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Analysis of single-cell RNA sequencing data based on autoencoders.

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Tangherloni, Andrea 
Ricciuti, Federico 
Besozzi, Daniela 
Liò, Pietro 


BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches. RESULTS: Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of scRNA-Seq data, so that the deployment of AE-based tools might represent the way forward in this context. We introduce here scAEspy, a unifying tool that embodies: (1) four of the most advanced AEs, (2) two novel AEs that we developed on purpose, (3) different loss functions. We show that scAEspy can be coupled with various batch-effect removal tools to integrate data by different scRNA-Seq platforms, in order to better identify the cell-types. We benchmarked scAEspy against the most used batch-effect removal tools, showing that our AE-based strategies outperform the existing solutions. CONCLUSIONS: scAEspy is a user-friendly tool that enables using the most recent and promising AEs to analyse scRNA-Seq data by only setting up two user-defined parameters. Thanks to its modularity, scAEspy can be easily extended to accommodate new AEs to further improve the downstream analysis of scRNA-Seq data. Considering the relevant results we achieved, scAEspy can be considered as a starting point to build a more comprehensive toolkit designed to integrate multi single-cell omics.



Autoencoders, Batch correction, Clustering, Data integration, Dimensionality reduction, scRNA-Seq, Machine Learning, RNA, Sequence Analysis, RNA, Single-Cell Analysis, Exome Sequencing

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BMC Bioinformatics

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Springer Science and Business Media LLC
Wellcome Trust (203151/Z/16/Z)
European Research Council (677501)
Cancer Research Uk (None)
Medical Research Council (MC_PC_17230)
European Research Council project 677501 – ZF_Blood (AC)