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Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis

Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measureme...

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Autores principales: Alessandri, Luca, Ratto, Maria Luisa, Contaldo, Sandro Gepiro, Beccuti, Marco, Cordero, Francesca, Arigoni, Maddalena, Calogero, Raffaele A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657975/
https://www.ncbi.nlm.nih.gov/pubmed/34884559
http://dx.doi.org/10.3390/ijms222312755
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author Alessandri, Luca
Ratto, Maria Luisa
Contaldo, Sandro Gepiro
Beccuti, Marco
Cordero, Francesca
Arigoni, Maddalena
Calogero, Raffaele A.
author_facet Alessandri, Luca
Ratto, Maria Luisa
Contaldo, Sandro Gepiro
Beccuti, Marco
Cordero, Francesca
Arigoni, Maddalena
Calogero, Raffaele A.
author_sort Alessandri, Luca
collection PubMed
description Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data.
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spelling pubmed-86579752021-12-10 Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis Alessandri, Luca Ratto, Maria Luisa Contaldo, Sandro Gepiro Beccuti, Marco Cordero, Francesca Arigoni, Maddalena Calogero, Raffaele A. Int J Mol Sci Article Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data. MDPI 2021-11-25 /pmc/articles/PMC8657975/ /pubmed/34884559 http://dx.doi.org/10.3390/ijms222312755 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alessandri, Luca
Ratto, Maria Luisa
Contaldo, Sandro Gepiro
Beccuti, Marco
Cordero, Francesca
Arigoni, Maddalena
Calogero, Raffaele A.
Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
title Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
title_full Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
title_fullStr Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
title_full_unstemmed Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
title_short Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
title_sort sparsely connected autoencoders: a multi-purpose tool for single cell omics analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657975/
https://www.ncbi.nlm.nih.gov/pubmed/34884559
http://dx.doi.org/10.3390/ijms222312755
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