improved functionalities comparing to the original version and consists of the following processes:
1) Transcription factors’ (TFs) activities and pathway scores from gene expressions can be inferred with our in-house tools DoRothEA & PROGENy, respectively.
2) TFs’ activities and signed directed protein-protein interaction networks with or without the provided target of perturbations and pathway scores are then used to derive a series of linear constraints to generate integer linear programming (ILP) problems.
3) An ILP solver (IBM ILOG CPLEX) is subsequently applied to identify the sub-network topology with minimised fitting error and model size.
Applications of CARNIVAL include the identification of drug’s modes of action and of deregulated processes in diseases (even if the molecular targets remain unknown) by deciphering the alterations of main signalling pathways as well as alternative pathways and off-target effects.
The input for CARNIVAL consists of:
A prior knowledge network (PKN) comprises a list of signed and directed interactions between signalling proteins. (Required)
Inferred transcription factor activities which can be inferred from GEX data using DoRothEA. (Required)
A list of target of perturbations (drugs, diseases, etc.) with or without their effects on signalling proteins. (Optional)
Inferred pathway scores representing signalling pathway activities from GEX data using PROGENy (Optional)
The outcome of CARNIVAL includes the list of identified networks that fitted to the provided experimental data as well as the predicted activities of signalling proteins in the networks whether they are up- or down-regulated.
Melas I.N. et al. Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury Integrative Biology, 2015, 7, 904.
Garcia-Alonso L. et al. Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer. Cancer Res. 2018 Feb 1;78(3):769-780. doi: 10.1158/0008-5472.CAN-17-1679. Epub 2017 Dec 11.
Schubert M. et al. Perturbation-response genes reveal signaling footprints in cancer gene expression Nature Communicationsvolume 9, Article number: 20 (2018) doi: 10.1038/s41467-017-02391-6.