Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

Wang, Dongfang; Gu, Jin

doi:10.1007/s40484-016-0063-4

Cited by 56 publications

(39 citation statements)

References 93 publications

(109 reference statements)

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“…Pavel et al (2016) used a fuzzy logic modeling framework (Xu et al 2008) to integrate multiple types of omics data with expert curated biological rules for identification of cancer drivers and to infer patientspecific gene activity. To deal with sample heterogeneity, Wang and Gu (2016) have proposed three clustering categories, direct integrative clustering, clustering of clusters and regulatory integrative clustering. Nibbe et al (2010) demonstrated that integration of complementary data sources (transcriptomic and proteomic data) using a 'proteomics-first' approach can enhance discovery of candidate sub-networks in cancer.…”

Section: Tools Available For Integration Of Multi-omics Datamentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

371

240

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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

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Section: Tools Available For Integration Of Multi-omics Datamentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

371

240

View full text Add to dashboard Cite

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“…Many clustering methods have been specifically developed to analyse multi-omics data. Several authors provide full reviews and benchmarks [9][10][11]. In particular, Wang and Gu [9] suggest the following typology: i) direct integrative clustering, consisting in a preprocessing of the original data set before concatenation into a single data set ready for some standard clustering analysis [12,13]; ii) regulatory integrative clustering, which are based on pathways [14]; iii) clustering of clusters, i.e., methods that take clustering made on different data sets and find a consensus [15,16].…”

Section: Multi-omicsmentioning

confidence: 99%

Fast tree aggregation for consensus hierarchical clustering

et al. 2020

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Background: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. Results: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be O(nq log(n)), n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real-omics data sets, introducing a spectral variant as an efficient and robust by-product. Conclusions: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.

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“…Several recent reviews discussed multi-omics integration. [10], [11] and [12] review methods for multiomics integration, and [13] review multi-omics clustering for cancer application. These reviews do not include a benchmark, and do not focus on multi-view clustering.…”

Section: Introductionmentioning

confidence: 99%

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

Rappoport

Shamir

2018

Preprint

143

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High throughput experimental methods developed in recent years have been used to collect large biomedical omics datasets. Clustering of such datasets has proven invaluable for biological and medical research, and helped reveal structure in data from several domains. Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for multi-omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types.In addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic benchmark comparison of leading multi-omics and multiview clustering algorithms. The results highlight several key questions regarding the use of single-vs. multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the rapidly increasing use of multi-omics data, these issues may be important for future progress in the field.

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Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

Cited by 56 publications

References 93 publications

Integrated omics: tools, advances and future approaches

Integrated omics: tools, advances and future approaches

Fast tree aggregation for consensus hierarchical clustering

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

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