DeCovarT, a multidimensional probalistic model for the deconvolution of heterogeneous transcriptomic samples

TL;DR

DeCovarT is a novel probabilistic model that improves deconvolution of heterogeneous transcriptomic samples by integrating covariance matrices, outperforming existing methods especially with overlapping cellular profiles.

Contribution

The paper introduces DeCovarT, a new deconvolution tool that incorporates cellular covariance structures, enhancing accuracy over prior methods in complex tissue analysis.

Findings

Outperforms existing deconvolution methods in simulations

Better differentiation of closely related cell populations

Effective with overlapping cellular transcriptomic profiles

Abstract

Although bulk transcriptomic analyses have greatly contributed to a better understanding of complex diseases, their sensibility is hampered by the highly heterogeneous cellular compositions of biological samples. To address this limitation, computational deconvolution methods have been designed to automatically estimate the frequencies of the cellular components that make up tissues, typically using reference samples of physically purified populations. However, they perform badly at differentiating closely related cell populations. We hypothesised that the integration of the covariance matrices of the reference samples could improve the performance of deconvolution algorithms. We therefore developed a new tool, DeCovarT, that integrates the structure of individual cellular transcriptomic network to reconstruct the bulk profile. Specifically, we inferred the ratios of the mixture components by a standard maximum likelihood estimation (MLE) method, using the Levenberg-Marquardt algorithm to recover the maximum from the parametric convolutional distribution of our model. We then consider a reparametrisation of the log-likelihood to explicitly incorporate the simplex constraint on the ratios. Preliminary numerical simulations suggest that this new algorithm outperforms previously published methods, particularly when individual cellular transcriptomic profiles strongly overlap.