A sequential Monte Carlo approach to gene expression deconvolution.

Oyetunji E Ogundijo,Xiaodong Wang,Lars Kaderali

doi:10.1371/journal.pone.0186167

Abstract

High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Highlights

We assessed the performance of the proposed method, which we will refer to as the sequential Monte Carlo (SMC) method, on both simulated dataset and datasets that contain real mixed samples
We compared the estimates of the cell types proportion and the cell-type specific expression matrices with some existing methods and secondly, we went further to test the ability of the proposed method to identify differentially expressed genes
J > I implies that Jurkat is upregulated as compared to IM-9

Summary

Introduction

Gene expression data obtained from pure samples, comprising of a single cell type, can be analyzed to yield a significant amount of information. Measuring gene expression levels in different conditions may prove useful in medical diagnosis, treatment prescription, drug design [5, 6] and most importantly in the identification of genes that are differentially expressed between groups of samples [7], such as tumor versus non-tumor tissues [8]. There are some limitations in using these techniques They are very expensive and often come with low cell throughput rate [12,13,14], resulting in a drastic reduction in the yield of biological contents

Methods

Results

Conclusion