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Nat Commun. 2018 Apr 16;9(1):1471. doi: 10.1038/s41467-018-03843-3.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm.

Author information

1
Department of Systems Biology, Columbia University, New York, NY, 10032, USA.
2
Department of Biological Sciences, Columbia University, New York, NY, 10027, USA.
3
Department of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA.
4
GlaxoSmithKline, King of Prussia, PA, 19406, USA.
5
Amsterdam Neuroscience, Amsterdam, 1081, The Netherlands.
6
Department of Systems Biology, Columbia University, New York, NY, 10032, USA. malvarez@darwinhealth.com.
7
DarwinHealth Inc, New York, NY, 10032, USA. malvarez@darwinhealth.com.
8
Department of Systems Biology, Columbia University, New York, NY, 10032, USA. andrea.califano@columbia.edu.
9
DarwinHealth Inc, New York, NY, 10032, USA. andrea.califano@columbia.edu.
10
Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, 10032, USA. andrea.califano@columbia.edu.
11
J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, 10032, USA. andrea.califano@columbia.edu.
12
Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA. andrea.califano@columbia.edu.
13
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, 10032, USA. andrea.califano@columbia.edu.

Abstract

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

PMID:
29662057
PMCID:
PMC5902599
DOI:
10.1038/s41467-018-03843-3
[Indexed for MEDLINE]
Free PMC Article

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