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A2Sign: Agnostic Algorithms for Signatures - a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution

MOTIVATION
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.


RESULTS
We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization 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.


AVAILABILITY AND IMPLEMENTATION
All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform non-negative tensor factorization, we used the NMTF package, which can be downloaded using Python pip install.


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.