An R package integrate into Seurat V5, meant to aid clustering and annotation step in scRNA-seq analysis
scClustAnnot is an R package to perform clustering and annotation step
in scRNA-seq work flow. Currently, clustering and annotation in
scRNA-seq analysis requires many repetitive manual works, including
resolution tuning, identifying differentially expressed genes (DEGs),
and interpreting DEGs biological functions at given context, which is
time consuming and subject to researcher’s bias. There exists some
automatic annotation tool, however, mostly done by map to previously
annotated data. In that case, the quality of previously annotated data
is a great concern. This package aims to automate above manual task to
an extent, so that researchers’ bias can be reduced and analysis can be
performed more effectively. The scClustAnnot package was developed
using R version 4.5.0 (2025-04-11 ucrt), platform
Microsoft Windows 11 25H2 (64 bit)
To install the latest version of the package:
install.packages("devtools")
library("devtools")
devtools::install_github("DDMMMAA/scClustAnnot", build_vignettes = TRUE)
library("scClustAnnot")To run shiny App:
runscClustAnnot()ls("package:scClustAnnot")
data(package = "scClustAnnot")
browseVignettes("scClustAnnot")scClustAnnot Contains 5 functions.
- ClusterUnderRes() for cluster given scRNA-seq data under various resolution and generate a cluster tree visualization
- FindClusterDeg() for identify cluster specific differential expressed genes
- SinglerAnnote() for automatically annotate cell by map to previously annotated dataset at single cell level
- ClustifyrAnnote() for automatically annotate cell by map to previously annotated dataset at cluster level
- ClusterGo() for conduct GO enrichment analysis among clusters
The package also contains one scRNA-seq data already converted to Seurat object
This package is an individual project developed and maintained solely by
Jiaqi Ma, contributions include project planing, literature review,
documentation, implementation, build vignette, shiny app, etc. The
package is heavily based on the Seurat framework. In
ClusterUnderRes() function Clustree was utilized to generate basic
cluster tree. In SingleRAnnotate() function, reference scRNA-seq
annotation was accessed via Celldex API, SingleR were utilized for
mapping query scRNA-seq data to reference data. In ClustifyrAnnote()
function, clustifyr is conducting core mapping to reference dataset
fetched from clustifyrdatahub. In FindClusterDeg() funcion,
Seurat::FindAllMarkers() play the role of identify cluster specific
markers. In ClusterGo() function, ClusterProfiler was conducting
core GO enrichment analysis. Development of this package was assisted by
ChatGPT 5, for read error/warning message, debug, generate test case,
and build shiny app. All generated code and documentation were manually
reviewed, refined, and tested by the author before inclusion in the
final package.
- Jiaqi, M. (2025) scClustAnnot: An R package integrate into Seurat V5, meant to aid clustering and annotation step in scRNA-seq analysis. Unpublished. https://github.com/DDMMMAA/scClustAnnot
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This package was developed as part of an assessment for 2025 BCB410H:
Applied Bioinformatics course at the University of Toronto, Toronto,
CANADA. scClustAnnot welcomes issues, enhancement requests, and other
contributions. To submit an issue, use the GitHub issues.