A statistical mechanics approach to the sample deconvolution problem

TL;DR

This paper introduces a statistical mechanics framework to deconvolute mixed gene expression data from multicellular samples, providing analytical insights and an algorithm for reconstructing individual cell type expression levels.

Contribution

It presents a novel application of statistical mechanics to gene expression deconvolution, offering analytical results and a practical algorithm for sample unmixedness.

Findings

Analytical conditions for successful deconvolution

Algorithm for reconstructing cell type expression levels

Insights into sample variability effects

Abstract

In a multicellular organism different cell types express a gene in different amounts. Samples from which gene expression levels can be measured typically contain a mixture of different cell types, the resulting measurements thus give only averages over the different cell types present. Based on fluctuations in the mixture proportions from sample to sample it is in principle possible to reconstruct the underlying expression levels of each cell type: to deconvolute the sample. We use a statistical mechanics approach to the problem of deconvoluting such partial concentrations from mixed samples, give analytical results for when and how well samples can be unmixed, and suggest an algorithm for sample deconvolution.