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Article

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

1
Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy
2
Department of Computer Science, University of Torino, 10149 Torino, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Jung Hun Oh
Int. J. Mol. Sci. 2021, 22(23), 12755; https://doi.org/10.3390/ijms222312755
Received: 27 September 2021 / Revised: 12 November 2021 / Accepted: 23 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Deep Learning and Machine Learning in Bioinformatics)
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. View Full-Text
Keywords: single cell RNAseq; single cell ATACseq; sparsely connected autoencoders; gene regulatory network; transcription factor; miRNA; pseudo-bulk data single cell RNAseq; single cell ATACseq; sparsely connected autoencoders; gene regulatory network; transcription factor; miRNA; pseudo-bulk data
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MDPI and ACS Style

Alessandri, L.; Ratto, M.L.; Contaldo, S.G.; Beccuti, M.; Cordero, F.; Arigoni, M.; Calogero, R.A. Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis. Int. J. Mol. Sci. 2021, 22, 12755. https://doi.org/10.3390/ijms222312755

AMA Style

Alessandri L, Ratto ML, Contaldo SG, Beccuti M, Cordero F, Arigoni M, Calogero RA. Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis. International Journal of Molecular Sciences. 2021; 22(23):12755. https://doi.org/10.3390/ijms222312755

Chicago/Turabian Style

Alessandri, Luca, Maria L. Ratto, Sandro G. Contaldo, Marco Beccuti, Francesca Cordero, Maddalena Arigoni, and Raffaele A. Calogero 2021. "Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis" International Journal of Molecular Sciences 22, no. 23: 12755. https://doi.org/10.3390/ijms222312755

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