G. Boldina, P. Fogel, C. Rocher, C. Bettembourg, G. Luta and F. Augé. Bioinformatics, Volume 38, Issue 4, Pages 1015-1021, 15 February 2022.
Abstract: Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue.
Full Title : "A2Sign: Agnostic Algorithms for Signatures—a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution"
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