Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 27 September 2021 / Approved: 29 September 2021 / Online: 29 September 2021 (17:26:18 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Int. J. Mol. Sci. 2021, 22, 12755
DOI: 10.3390/ijms222312755

Abstract

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 (AE) is composed of an encoder and a decoder sub-model. AE are very powerful 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 no anymore a black box and it can be used to depict new biologically interesting features from single cell data; Results: In this paper, we show that SCA hidden layer can grab new information usually hidden in single cell data, like as providing clustering on meta-features difficult, i.e. transcription factors expression, or impossible, miRNA expression, to depict in single cell RNAseq data. Furthermore, a 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 represent the bioinformatics version of a “Swiss Army knife” for the extraction of hidden knowledgeable features from single cell OMICs data.

Keywords

single cell RNAseq; single cell ATACseq; sparsely connected autoencoder; pseudo-bulk

Subject

LIFE SCIENCES, Other

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