Dimension reduction techniques for the integrative analysis of multi-omics data

Meng, Chen; Zeleznik, Oana A.; Thallinger, Gerhard G.; Küster, Bernhard; Gholami, Amin Moghaddas; Culhane, Aedín C.

doi:10.1093/bib/bbv108

Cited by 326 publications

(256 citation statements)

References 82 publications

(125 reference statements)

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“…This statistical technique projects the information in multi-variable/-parameter datasets onto low dimensions, typically two dimensions, which is useful here to facilitate meaningful comparisons and clustering of the commercial antibodies by means of optimal linear combinations (principal components, PCs) of the original metrics, that are represented visually in a biplot [33, 34]. For example, Hemmink et al [35] have used PCA to analyse immunological datasets following an influenza pathogenesis study in pigs showing the correlation of cytokine production, with viral titre over time.…”

Section: Methodsmentioning

confidence: 99%

Enhancing the toolbox to study IL-17A in cattle and sheep

Wattegedera

Corripio-Miyar

Pang

et al. 2017

Vet Res

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The development of methods to detect cytokine expression by T cell subsets in ruminants is fundamental to strategic development of new livestock vaccines for prevention of infectious diseases. It has been possible to detect T cell expression of IFN-γ, IL-4 and IL-10 in ruminants for many years but methods to detect expression of IL-17A are relatively limited. To address this gap in capability we have cloned bovine and ovine IL-17A cDNAs and expressed biologically-active recombinant proteins in Chinese Hamster Ovary (CHO) cells. We used the transfected CHO cells to screen commercially-available antibodies for their ability to detect IL-17A expression intracellularly and in culture supernates. We demonstrate that an ELISA for bovine IL-17A detects native ovine IL-17A. Moreover, the constituent polyclonal antibodies (pabs) in the ELISA were used to enumerate peripheral blood mononuclear cells (PBMC) expressing IL-17A from cattle and sheep by ELISpot. We identified two monoclonal antibodies (mabs) that detect recombinant intracellular IL-17A in CHO cells by flow cytometry. One of these mabs was used to detect native intracellular IL-17A expression in PBMC in conjunction with cell surface phenotyping mabs [CD4+ve, CD8+ve and Workshop Cluster 1 (WC-1)+ve gamma-delta (γδ)] we show that distinct T cell subsets in cattle (defined as CD4+ve, CD8+ve or WC-1+ve) and sheep (defined as CD4+ve or WC-1+ve) can express IL-17A following activation. These novel techniques provide a solid basis to investigate IL-17A expression and define specific CD4+ve T cell subset activation in ruminants.Electronic supplementary materialThe online version of this article (doi:10.1186/s13567-017-0426-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Enhancing the toolbox to study IL-17A in cattle and sheep

Wattegedera

Corripio-Miyar

Pang

et al. 2017

Vet Res

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“…Some methods search for a “consensus” after clustering patients by each platform separately [37], or cluster with protein-protein interactions [29], or patient similarity networks [80, 81]. Other methods formulate the problem as a “multi-view” matrix factorization and dimension reduction , or as a probabilistic model (reviewed in [82]). In all cases, a key challenge is the selection of features from each platform as inputs to the clustering algorithms; for example, it is possible to summarize mutations, gene expression, and DNA methylation events as binary alterations [80], and then treat any missing data as a non-alteration event.…”

Section: Analysis Approaches To Determine Molecular Subtypes and Cancmentioning

confidence: 99%

Precision Oncology: The Road Ahead

Senft

Leiserson

Ruppin

et al. 2017

Trends in Molecular Medicine

150

123

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Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and non-genetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single-cell analysis of tumor specimens. These may ultimately lead to the development of next generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.

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“…Although factor models that aim to address this have previously been proposed (e.g. Meng et al , , ; Tenenhaus et al , ; preprint: Singh et al , ), these methods either lack sparsity, which can reduce interpretability, or require a substantial number of parameters to be determined using computationally demanding cross‐validation or post hoc. Further challenges faced by existing methods are computational scalability to larger data sets, handling of missing values and non‐Gaussian data modalities, such as binary readouts or count‐based traits.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

Argelaguet

Velten

Arnol

et al. 2018

Molecular Systems Biology

837

804

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Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

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Dimension reduction techniques for the integrative analysis of multi-omics data

Cited by 326 publications

References 82 publications

Enhancing the toolbox to study IL-17A in cattle and sheep

Enhancing the toolbox to study IL-17A in cattle and sheep

Precision Oncology: The Road Ahead

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

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