A sequential Monte Carlo approach to gene expression deconvolution

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.

Discovering Pathway and Cell Type Signatures in Transcriptomic Compendia with Machine Learning

“…The NMF core algorithm can be guided to identify cell types by restricting the component matrix columns to sum to one (61). Additionally, a Markov chain Monte Carlo approach has been proposed to estimate cell type proportions in an unsupervised fashion (62).…”

Discovering pathway and cell-type signatures in transcriptomic compendia with machine learning

“…Additionally, a Markov Chain Monte Carlo (MCMC) approach has been proposed to estimate cell-type proportions in an unsupervised fashion (61). Nearest shrunken centroids, which minimizes the number of genes required to describe subtypes (62), was also used to deconvolve tumors into malignant, nonmalignant, and stroma components (63).…”

Statistical modeling of cell‐to‐cell variability in viral infection during passaging in suspension cell culture: Application in Monte‐Carlo simulation

“…Moreover, a computational framework is proposed that focuses on kinetic model selection for HIV viral entry through binding to receptor and coreceptor expression based on their predictions to infectivity measurement (Mistry, D'Orsogna, Webb, Lee, & Chou, 2016). Similarly, in other biological systems, the Monte‐Carlo simulations have been performed to study endosomal fusion and escape of influenza (Lagache, Sieben, Meyer, Herrmann, & Holcman, 2017) and gene expression profiles in normal and cancer cells (Ogundijo & Wang, 2017; Zhang et al, 1997). The probability distribution for protein expression and the number of packaged virus in this study corresponds biologically to the variability in the expression and packaging process, with some cells getting infected and undergoing viral protein expression without mutations, while other cells in the suspension culture undergo mutations due to the transposon insertion (Giri et al, 2010).…”