Unraveling the complexity: understanding the deconvolutions of RNA-seq data

Abstract

Abstract Deconvolution of RNA sequencing data is a computational method used to estimate the relative proportions of different cell types or subpopulations within a heterogeneous sample based on gene expression profiles. This technique is particularly useful in studies where the goal is to identify changes in gene expression that are specific to a particular cell type or subpopulation. The deconvolution process involves using reference gene expression profiles from known cell types or subpopulations to infer the relative abundance of these cells within a mixed sample. This is typically done using linear regression or other statistical methods to model the observed gene expression data as a linear combination of the reference profiles. Once the relative proportions of each cell type or subpopulation have been estimated, downstream analyses can be performed on each component separately, allowing for more precise identification of cell-type-specific changes in gene expression. Overall, deconvolution of RNA sequencing data is a powerful tool for dissecting complex biological systems and identifying cell-type-specific molecular signatures that may be relevant for disease diagnosis and treatment.