  Although bulk transcriptomic analyses have significantly contributed to an
enhanced comprehension of multifaceted diseases, their exploration capacity is
impeded by the heterogeneous compositions of biological samples. Indeed, by
averaging expression of multiple cell types, RNA-Seq analysis is oblivious to
variations in cellular changes, hindering the identification of the internal
constituents of tissues, involved in disease progression. On the other hand,
single-cell techniques are still time, manpower and resource-consuming
analyses.To address the intrinsic limitations of both bulk and single-cell
methodologies, computational deconvolution techniques have been developed to
estimate the frequencies of cell subtypes within complex tissues. These methods
are especially valuable for dissecting intricate tissue niches, with a
particular focus on tumour microenvironments (TME).In this paper, we offer a
comprehensive overview of deconvolution techniques, classifying them based on
their methodological characteristics, the type of prior knowledge required for
the algorithm, and the statistical constraints they address. Within each
category identified, we delve into the theoretical aspects for implementing the
underlying method, while providing an in-depth discussion of their main
advantages and disadvantages in supplementary materials.Notably, we emphasise
the advantages of cutting-edge deconvolution tools based on probabilistic
models, as they offer robust statistical frameworks that closely align with
biological realities. We anticipate that this review will provide valuable
guidelines for computational bioinformaticians in order to select the
appropriate method in alignment with their statistical and biological
objectives.We ultimately end this review by discussing open challenges that
must be addressed to accurately quantify closely related cell types from RNA
sequencing data, and the complementary role of single-cell RNA-Seq to that
purpose.
