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Discovering combinatorial interactions in survival data.

ABSTRACT: MOTIVATION: Although several methods exist to relate high-dimensional gene expression data to various clinical phenotypes, finding combinations of features in such input remains a challenge, particularly when fitting complex statistical models such as those used for survival studies. RESULTS: Our proposed method builds on existing 'regularization path-following' techniques to produce regression models that can extract arbitrarily complex patterns of input features (such as gene combinations) from large-scale data that relate to a known clinical outcome. Through the use of the data's structure and itemset mining techniques, we are able to avoid combinatorial complexity issues typically encountered with such methods, and our algorithm performs in similar orders of duration as single-variable versions. Applied to data from various clinical studies of cancer patient survival time, our method was able to produce a number of promising gene-interaction candidates whose tumour-related roles appear confirmed by literature.

SUBMITTER: Duverle DA

PROVIDER: S-EPMC3834797 | biostudies-other | 2013 Dec

REPOSITORIES: biostudies-other

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Discovering combinatorial interactions in survival data.

Duverle David A DA Takeuchi Ichiro I Murakami-Tonami Yuko Y Kadomatsu Kenji K Tsuda Koji K

Bioinformatics (Oxford, England) 20130913 23

<h4>Motivation</h4>Although several methods exist to relate high-dimensional gene expression data to various clinical phenotypes, finding combinations of features in such input remains a challenge, particularly when fitting complex statistical models such as those used for survival studies.<h4>Results</h4>Our proposed method builds on existing 'regularization path-following' techniques to produce regression models that can extract arbitrarily complex patterns of input features (such as gene comb ...[more]

PMID: 24037215

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Project description:Gene regulation often appears deterministic in the average cell population, but transcription is a probabilistic process at the single-cell level. Although many mechanisms are invoked to account for this behavior, it is difficult to determine how cell-to-cell variation in the interactions of transcription factors with target chromatin impact transcriptional output. Here, we use cells that contain a 200-copy tandem array of promoter or reporter gene units to simultaneously visualize transient interaction, equilibrium or steady-state binding of fluorescent-protein-labeled glucocorticoid receptor with its DNA response elements, the recruitment of diverse coregulators, and transcriptional output at the single-cell level. These regulatory proteins associate with target chromatin via a probabilistic mechanism that produces cell-to-cell variability in binding. The multiple steps of this process are partially independent and differ between individual regulators. The association level of each regulator influences the transcriptional output in individual cells, but this does not account for all transcriptional heterogeneity. Additionally, specific combinatorial interactions of the glucocorticoid receptor and coregulators with response elements regulate transcription at the single-cell level. Like many endogenous genes, the average array transcriptional activity evolves over time. This apparently deterministic average temporal promoter progression involves changes in the probability that specific combinatorial glucocorticoid receptor and coregulator interactions will occur on the response elements in single cells. These data support the emerging ;return-to-template' transcription model, which mechanistically unifies the observed extremely transient interactions between the transcription factor and response elements, cell-to-cell variability in steady-state association of factors with chromatin, and the resulting heterogeneous gene expression between individual cells.

Dataset Information

Discovering combinatorial interactions in survival data.

Publications

Discovering combinatorial interactions in survival data.

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