  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.
