The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
SlimR is an R package designed for annotating single-cell and spatial-transcriptomics (ST) datasets. It supports the creation of a unified marker list (‘Markers_list’) using multiple sources including: user-provided Excel tables mapping cell types to markers, Seurat objects containing cell label information, and the package’s built-in curated species-specific cell type and marker reference databases (e.g., ‘Cellmarker2’, ‘PanglaoDB’).
Based on the Markers_list, ‘SlimR’ enables one-click generation of annotation heatmaps (‘Annotation_heatmap’) visualizing relationships between input cell types and reference marker lists. Additionally, it can iterate through different cell types to generate corresponding annotation reference plots (e.g., ‘Markers_dotplot’, ‘Metric_heatmap’, ‘Mean_expression_box_plot’).
Install SlimR directly from GitHub using:
::install_github("Zhaoqing-wang/SlimR") devtools
Load the package in your R environment:
library(SlimR)
SlimR requires R (≥ 3.5) and depends on the following packages:
cowplot
, dplyr
, ggplot2
,
patchwork
, pheatmap
, readxl
,
scales
, Seurat
, tidyr
,
tools
. Install missing dependencies using:
# Install dependencies if needed:
install.packages(c("cowplot", "dplyr", "ggplot2", "patchwork",
"pheatmap", "readxl", "scales", "Seurat",
"tidyr", "tools"))
SlimR requires a standardized list format for storing marker information, metrics, and corresponding cell types (list names = cell types, first column = markers, subsequent columns = metrics).
Format Requirements:
- Each sheet name = cell type
- First row = column headers
- First column = markers
- Subsequent columns = metrics
<- read_excel_markers("D:/Laboratory/Marker_load.xlsx") Markers_list_Excel
Note: Output usable in sections 3.1, 3.2, and 4.1.
First identify cluster features:
<- FindAllMarkers(
seurat_markers
sce.all, group.by = "Cell_type",
only.pos = TRUE
)
Then generate marker list:
<- read_seurat_markers(
Markers_list_Seurat
seurat_markers,sort_by = "avg_log2FC",
gene_filter = 10
)
Note: Output usable in sections 3.1, 3.2, and 4.2.
Load the database:
<- SlimR::Cellmarker2 Cellmarker2
Optional metadata exploration:
<- SlimR::Cellmarker2_table
Cellmarker2_table View(Cellmarker2_table)
Generate marker list:
<- Markers_filter_Cellmarker2(
Markers_list_Cellmarker2
Cellmarker2,species = "Human",
tissue_class = "Intestine",
tissue_type = NULL,
cancer_type = NULL,
cell_type = NULL,
cell_name = NULL,
marker = NULL,
counts = NULL
)
Note: Output usable in sections 3.1, 3.2, and 4.3.
Load the database:
<- SlimR::PanglaoDB PanglaoDB
Optional metadata exploration:
<- SlimR::PanglaoDB_table
PanglaoDB_table View(PanglaoDB_table)
Generate marker list:
<- Markers_filter_PanglaoDB(
Markers_list_panglaoDB
PanglaoDB,species_input = 'Human',
organ_input = 'GI tract'
)
Note: Output usable in sections 3.1, 3.2, and 4.4.
Generates a heatmap comparing marker expression across cell clusters:
Celltype_annotation_Heatmap(
seurat_obj = sce,
gene_list = Markers_list,
species = "Human",
cluster_col = "seurat_cluster",
min_expression = 0.1,
specificity_weight = 3
)
Generates per-cell-type expression box plots:
Celltype_annotation_Box(
seurat_obj = sce,
gene_list = Markers_list,
species = "Human",
cluster_col = "seurat_cluster",
assay = "RNA",
save_path = "./SlimR/Celltype_annotation_Bar/"
)
Generates integrated dot plots and metric heatmaps:
Celltype_annotation_Excel(
seurat_obj = sce,
gene_list = Markers_list_Excel,
species = "Human",
cluster_col = "seurat_cluster",
assay = "RNA",
save_path = "./SlimR/Celltype_annotation_Excel/"
)
Celltype_annotation_Seurat(
seurat_obj = sce,
gene_list = Markers_list_Seurat,
species = "Human",
cluster_col = "seurat_cluster",
assay = "RNA",
save_path = "./SlimR/Celltype_annotation_Seurat/"
)
Celltype_annotation_Cellmarker2(
seurat_obj = sce,
gene_list = Markers_list_Cellmarker2,
species = "Human",
cluster_col = "seurat_cluster",
assay = "RNA",
save_path = "./SlimR/Celltype_annotation_Cellmarkers2.0/"
)
Celltype_annotation_PanglaoDB(
seurat_obj = sce,
gene_list = Markers_list_panglaoDB,
species = "Human",
cluster_col = "seurat_cluster",
assay = "RNA",
save_path = "./SlimR/Celltype_annotation_PanglaoDB/"
)
Thank you for using SlimR. For questions, issues, or suggestions, please contact:
Zhaoqing Wang
📧 851091628@qq.com; zhaoqingwang@mail.sdu.edu.cn
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.