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

Abstract 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 les...

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Published in:Bioinformatics 2022-01, Vol.38 (4), p.1015-1021
Main Authors: Boldina, Galina, Fogel, Paul, Rocher, Corinne, Bettembourg, Charles, Luta, George, Augé, Franck
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container_end_page 1021
container_issue 4
container_start_page 1015
container_title Bioinformatics
container_volume 38
creator Boldina, Galina
Fogel, Paul
Rocher, Corinne
Bettembourg, Charles
Luta, George
Augé, Franck
description Abstract 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 (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. Availability and implementation All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install. Supplementary information Supplementary data are available at Bioinformatics online.
doi_str_mv 10.1093/bioinformatics/btab773
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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 (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. 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subjects Algorithms
Exome Sequencing
Gene Expression Profiling
RNA-Seq
Transcriptome
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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