PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

Abstract

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

“…Their method is based on Ordinary Least Squares (OLS) regression and does not consider either non-negativity or sum-to-one constraints explicitly, but rather it enforces these constraints implicitly after the optimization procedure. An extension of this approach is proposed by Qiao et al [21], which uses non-negative least squares (NNLS) to explicitly enforce non-negativity as part of the optimization. Gong et al [22] present a quadratic programming (QP) framework to explicitly encode both constraints in the optimization problem formulation.…”

“…• LiverBrainLung [24] [21]. We directly download these datasets from the supplementary material of the paper.…”

“…Additionally, there is a known relationship between the mean expression value and its variance [45], which makes these outliers noisier than the rest of the features. This becomes even more important when dealing with purified cell-types that come from different labs, since highly expressed time/micro-environment dependent genes would be significantly different than the ones in the mixture [21]. A simple argument is to filter genes that the range of expression values in Affymetrix microarray technology is bounded by 2 16 (due to initial normalization and image processing steps).…”

DeMix: deconvolution for mixed cancer transcriptomes using raw measured data

“…Current methods for dissecting array-based expression data (Abbas et al, 2009;Clarke et al, 2010;Erkkila¨et al, 2010;Ghosh, 2004;Gosink et al, 2007;Lu et al, 2003;Qiao et al, 2012;Stuart et al, 2004;Tothill et al, 2005;Venet et al, 2001;Wang et al, 2006) are limited in their application to actual data for the three following reasons.…”

“…Most methods require good knowledge of one of these measures. The tissue-specific expressions (A) can be derived from a set of genes with known expression profiles in all constituting tissue types (Lu et al, 2003;Qiao et al, 2012) or from a set of genes that show significant enrichment in one of the tissue types (Tothill et al, 2005;Wang et al, 2006). Other methods focus on deconvolution of gene expressions based on known tissue proportions (Erkkila¨et al, 2010;Ghosh, 2004;Shen-Orr et al, 2010;Stuart et al, 2004).…”