The Role of Scale in the Estimation of Cell-type Proportions

Abstract

Complex tissues are composed of a large number of different types of cells, each involved in a multitude of biological processes. Consequently, an important component to understanding such processes is understanding the cell-type composition of the tissues. Estimating cell type composition using high-throughput gene expression data is known as cell-type deconvolution. In this paper, we first summarize the extensive deconvolution literature by identifying a common regression-like approach to deconvolution. We call this approach the Unified Deconvolution-as-Regression (UDAR) framework. While methods that fall under this framework all use a similar model, they fit using data on different scales. Two popular scales for gene expression data are logarithmic and linear. Unfortunately, each of these scales has problems in the UDAR framework. Using log-scale gene expressions proposes a biologically implausible model and using linear-scale gene expressions will lead to statistically inefficient estimators. To overcome these problems, we propose a new approach for cell-type deconvolution that works on a hybrid of the two scales. This new approach is biologically plausible and improves statistical efficiency. We compare the hybrid approach to other methods on simulations as well as a collection of eleven real benchmark datasets. Here, we find the hybrid approach to be accurate and robust.deconvolution, gene expression, microarray, RNA-seq

How many markers are needed to robustly determine a cell's type?

“…Single-cell data have been routinely used to increase deconvolution performance in recently developed tools ( Tsoucas et al., 2019 ; Wang et al., 2019 ; Newman et al., 2019 ; Dong et al., 2021 ), but performance remains plagued by batch effects and cell-type similarity ( Newman et al., 2019 ; Huang et al., 2020 ; Avila Cobos et al., 2020 ). The role of marker genes in deconvolution remains particularly unclear: a recent benchmark suggests that the quality of markers is more important than the deconvolution method ( Avila Cobos et al., 2020 ), and in most studies the influence of the number of markers is only partially assessed ( Newman et al., 2019 ; Hunt and Gagnon-Bartsch 2021 ). Our annotation assessment suggested that cell types are best captured with 10–200 meta-analytic markers; deconvolution is a natural place to test this heuristic.…”

How many markers are needed to robustly determine a cell’s type?

“…Single cell data have been routinely used to increase deconvolution performance in recently developed tools (Tsoucas et al 2019; Wang et al 2019; Newman et al 2019; Dong et al), but performance remains plagued by batch effects and cell type similarity (Newman et al 2019; Huang et al 2020; Cobos et al 2020). The role of marker genes in deconvolution remains particularly unclear: a recent benchmark suggests that the quality of markers is more important than the deconvolution method (Cobos et al 2020), in most studies the influence of the number of markers is only partially assessed (Newman et al 2019; Hunt and Gagnon-Bartsch 2019). Our annotation assessment suggested that cell types are best captured with 10 to 200 meta-analytic markers; deconvolution is a natural place to test this heuristic.…”