Digital sorting of complex tissues for cell type-specific gene expression profiles

Abstract

BackgroundCellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations.ResultsHere, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies.ConclusionsThe results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.

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

“…Given that the expression domain of cell-type specific markers is restricted to unique cells in the reference profile, Gaujoux et al [29] present a semi-supervised NMF (ssNMF) method that explicitly enforces an orthogonality constraint at each iteration over the subset of markers in the reference profile. This constraint both enhances the convergence of the NMF algorithm, and simplifies the matching of columns in the estimated cell-type expression to the columns of the reference panel, G. The Digital Sorting Algorithm (DSA) [30] works as follows: if concentration matrix C is known a priori, it directly uses quadratic programming (QP) with added constraints on the lower/upper bound of gene expressions to estimate matrix G. Otherwise, if fractions are also unknown, it uses the average expression of given marker genes that are only expressed in one cell-type, combined with the STO constraint, to estimate concentrations matrix C first. Population-specific expression analysis (PSEA) [36] performs a linear least squares regression to estimate quantitative measures of cell-type-specific expression levels, in a similar fashion as the update equation for estimatinĝ G in Equation 3.…”

High-throughput genomic profiling of tumor-infiltrating leukocytes

“…A biological mixture m can then be modeled as a system of linear equations in which m = G × f , where f is a vector containing the fraction of each cell subset from G in m . While methods have been proposed to determine f , G or both [16,17,21,22,24–30,32,35–37,39,41,49], many approaches estimate f given m and G . Here, we focus on deconvolution techniques that can enumerate TIL proportions ( f ).…”

“…While both methods have significant utility, they also have notable limitations for high-resolution TIL characterization. For example, flow cytometry, like other single cell analysis methods (e.g., single cell RNA-seq), requires mechanical or enzymatic dissociation of solid tissues, which can distort TIL representation [6,16,17]. In contrast, IHC is directly applicable to solid tissues, but is generally limited to one marker (or cell type) per tissue section, restricting its scope to a small number of cell types.…”

“…For over a decade, computational techniques have been applied to decipher cellular content directly from genomic profiles of mixture samples [8,16,17,21–43]. Here, we review recent developments and outstanding questions related to in silico dissection of TILs from bulk tumors (Figure 1).…”

Comprehensive Evaluation of RNA-seq Quantification Methods for Linearity

“…In the field of biomedical research, deconvolution is widely applied to retrieve cell-type or tissue specific gene expression profiles from heterogeneous tissue samples. Most deconvolution algorithms in the literature assume a linear model [10][11][12][13][14][15][16][17], in which the expression signal of the mixture is a weighted sum of the expression for its constitutive cell types. Previous analysis has shown the necessity of using anti-log expression microarray data to avoid unwanted bias introduced by non-linear transformation [18].…”