Supportnig material for the manuscript: Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining Alessandri L, Cordero F, Beccuti M, Licheri N, Arigoni M, Olivero M, Di Renzo F, Sapino A and Calogero RA


This vignette provides support to use Sparsely-Connected Autoencoder (SCA) in the analysis of single cell RNAseq data (scRNAseq). SCA analysis was added as extention of rCASC.


To simplify usage and to guarantee reproducibility the tools required for the SCA workflow are embedded in docker containers stored at For more info on the computational approaches used in SCAtutorial please see Kulkarni et al. BMC Bioinformatics 2018

An extensive description of how rCASC works is provided at rCASC vignette. Installation of rCASC including the SCA modules requires:

To install the SCAtutorial, write in an R session:

install_github("kendomaniac/SCAtutorial", ref="master")

Then, after package installation, execute in R:

#check if docker daemon is  running and install rCASC package from github.

This step checks that docker daemon is running and downloads the docker containers required for the tutorial. It might require sometime, between minutes to hours, depending on the available internet bandwidth.

Web site

The vignette of the SCAtutorial is available at vignette

The vignette is located in the folder SCAtutorial/docs/articles. The data produced by the building of the vignette are generated using the command:


and they are located in SCAtutorial/vignettes/setA folder.

kendomaniac/SCAtutorial documentation built on Sept. 11, 2020, 9:18 a.m.