ArticleMolecular & cellular proteomics : MCP2019
Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis.
Article in Molecular & cellular proteomics : MCP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide.Frontiers in cell and developmental biology · 2024Review
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- Article
- Independent component analysis recovers consistent regulatory signals from disparate datasets.PLoS computational biology · 2021Article
- Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.Frontiers in genetics · 2021Article
- Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.Molecular systems biology · 2021Article
- Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.Cell · 2020Article
- Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.International journal of molecular sciences · 2019Review
- Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems.Molecular & cellular proteomics : MCP · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.
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Registered trials
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