Getting Started with scGenePanel
Source:
vignettes/scGenePanel.Rmd
Introduction
scGenePanel is an R package designed to visualize single-cell RNA-seq gene expression data. Specifically, it provides a multi-panel plot that allows users to visualize gene expression counts across different cell identities and types. This package is particularly useful for researchers interested in exploring gene expression patterns in single-cell datasets.
Why scGenePanel in Bioconductor?
Single-cell workflows commonly use either Seurat objects or
Bioconductor’s SingleCellExperiment class. scGenePanel
accepts both, allowing the same gene-centred visualization workflow to
be used alongside established Bioconductor infrastructure and across
analysis pipelines. Rather than replacing general-purpose analysis or
plotting tools, it assembles a recurring exploratory task into one
coordinated output: group-stratified embeddings, expression
distributions, and cell-frequency/expression summaries for a chosen gene
and cell population. This makes the visualization specification easier
to reuse in scripts while retaining an interactive Shiny interface for
exploration.
Features of scGenePanel visual
- A multi-panel plot that contains:
- UMAP : Visualize cluster of cells, split by cell identity of interest and highlight cell type of interest
- Violin plot : Visualize gene expression counts, colored by cell identity of interest, split by cell type of interest and significance test computed on cell identity of interest
- Tabular plot : Quantify cell counts, cell counts >0.25, % expressed and quantiles of gene expression counts per cell identity of interest
- Accepts two commonly used input data object (Seurat, SingleCellExperiment)
- “Tableau” or “RColorBrewer” discrete qualitative color palettes options available
- Order of cell identity of interest can be changed via group_order parameter
- Shiny version to explore gene expression data interactively
Installation
From Github repository
From local source (Git clone)
Data included in this package
The package comes with two test datasets: Seurat and
SingleCellExperiment objects. For this vignette, a Seurat dataset -
human_panc_islets.Rds - is included in this package. This
Seurat object contains single cell RNAseq data of human pancreatic
islets from five healthy donors published in Shrestha et al., JCI
Insight (2021) that has been downsampled to 500 cells. The
complete dataset is available in zenodo. Also included in
this package is a SCE dataset - sce_object.Rds.
Key annotations included in the metadata are:
CellTypes- cell types annotationsAge- donor age
Load data
scGenePanel works with Seurat and SingleCellExperiment objects as the main input data. To load test data:
seurat_object <- readRDS(system.file("extdata", "human_panc_islets.Rds", package="scGenePanel"))
sce_object <- readRDS(system.file("extdata", "sce_object.Rds", package="scGenePanel"))Alternatively, you can use your own Seurat or SingleCellExperiment object.
Create a multi-panel plot
library(Seurat)
# Load the Seurat object
data <- readRDS(system.file("extdata", "human_panc_islets.Rds", package="scGenePanel"))
# Set the cell identity metadata
Idents(data) <- "CellTypes"
# Filter the dataset to include only specific cell identities and age of interest
data <- subset(x = data, idents = c("Alpha", "Beta", "Delta"))
data <- subset(x = data, subset = Age %in% c("14y", "39y", "66y"))
# Cell type distribution
table(data$CellTypes)##
## Alpha Beta Delta Gamma Epsilon Acinar
## 177 57 3 0 0 0
## Ductal Endothelial Stellate Immune
## 0 0 0 0##
## 14y 39y 50y 59y 66y
## 57 135 0 0 45scGenePanel() loads the multi-panel visual for the gene
of interest among the cell identity of interest entered by parameter
meta_group and cell type of interest entered by parameter
cell_type_name.
library(scGenePanel)
scGenePanel(
object = data,
gene = "INS",
meta_group = "Age",
cell_type_name = "Beta",
cell_type_colname = "CellTypes",
col_palette = "Tableau",
group_order = NULL,
output_dir = tempdir()
)
scGenePanelShiny() launches a Shiny app, allowing for
interactive exploration of the gene expression data. In this case, you
only need to provide the data object, the parameters will be set
interactively in the app.
- scGenePanel pre-fills your available cell type and group information, just pick the ones you want to visualize
- Several color palettes previews are available
- You can render the multi-plot or individual plots
- You can save both PNG and PDF versions of the plots
Session information
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scGenePanel_0.99.0 Seurat_5.6.0 SeuratObject_5.4.0 sp_2.2-3
## [5] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.23 splines_4.6.1
## [3] later_1.4.8 tibble_3.3.1
## [5] polyclip_1.10-7 janitor_2.2.1
## [7] fastDummies_1.7.6 lifecycle_1.0.5
## [9] rstatix_1.1.0 globals_0.19.1
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## [13] backports_1.5.1 magrittr_2.0.5
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