† These authors contributed equally to this work Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of single-cell RNA sequencing (scRNA-Seq) data. However, their integration with the common scRNA-Seq bioinformatics pipelines still poses a challenge. Here, we introduce scAEspy, a unifying tool that embodies five of the most advanced AEs and different loss functions, including two novel AEs that we developed. scAEspy allows the integration of data generated using different scRNA-Seq platforms. We benchmarked scAEspy against principal component analysis (PCA) on five public datasets, showing that our new AEs outperform the existing solutions, achieving more than 20% increase of the Rand Index in the identification of cell clusters. Autoencoders | scRNA-Seq | Dimensionality reduction | Clustering | Batch correction | Data Visualisation Correspondence: at860@cam.ac.uk pl219@cam.ac.uk as889@cam.ac.uk Tangherloni et al. | bioRχiv | August 6, 2019 | 1-12

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.