Open AccessrCASC: reproducible classification analysis of single-cell sequencing data
Author(s) -
Luca Alessandrì,
Francesca Cordero,
Marco Beccuti,
Maddalena Arigoni,
Martina Olivero,
Greta Romano,
Sergio Rabellino,
Nicola Licheri,
Gennaro De Libero,
Luigia Pace,
Raffaele Calogero
Publication year - 2019
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giz105
Subject(s) - computer science , workflow , cluster analysis , data mining , modular design , population , preprocessor , computational biology , biology , machine learning , artificial intelligence , database , demography , sociology , operating system
Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g., ATAC-seq. Many related algorithms and tools have been developed, but few computational workflows provide analysis flexibility while also achieving functional (i.e., information about the data and the tools used are saved as metadata) and computational reproducibility (i.e., a real image of the computational environment used to generate the data is stored) through a user-friendly environment.