Journal
MOLECULAR SYSTEMS BIOLOGY
Volume 17, Issue 1, Pages -Publisher
WILEY
DOI: 10.15252/msb.20209730
Keywords
causal reasoning; kidney cancer; metabolism; multi-omics; signaling
Categories
Funding
- European Union [675585]
- German Federal Ministry of Education and Research (Bundesministerium fur Bildung und Forschung BMBF) MSCoreSys research initiative research core SMART-CARE [031L0212A]
- JRC - Bayer AG
- Medical Research Council [MC_UU_12022/6]
- Novo Nordisk Foundation [NNF14CC0001]
- Danish Council for Independent Research [8020-00100B]
- Lundbeck Foundation [R193-2015-243]
- German Research Foundation (DFG) [SFBTRR57, SFBTRR219, CRU344]
- European Research Council [ERC-StG 677448]
- State of North Rhine-Westphalia
- BMBF eMed Consortia Fibromap
- ERA-CVD Consortia MEND-AGE
- Else Kroener Fresenius Foundation (EKFS)
- Interdisciplinary Centre for Clinical Research (IZKF) within the faculty of Medicine at the RWTH Aachen University [O3-11]
- German Society of Internal Medicine (DGIM)
- ProjektDEAL
- MRC [MC_UU_12022/6] Funding Source: UKRI
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COSMOS is a method that integrates multiple omics datasets to provide mechanistic hypotheses for experimental observations through network-level causal reasoning and activity estimation. When applied to a ccRCC dataset, COSMOS successfully captures relevant crosstalks within and between different omics layers.
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.
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