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Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study

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Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study. / Staedler, Nicolas; Dondelinger, Frank; Hill, Steven et al.
In: Bioinformatics, Vol. 33, No. 18, 15.09.2017, p. 2890-2896.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Staedler, N, Dondelinger, F, Hill, S, Akbani, R, Lu, Y, Mills, G & Mukherjee, S 2017, 'Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study', Bioinformatics, vol. 33, no. 18, pp. 2890-2896. https://doi.org/10.1093/bioinformatics/btx322

APA

Staedler, N., Dondelinger, F., Hill, S., Akbani, R., Lu, Y., Mills, G., & Mukherjee, S. (2017). Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study. Bioinformatics, 33(18), 2890-2896. https://doi.org/10.1093/bioinformatics/btx322

Vancouver

Staedler N, Dondelinger F, Hill S, Akbani R, Lu Y, Mills G et al. Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study. Bioinformatics. 2017 Sept 15;33(18):2890-2896. Epub 2017 May 23. doi: 10.1093/bioinformatics/btx322

Author

Staedler, Nicolas ; Dondelinger, Frank ; Hill, Steven et al. / Molecular heterogeneity at the network level : high-dimensional testing, clustering and a TCGA case study. In: Bioinformatics. 2017 ; Vol. 33, No. 18. pp. 2890-2896.

Bibtex

@article{f41cb4f66ba044089ab34e0ea09fd785,
title = "Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study",
abstract = "Motivation: Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging.Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset.Results: We leverage recent ideas from high-dimensional statistics for testing and clustering in the network biology setting. The methods we describe can be applied directly to most continuous molecular measurements and networks do not need to be specified beforehand. We illustrate the ideas and methods in a case study using protein data from the Cancer Genome Atlas (TCGA). This provides evidence that patterns of interplay between signalling proteins differ significantly between cancer types. Furthermore, we show how the proposed approaches can be used to learn subtypes and the molecular networks that define them.Availability: As the Bioconductor package nethet.",
author = "Nicolas Staedler and Frank Dondelinger and Steven Hill and Rehan Akbani and Yiling Lu and Gordon Mills and Sach Mukherjee",
year = "2017",
month = sep,
day = "15",
doi = "10.1093/bioinformatics/btx322",
language = "English",
volume = "33",
pages = "2890--2896",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "18",

}

RIS

TY - JOUR

T1 - Molecular heterogeneity at the network level

T2 - high-dimensional testing, clustering and a TCGA case study

AU - Staedler, Nicolas

AU - Dondelinger, Frank

AU - Hill, Steven

AU - Akbani, Rehan

AU - Lu, Yiling

AU - Mills, Gordon

AU - Mukherjee, Sach

PY - 2017/9/15

Y1 - 2017/9/15

N2 - Motivation: Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging.Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset.Results: We leverage recent ideas from high-dimensional statistics for testing and clustering in the network biology setting. The methods we describe can be applied directly to most continuous molecular measurements and networks do not need to be specified beforehand. We illustrate the ideas and methods in a case study using protein data from the Cancer Genome Atlas (TCGA). This provides evidence that patterns of interplay between signalling proteins differ significantly between cancer types. Furthermore, we show how the proposed approaches can be used to learn subtypes and the molecular networks that define them.Availability: As the Bioconductor package nethet.

AB - Motivation: Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging.Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset.Results: We leverage recent ideas from high-dimensional statistics for testing and clustering in the network biology setting. The methods we describe can be applied directly to most continuous molecular measurements and networks do not need to be specified beforehand. We illustrate the ideas and methods in a case study using protein data from the Cancer Genome Atlas (TCGA). This provides evidence that patterns of interplay between signalling proteins differ significantly between cancer types. Furthermore, we show how the proposed approaches can be used to learn subtypes and the molecular networks that define them.Availability: As the Bioconductor package nethet.

U2 - 10.1093/bioinformatics/btx322

DO - 10.1093/bioinformatics/btx322

M3 - Journal article

VL - 33

SP - 2890

EP - 2896

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 18

ER -