CDSeq – A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data

Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address tissue heterogeneity have drawbacks. Experimental techniques, such as fluorescence-activated cell sorting, and single cell RNA sequencing are expensive. Computational approaches that use expression data from heterogeneous samples are promising, but most of the current methods estimate either cell-type proportions or cell-type-specific expression profiles by requiring the other as input. Although such partial deconvolution methods have been successfully applied to tumor samples, the additional input required may be unavailable.

NIEHS researchers have developed a novel complete deconvolution method, CDSeq, that uses only RNA-Seq data from bulk tissue samples to simultaneously estimate both cell-type proportions and cell-type-specific expression profiles. Using several synthetic and real experimental datasets with known cell-type composition and cell-type-specific expression profiles, they compared CDSeq’s complete deconvolution performance with seven other established deconvolution methods. Complete deconvolution using CDSeq represents a substantial technical advance over partial deconvolution approaches and will be useful for studying cell mixtures in tissue samples.

Schematic of the CDSeq approach

rna-seq

Heterogeneous samples consist of different cell types. The bulk RNA-Seq profile represents a weighted average of the expression profiles of the constituent cell types. CDSeq takes as input the bulk RNA-Seq data for a collection of samples and performs complete deconvolution that outputs estimates of both the cell-type-specific expression profiles and the cell-type proportions for each sample. This Figure depicts a simple scenario of six biological samples comprising four cell types, each with gene expression measurements on eight genes. 

Availability – CDSeq is available at GitHub repository (MATLAB and Octave code): https://github.com/kkang7/CDSeq

Kang K, Meng Q, Shats I, Umbach DM, Li M, Li Y, et al. (2019) CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. PLoS Comput Biol 15(12): e1007510. [article]

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