Get more knowledge from your existing or future bulk RNA data without the high cost of the single-cell experiments.

If you are using or contemplating using single-cell technologies, you may be interested in this review and benchmarking article on cellular deconvolution. Deconvolution is a technique that allows you to get information at the cell type level from bulk RNA data. If you want to know how many cells you have for each cell type present in your assay, you have two options. One way is to sequence every cell, assign a cell type to each cell, and then combine all data to get cell type abundance levels. That’s the classical single cell approach. The alternative is to take bulk RNA data and use deconvolution to quantify the cell type abundance levels. Extracting cell type information from a bulk RNA experiment can be seen as a particular case of the blind source separation problem. The classical example is the cocktail party problem. At a cocktail party, there are many people in the room, all talking at the same time. A listener has to be able to follow one of the discussions, even though she hears many people involved in parallel discussions. The human brain can easily handle this sort of source separation problem. The deconvolution process does essentially the same thing: identifies specific types of cells and separates their gene expression behavior from the others, allowing us to follow their evolution separately. This article reviews 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. So basically you have a hands-on comparison of most existing deconvolution methods, based on real data from real experiments.

Dive into the transformative journey of cellular deconvolution over the past fourteen years. Authored by Advaita scientists, this comprehensive review, published in Nucleic Acids Research, meticulously evaluates 53 deconvolution methods using data from 283 cell types across 30 tissues and 63 individuals. Discover how these advancements can help you extract more knowledge from existing or future bulk RNA data without the high cost of the single-cell experiments.

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Published Article: Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges