A Critical Survey of Deconvolution Methods for Separating cell-types in Complex Tissues

TL;DR

This paper provides a comprehensive survey of deconvolution methods for separating cell-type signals in complex tissues, analyzing models, techniques, and assumptions, and proposing solutions to improve accuracy and reliability.

Contribution

It offers a detailed review of existing deconvolution techniques, identifies their shortcomings, and introduces new solutions and a step-by-step process for improved application.

Findings

Assessment of various loss functions and constraints improves deconvolution accuracy.

Identification of normalization and data filtering issues affecting results.

Proposed solutions enhance the reliability of cell-type deconvolution methods.

Abstract

Identifying concentrations of components from an observed mixture is a fundamental problem in signal processing. It has diverse applications in fields ranging from hyperspectral imaging to denoising biomedical sensors. This paper focuses on in-silico deconvolution of signals associated with complex tissues into their constitutive cell-type specific components, along with a quantitative characterization of the cell-types. Deconvolving mixed tissues/cell-types is useful in the removal of contaminants (e.g., surrounding cells) from tumor biopsies, as well as in monitoring changes in the cell population in response to treatment or infection. In these contexts, the observed signal from the mixture of cell-types is assumed to be a linear combination of the expression levels of genes in constitutive cell-types. The goal is to use known signals corresponding to individual cell-types along with a model of the mixing process to cast the deconvolution problem as a suitable optimization problem. In this paper, we present a survey of models, methods, and assumptions underlying deconvolution techniques. We investigate the choice of the different loss functions for evaluating estimation error, constraints on solutions, preprocessing and data filtering, feature selection, and regularization to enhance the quality of solutions, along with the impact of these choices on the performance of regression-based methods for deconvolution. We assess different combinations of these factors and use detailed statistical measures to evaluate their effectiveness. We identify shortcomings of current methods and avenues for further investigation. For many of the identified shortcomings, such as normalization issues and data filtering, we provide new solutions. We summarize our findings in a prescriptive step-by-step process, which can be applied to a wide range of deconvolution problems.