Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses
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Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. / Dugourd, Aurelien; Kuppe, Christoph; Sciacovelli, Marco; Gjerga, Enio; Gabor, Attila; Emdal, Kristina B.; Vieira, Vitor; Bekker-Jensen, Dorte B.; Kranz, Jennifer; Bindels, Eric M.J.; Costa, Ana S.H.; Sousa, Abel; Beltrao, Pedro; Rocha, Miguel; Olsen, Jesper V.; Frezza, Christian; Kramann, Rafael; Saez-Rodriguez, Julio.
In: Molecular Systems Biology, Vol. 17, No. 1, e9730, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses
AU - Dugourd, Aurelien
AU - Kuppe, Christoph
AU - Sciacovelli, Marco
AU - Gjerga, Enio
AU - Gabor, Attila
AU - Emdal, Kristina B.
AU - Vieira, Vitor
AU - Bekker-Jensen, Dorte B.
AU - Kranz, Jennifer
AU - Bindels, Eric M.J.
AU - Costa, Ana S.H.
AU - Sousa, Abel
AU - Beltrao, Pedro
AU - Rocha, Miguel
AU - Olsen, Jesper V.
AU - Frezza, Christian
AU - Kramann, Rafael
AU - Saez-Rodriguez, Julio
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - causal reasoning
KW - kidney cancer
KW - metabolism
KW - multi-omics
KW - signaling
U2 - 10.15252/msb.20209730
DO - 10.15252/msb.20209730
M3 - Journal article
C2 - 33502086
AN - SCOPUS:85099968883
VL - 17
JO - Molecular Systems Biology
JF - Molecular Systems Biology
SN - 1744-4292
IS - 1
M1 - e9730
ER -
ID: 257326425