Sparse canonical methods for biological data integration: application to a cross-platform study

Cao, Kim Anh Lê; Martin, Pascal G.P.; Robert-Granié, Christèle; Besse, Philippe

doi:10.1186/1471-2105-10-34

Cited by 233 publications

(171 citation statements)

References 32 publications

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“…Lê Cao et al (2008Cao et al ( , 2009) proposed a sparse PLS approach to combine integration and simultaneous variable (e.g. gene) selection in one step.…”

Section: Methodsologies For Integrated Transcriptomics and Metabolomicsmentioning

confidence: 99%

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Zhang

Li²,

Nie³

2010

Microbiology

415

241

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Recent advances in various ‘omics’ technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular ‘omics’ platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single ‘omics’ analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-‘omics’ approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous ‘omics’ datasets in various microbial systems and the results have demonstrated that the multi-‘omics’ approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental ‘omics’ approaches, recent application of the integrated ‘omics’ for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated ‘omics’ analyses. Online databases and bioinformatic infrastructure available for integrated ‘omics’ analyses are also briefly discussed.

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“…Lê Cao et al (2008Cao et al ( , 2009) proposed a sparse PLS approach to combine integration and simultaneous variable (e.g. gene) selection in one step.…”

Section: Methodsologies For Integrated Transcriptomics and Metabolomicsmentioning

confidence: 99%

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Zhang

Li²,

Nie³

2010

Microbiology

415

241

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“…We calculated the sparse partial least square (sPLS) correlations between profiles of expression of sRNAs and mRNAs. The sPLS approach has been recently developed to perform simultaneous variable selection in the two data sets (33,34). Variable selection is achieved by introducing LASSO penalization on the pair of loading vectors.…”

Section: Methodsmentioning

confidence: 99%

Small RNA Transcriptome of the Oral Microbiome during Periodontitis Progression

Duran-Pinedo¹,

Yost²,

Frias-Lopez

2015

Appl Environ Microbiol

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The oral microbiome is one of the most complex microbial communities in the human body, and due to circumstances not completely understood, the healthy microbial community becomes dysbiotic, giving rise to periodontitis, a polymicrobial inflammatory disease. We previously reported the results of community-wide gene expression changes in the oral microbiome during periodontitis progression and identified signatures associated with increasing severity of the disease. Small noncoding RNAs (sRNAs) are key players in posttranscriptional regulation, especially in fast-changing environments such as the oral cavity. Here, we expanded our analysis to the study of the sRNA metatranscriptome during periodontitis progression on the same samples for which mRNA expression changes were analyzed. We observed differential expression of 12,097 sRNAs, identifying a total of 20 Rfam sRNA families as being overrepresented in progression and 23 at baseline. Gene ontology activities regulated by the differentially expressed (DE) sRNAs included amino acid metabolism, ethanolamine catabolism, signal recognition particle-dependent cotranslational protein targeting to membrane, intron splicing, carbohydrate metabolism, control of plasmid copy number, and response to stress. In integrating patterns of expression of protein coding transcripts and sRNAs, we found that functional activities of genes that correlated positively with profiles of expression of DE sRNAs were involved in pathogenesis, proteolysis, ferrous iron transport, and oligopeptide transport. These findings represent the first integrated sequencing analysis of the community-wide sRNA transcriptome of the oral microbiome during periodontitis progression and show that sRNAs are key regulatory elements of the dysbiotic process leading to disease. Bacterial small noncoding RNAs (sRNAs) encompass a large and diverse group of RNA molecules that do not result in the translation of a protein product. They show high heterogeneity in size and structure and many are used in regulatory roles or other functional capacities upon transcription. sRNAs are important in bacteria because they can be used to adapt rapidly to changing environmental conditions, especially in an environment such as the oral cavity, which is exposed daily to regular changes in amount and quality of nutrients available for use by the oral biofilms. Most of these sRNAs are encoded in the 5= and 3= untranslated regions, as well as in intergenic regions (IGRs) of the genome (1).sRNAs display a wide variety of mechanisms of action. sRNAs repress translation of mRNA by attaching to the ribosome binding site (RBS) competing with the ribosome and leading to the rapid degradation of the mRNA (2). Other noncanonical mechanisms of translation repression have been also described, such as binding cis-acting antisense RNA to a ribosome standby site upstream of the RBS (3). In addition, sRNAs can also activate the translations of mRNAs (4, 5) or can modulate gene expression by varying the level of transcript stability (6). sRNA ca...

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“…Both are multivariate models to account for pleiotropic effects [43, 44] (i.e., genetic variants associated with multiple imaging quantitative traits) and the covariance structure among imaging endophenotypes [45, 46]. Two block methods include sparse CCA [47, 48], regularized kernel CCA [49] and sparse PLS correlation (PLSC) [50, 51], which are usually used for correspondence analysis between two modalities. Floch et al [50] compared the performance of detecting the correlation between fMRI imaging and SNP data with different methods such as univariate approach, PLSC, sparse PLSC, regularized kernel CCA, and their combinations with PCA and pre-filters.…”

Section: Sparse Models For [Correlation/association] Analysis Of Imentioning

confidence: 99%

“…This general formulation includes a variety of existing sparse CCA models as specific examples [47, 58], e.g., CCA- l 1 or CCA-elastic net (λ 1 =0, λ 2 =0) and CCA-group lasso (τ 1 =0, τ 2 =0) models. The solution of Eq.1 usually involves with the inversion of the covariance matrices, which might be non-invertible because of the high dimensionality of data.…”

Section: Sparse Models For [Correlation/association] Analysis Of Imentioning

confidence: 99%

Sparse models for correlative and integrative analysis of imaging and genetic data

Lin

Cao

Calhoun

et al. 2014

Journal of Neuroscience Methods

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The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis.

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Sparse canonical methods for biological data integration: application to a cross-platform study

Cited by 233 publications

References 32 publications

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Small RNA Transcriptome of the Oral Microbiome during Periodontitis Progression

Sparse models for correlative and integrative analysis of imaging and genetic data

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