A2Sign: Agnostic Algorithms for Signatures — a universal method for identifying molecular signatures
Dernière mise à jour : 16 déc. 2021
P. Fogel, G. Luta. Bioinformatics - Oxford Academic. November, 12th 2021.
Full title : "A2Sign: Agnostic Algorithms for Signatures — a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution".
Full list of authors: Galina Boldina, Paul Fogel, Corinne Rocher, Charles Bettembourg, George Luta and Franck Augé.
With the contribution of Christophe Geissler regarding the use of the NTF (Non-negative Tensor Factorization (NTF)) and the HHI (Herfindahl-Hirschman Index).
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.
We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data.