Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Lock, Eric F.; Hoadley, Katherine A.; Marron, J. S.; Nobel, Andrew B.

doi:10.1214/12-aoas597

Cited by 445 publications

(537 citation statements)

References 36 publications

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“…We foresee that the real potential for nutritional systems biology applications will lie in multiknowledge integration strategies that will, by including information at different levels, shed light on gene-nutrient interactions with a degree of accuracy and completeness not yet achievable today. Lock et al (2013) Multivariatebased integration…”

Section: Resultsmentioning

confidence: 99%

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

et al. 2015

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Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multiomics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

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Section: Resultsmentioning

confidence: 99%

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

et al. 2015

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“…The method was recently used in a landmark study to predict novel breast cancer subtypes with distinct clinical outcomes (9), and it was found that the joint clustering of copy number and gene expression profiles resolved the considerable heterogeneity of the expression-only subgroups. Other approaches on data integration that have emerged in recent years include generalized data decomposition methods (10,11) and nonparametric Bayesian models (12). However, two major challenges have not yet been fully addressed.…”

mentioning

confidence: 99%

Pattern discovery and cancer gene identification in integrated cancer genomic data

Wang

Seshan

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

385

371

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Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [Nature (2012) 486(7403):346-352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression.A major goal of many cancer genome projects is to characterize key genetic alterations in cancer and discover therapeutic targets through comprehensive genomic profiling of the cancer genome. The Cancer Genome Atlas (TCGA) studies have unveiled the genetic landscape of several cancer types by whole-genome and whole-exome sequencing, DNA copy number profiling, promoter methylation profiling, and mRNA expression profiling in a large number of tumors (1-5). Complementary to the tumor project, the Cancer Cell Line Encyclopedia (CCLE) (6) and the Sanger cell line project (7) has cataloged a compilation of genetic and molecular data in almost 1,000 human cancer cell lines, coupled with pharmacological profiles for a large panel of anticancer drugs. These large-scale integrative genomic efforts have been geared toward comprehensively cataloging individual genomic alterations, analogous to a reverse-engineering process where thousands of individual cancer genomes are taken apart to shed light on common biological principles. Unfortunately, cancer genomes exhibit considerable heterogeneity with abnormalities occurring in different genes among different individuals, posing a great challenge to identify those genes with functional importance and therapeutic implications. Thus, there is a...

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“…Matrix Factorization unsupervised JIVE [130] Cancer patient stratification by integrating mRNA expression and miRNA expression data. Network-based unsupervised SNF [131] Patient subtyping by integrating patient similarity networks constructed from mRNA expression, DNA methylation and miRNA expression data.…”

Section: Databasementioning

confidence: 99%

Integrative methods for analyzing big data in precision medicine

2016

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We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of Big Data in biology and medicine. These data offer many opportunities to advance precision medicine.We outline key challenges in precision medicine and present recent advances in data integrationbased methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, bio-markers discovery and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.

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Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Cited by 445 publications

References 36 publications

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

Pattern discovery and cancer gene identification in integrated cancer genomic data

Integrative methods for analyzing big data in precision medicine

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