RCASC: reproducible classification analysis of single-cell sequencing data.

ABSTRACT:

Background

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.

Findings

rCASC is a modular workflow providing an integrated analysis environment (from count generation to cell subpopulation identificat

SUBMITTER: Alessandri L 

PROVIDER: S-EPMC6732171 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature