Dataset Information

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.

ABSTRACT:

Summary

We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor-stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data.

Availability and implementation

UNDO is available at http://bioconductor.org/packages.

SUBMITTER: Wang N

PROVIDER: S-EPMC4271149 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Publications

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.

Wang Niya N Gong Ting T Clarke Robert R Chen Lulu L Shih Ie-Ming IeM Zhang Zhen Z Levine Douglas A DA Xuan Jianhua J Wang Yue Y

Bioinformatics (Oxford, England) 20140910 1

<h4>Summary</h4>We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We dem ...[more]

PMID: 25212756

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Project description:MotivationComplex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations.ResultsWe report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided.Availability and implementationThe R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.

Summary

Availability and implementation

Publications

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.

Similar Datasets

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