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bioRxiv · 10.1101/430967

rCASC: reproducible Classification Analysis of Single Cell sequencing data

Alessandri, L.·Beccuti, M.·Arigoni, M.·Olivero, M.·Romano, G.·De Libero, G.·Pace, L.·Cordero, F.·Calogero, R.
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Abstract

SummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.\n\nAvailability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.\n\nSupplementary informationSupplementary data are available at rCASC github

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Alessandri, L., Beccuti, M., Arigoni, M., Olivero, M., Romano, G., De Libero, G., Pace, L., Cordero, F., Calogero, R.. 2018-10-03. rCASC: reproducible Classification Analysis of Single Cell sequencing data. https://doi.org/10.1101/430967

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