Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Research output: Contribution to journalJournal articleResearchpeer-review

  • Aurelien Dugourd
  • Christoph Kuppe
  • Marco Sciacovelli
  • Enio Gjerga
  • Attila Gabor
  • Emdal, Kristina Bennet
  • Vitor Vieira
  • Dorte B. Bekker-Jensen
  • Jennifer Kranz
  • Eric M.J. Bindels
  • Ana S.H. Costa
  • Abel Sousa
  • Pedro Beltrao
  • Miguel Rocha
  • Olsen, Jesper Velgaard
  • Christian Frezza
  • Rafael Kramann
  • Julio Saez-Rodriguez

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Original languageEnglish
Article numbere9730
JournalMolecular Systems Biology
Issue number1
Number of pages17
Publication statusPublished - 2021

    Research areas

  • causal reasoning, kidney cancer, metabolism, multi-omics, signaling

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