Seurat is a mainstream tool for single-cell analysis, but its data
structure can change between versions and may be less explicit for
teaching and extension. In contrast, SingleCellExperiment is a
well-defined Bioconductor class with a rich ecosystem in R and a
corresponding Python counterpart, AnnData. The goal of sclet is to
provide a lightweight set of Seurat-like helpers for
SingleCellExperiment, making common workflows easier to learn and
apply.
In addition to core steps (preprocessing, dimensionality reduction,
clustering, visualization, batch correction, and pseudobulk differential
expression), sclet also offers optional wrappers for popular
downstream tools, including trajectory inference, enrichment analysis,
cell type annotation, cell-cell communication, and Milo differential
abundance.
Guangchuang YU
School of Basic Medical Sciences, Southern Medical University
Get the development version from github:
## install.packages("remotes")
remotes::install_github("YuLab-SMU/sclet")For more details, please refer to the online documents: