| Version: | 1.0.8 |
| Title: | Machine Learning-Assisted, Marker-Based Tool for Single-Cell and Spatial Transcriptomics Annotation |
| Description: | Annotates single-cell and spatial-transcriptomic (ST) data using marker datasets. Supports unified markers list ('Markers_list') creation from built-in databases (e.g., 'Cellmarker2', 'PanglaoDB', 'scIBD', 'TCellSI'), Seurat objects, or user-supplied Excel files. SlimR can predict calculate parameters by machine learning algorithms (e.g., 'Random Forest', 'Gradient Boosting', 'Support Vector Machine', 'Ensemble Learning'), and based on Markers_list, calculate gene expression of different cell types and predict annotation information and calculate corresponding AUC and annotate it, then verify it. At the same time, it can calculate gene expression corresponding to the cell type to generate a reference map for manual annotation (e.g., 'Heat Map', 'Feature Plots', 'Combined Plots'). For more details see Kabacoff (2020, ISBN:9787115420572). |
| License: | MIT + file LICENSE |
| URL: | https://github.com/Zhaoqing-wang/SlimR |
| BugReports: | https://github.com/Zhaoqing-wang/SlimR/issues |
| Depends: | R (≥ 3.5) |
| Imports: | cowplot, dplyr, ggplot2, patchwork, pheatmap, readxl, scales, Seurat, tidyr, tools, tibble |
| Suggests: | crayon, caret, gbm, lattice |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.3 |
| Date: | 2025-10-08 |
| NeedsCompilation: | no |
| Packaged: | 2025-10-07 17:04:43 UTC; Runaw |
| Author: | Zhaoqing Wang 
[aut, cre] |
| Maintainer: | Zhaoqing Wang <zhaoqingwang@mail.sdu.edu.cn> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-08 12:00:13 UTC |
Cellmarker2 dataset
Description
A dataset containing marker genes for different cell types from Cellmarker2
Usage
Cellmarker2
Format
A data frame with 8 columns:
Details
This dataset is used to filter and create a standardized marker list. The dataset can be filtered based on species, tissue class, tissue type, cancer type, and cell type to generate a list of marker genes for specific cell types.
Source
http://117.50.127.228/CellMarker/
See Also
Other Section_0_Database:
Cellmarker2_raw,
Cellmarker2_table,
Markers_list_TCellSI,
Markers_list_scIBD,
PanglaoDB,
PanglaoDB_raw,
PanglaoDB_table
Cellmarker2 raw dataset
Description
A dataset containing marker genes for different cell types from Cellmarker2
Usage
Cellmarker2_raw
Format
A data frame with 20 columns contined in the Cellmarker2 database:
Details
This dataset is used to filter and create a standardized marker list. The dataset can be filtered based on species, tissue class, tissue type, cancer type, and cell type to generate a list of marker genes for specific cell types.
Source
http://117.50.127.228/CellMarker/
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_table,
Markers_list_TCellSI,
Markers_list_scIBD,
PanglaoDB,
PanglaoDB_raw,
PanglaoDB_table
Cellmarker2 table
Description
A dataset containing marker genes for different cell types from Cellmarker2
Usage
Cellmarker2_table
Format
A list contain different types like species, tissue_class, tissue_type, cancer_type, cell_type
Details
This list is used to choose filters for creation of standardized marker list.
Source
http://117.50.127.228/CellMarker/
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_raw,
Markers_list_TCellSI,
Markers_list_scIBD,
PanglaoDB,
PanglaoDB_raw,
PanglaoDB_table
Annotate Seurat Object with SlimR Cell Type Predictions
Description
This function assigns SlimR predicted cell types to a Seurat object based on cluster annotations, and stores the results in the meta.data slot.
Usage
Celltype_Annotation(
seurat_obj,
cluster_col,
SlimR_anno_result,
plot_UMAP = TRUE,
annotation_col = "Cell_type_SlimR"
)
Arguments
seurat_obj |
A Seurat object containing cluster information in meta.data. |
cluster_col |
Character string indicating the column name in meta.data that contains cluster IDs. |
SlimR_anno_result |
List generated by function Celltype_Calculate() which containing a data.frame in $Prediction_results with: 1.cluster_col (Cluster identifiers (should match cluster_col in meta.data)) 2.Predicted_cell_type (Predicted cell types for each cluster). |
plot_UMAP |
logical(1); if TRUE, plot the UMAP with cell type annotations. |
annotation_col |
The location to write in 'meta.data' that contains the predicted cell type. (default = "Cell_type_SlimR") |
Value
A Seurat object with updated meta.data containing the predicted cell types.
Note
If plot_UMAP = TRUE, this function will print a UMAP plot as a side effect.
See Also
Other Section_3_Automated_Annotation_Workflow:
Celltype_Calculate(),
Celltype_Verification(),
Parameter_Calculate()
Examples
## Not run:
sce <- Celltype_Annotation(seurat_obj = sce,
cluster_col = "seurat_clusters",
SlimR_anno_result = SlimR_anno_result,
plot_UMAP = TRUE,
annotation_col = "Cell_type_SlimR"
)
## End(Not run)
Uses "marker_list" to generate combined plot for cell annotation
Description
Uses "marker_list" to generate combined plot for cell annotation
Usage
Celltype_Annotation_Combined(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = NULL,
colour_low = "white",
colour_high = "navy"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
A list of cells and corresponding gene controls, the name of the list is cell type, and the first column of the list corresponds to markers. Lists can be generated using functions such as "Markers_filter_Cellmarker2 ()", "Markers_filter_PanglaoDB ()", "read_excel_markers ()", "read_seurat_markers ()", etc. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = 'seurat_clusters'". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
save_path |
The output path of the cell annotation picture. Example parameters use "save_path = './SlimR/Celltype_annotation_Bar/'". |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
Value
The cell annotation picture is saved in "save_path".
See Also
Other Section_4_Semi_Automated_Annotation_Workflow:
Celltype_Annotation_Features(),
Celltype_Annotation_Heatmap()
Examples
## Not run:
Celltype_Annotation_Combined(seurat_obj = sce,
gene_list = Markers_list,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_Annotation_Combined"),
colour_low = "white",
colour_high = "navy"
)
## End(Not run)
Annotate cell types using features plot with different marker databases
Description
This function dynamically selects the appropriate annotation method
based on the gene_list_type parameter. It supports marker databases from
Cellmarker2, PanglaoDB, Seurat (via FindAllMarkers), or Excel files.
Usage
Celltype_Annotation_Features(
seurat_obj,
gene_list,
gene_list_type = "Default",
species = NULL,
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = NULL,
min_counts = 1,
metric_names = NULL,
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
...
)
Arguments
seurat_obj |
A valid Seurat object with cluster annotations in |
gene_list |
A list of data frames containing marker genes and metrics.
Format depends on |
gene_list_type |
Type of marker database to use. Be one of:
|
species |
Species of the dataset: |
cluster_col |
Column name in |
assay |
Assay layer in the Seurat object (default: |
save_path |
Directory to save output PNGs. Must be explicitly specified. |
min_counts |
Minimum number of counts for Cellmarker2 annotations (default: |
metric_names |
Optional. Change the row name for the input mertics, not recommended unless necessary. (NULL is used as default parameter; used in "Seurat"/"Excel"). |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
colour_low_mertic |
Color for lowest mertic level. (default = "white") |
colour_high_mertic |
Color for highest mertic level. (default = "navy") |
... |
Additional parameters passed to the specific annotation function. |
Value
Saves cell type annotation PNGs in save_path. Returns invisibly.
See Also
Other Section_4_Semi_Automated_Annotation_Workflow:
Celltype_Annotation_Combined(),
Celltype_Annotation_Heatmap()
Examples
## Not run:
# Example for Cellmarker2
Celltype_Annotation_Features(seurat_obj = sce,
gene_list = Markers_list_Cellmarker2,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_Cellmarker2"),
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
# Example for PanglaoDB
Celltype_Annotation_Features(seurat_obj = sce,
gene_list = Markers_list_panglaoDB,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_PanglaoDB")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
# Example for Seurat marker list
Celltype_Annotation_Features(seurat_obj = sce,
gene_list = Markers_list_Seurat,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_Seurat")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
# Example for Excel marker list
Celltype_Annotation_Features(seurat_obj = sce,
gene_list = Markers_list_Excel,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_Excel")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
## End(Not run)
Uses "marker_list" to generate heatmap for cell annotation
Description
Uses "marker_list" to generate heatmap for cell annotation
Usage
Celltype_Annotation_Heatmap(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
min_expression = 0.1,
specificity_weight = 3,
colour_low = "navy",
colour_high = "firebrick3"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
A list of cells and corresponding gene controls, the name of the list is cell type, and the first column of the list corresponds to markers. Lists can be generated using functions such as "Markers_filter_Cellmarker2 ()", "Markers_filter_PanglaoDB ()", "read_excel_markers ()", "read_seurat_markers ()", etc. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = 'seurat_clusters'". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
min_expression |
The min_expression parameter defines a threshold value to determine whether a cell's expression of a feature is considered "expressed" or not. It is used to filter out low-expression cells that may contribute noise to the analysis. Default parameters use "min_expression = 0.1". |
specificity_weight |
The specificity_weight parameter controls how much the expression variability (standard deviation) of a feature within a cluster contributes to its "specificity score." It amplifies or suppresses the impact of variability in the final score calculation.Default parameters use "specificity_weight = 3". |
colour_low |
Color for lowest probability level in Heatmap visualization of probability matrix. (default = "navy") |
colour_high |
Color for highest probability level Heatmap visualization of probability matrix. (default = "firebrick3") |
Value
The heatmap of the comparison between "cluster_col" in the Seurat object and the given gene set "gene_list" needs to be annotated.
See Also
Other Section_4_Semi_Automated_Annotation_Workflow:
Celltype_Annotation_Combined(),
Celltype_Annotation_Features()
Examples
## Not run:
Celltype_Annotation_Heatmap(seurat_obj = sce,
gene_list = Markers_list,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
min_expression = 0.1,
specificity_weight = 3,
colour_low = "navy",
colour_high = "firebrick3"
)
## End(Not run)
Uses "marker_list" to calculate probability, prediction results, AUC and generate heatmap for cell annotation
Description
Uses "marker_list" to calculate probability, prediction results, AUC and generate heatmap for cell annotation
Usage
Celltype_Calculate(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
min_expression = 0.1,
specificity_weight = 3,
threshold = 0.8,
compute_AUC = TRUE,
plot_AUC = TRUE,
AUC_correction = TRUE,
colour_low = "navy",
colour_high = "firebrick3"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
A list of cells and corresponding gene controls, the name of the list is cell type, and the first column of the list corresponds to markers. Lists can be generated using functions such as "Markers_filter_Cellmarker2 ()", "Markers_filter_PanglaoDB ()", "read_excel_markers ()", "read_seurat_markers ()", etc. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = 'seurat_clusters'". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
min_expression |
The min_expression parameter defines a threshold value to determine whether a cell's expression of a feature is considered "expressed" or not. It is used to filter out low-expression cells that may contribute noise to the analysis. Default parameters use "min_expression = 0.1". |
specificity_weight |
The specificity_weight parameter controls how much the expression variability (standard deviation) of a feature within a cluster contributes to its "specificity score." It amplifies or suppresses the impact of variability in the final score calculation.Default parameters use "specificity_weight = 3". |
threshold |
This parameter refers to the normalized similarity between the "alternative cell type" and the "predicted cell type" in the returned results. (the default parameter is 0.8) |
compute_AUC |
Logical indicating whether to calculate AUC values for predicted cell types. AUC measures how well the marker genes distinguish the cluster from others. When TRUE, adds an AUC column to the prediction results. (default: TRUE) |
plot_AUC |
The logic indicates whether to draw an AUC curve for the predicted cell type. When TRUE, add an AUC_plot to result. (default: TRUE) |
AUC_correction |
Logical value controlling AUC-based correction. (default = TRUE) When set to TRUE: 1.Computes AUC values for candidate cell types. (probability > threshold) 2.Selects the cell type with the highest AUC as the final predicted type. 3.Records the selected type's AUC value in the "AUC" column. |
colour_low |
Color for lowest probability level in Heatmap visualization of probability matrix. (default = "navy") |
colour_high |
Color for highest probability level Heatmap visualization of probability matrix. (default = "firebrick3") |
Value
A list containing:
Expression_list: List of expression matrices for each cell type
Proportion_list: List of proportion of expression for each cell type
Expression_scores_matrix: Matrix of expression scores
Probability_matrix: Matrix of normalized probabilities
Prediction_results: Data frame with cluster annotations including:
cluster_col: Cluster identifier
Predicted_cell_type: Primary predicted cell type
AUC: Area Under the Curve value (when compute_AUC = TRUE)
Alternative_cell_types: Semi-colon separated alternative cell types
Heatmap_plot: Heatmap visualization of probability matrix
AUC_plot: AUC visualization of Predicted cell type
See Also
Other Section_3_Automated_Annotation_Workflow:
Celltype_Annotation(),
Celltype_Verification(),
Parameter_Calculate()
Examples
## Not run:
SlimR_anno_result <- Celltype_Calculate(seurat_obj = sce,
gene_list = Markers_list,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
min_expression = 0.1,
specificity_weight = 3,
threshold = 0.8,
compute_AUC = TRUE,
plot_AUC = TRUE,
AUC_correction = TRUE,
colour_low = "navy",
colour_high = "firebrick3"
)
## End(Not run)
Perform cell type verification and generate the validation dotplot
Description
This function performs verification of predicted cell types by selecting high log2FC and high expression proportion genes and generates and generate the validation dotplot.
Usage
Celltype_Verification(
seurat_obj,
SlimR_anno_result,
assay = "RNA",
gene_number = 5,
colour_low = "white",
colour_high = "navy",
annotation_col = "Cell_type_SlimR"
)
Arguments
seurat_obj |
A Seurat object containing single-cell data. |
SlimR_anno_result |
A list containing SlimR annotation results with: Expression_list - List of expression matrices for each cell type. Prediction_results - Data frame with cluster annotations. |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
gene_number |
Integer specifying number of top genes to select per cell type. |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
annotation_col |
Character string specifying the column in meta.data to use for grouping. |
Value
A ggplot object showing expression of top variable genes.
See Also
Other Section_3_Automated_Annotation_Workflow:
Celltype_Annotation(),
Celltype_Calculate(),
Parameter_Calculate()
Examples
## Not run:
Celltype_Verification(seurat_obj = sce,
SlimR_anno_result = SlimR_anno_result,
assay = "RNA",
gene_number = 5,
colour_low = "white",
colour_high = "navy",
annotation_col = "Cell_type_SlimR"
)
## End(Not run)
Uses "marker_list" from Cellmarker2 for cell annotation
Description
Uses "marker_list" from Cellmarker2 for cell annotation
Usage
Celltype_annotation_Cellmarker2(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = NULL,
min_counts = 1,
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
Enter the standard "Marker_list" generated by the Cellmarker2 database for the SlimR package, generated by the "Markers_filter_Cellmarker2 ()" function. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = 'seurat_clusters'". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = "RNA"". |
save_path |
The output path of the cell annotation picture. Example parameters use "save_path = './SlimR/Celltype_annotation_Cellmarker2/'". |
min_counts |
The minimum number of counts of genes in "Marker_list" entered. This number represents the number of the same gene in the same species and the same location in the Cellmarker2 database used for annotation of this cell type. Default parameters use "min_counts = 1". |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
colour_low_mertic |
Color for lowest mertic level. (default = "white") |
colour_high_mertic |
Color for highest mertic level. (default = "navy") |
Value
The cell annotation picture is saved in "save_path".
See Also
Other Section_5_Other_Functions_Provided_by_SlimR:
Celltype_annotation_Excel(),
Celltype_annotation_PanglaoDB(),
Celltype_annotation_Seurat()
Examples
## Not run:
Celltype_annotation_Cellmarker2(seurat_obj = sce,
gene_list = Markers_list_Cellmarker2,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_Cellmarker2")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
## End(Not run)
Uses "marker_list" from Excel input for cell annotation
Description
Uses "marker_list" from Excel input for cell annotation
Usage
Celltype_annotation_Excel(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = NULL,
metric_names = NULL,
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
Enter the standard "Marker_list" generated by the Excel files database for the SlimR package, generated by the "read_excel_markers()" function. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = "seurat_clusters"". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
save_path |
The output path of the cell annotation picture. Example parameters use "save_path = './SlimR/Celltype_annotation_Excel/'". |
metric_names |
Change the row name for the input mertics, not recommended unless necessary. (NULL is used as default parameter) |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
colour_low_mertic |
Color for lowest mertic level. (default = "white") |
colour_high_mertic |
Color for highest mertic level. (default = "navy") |
Value
The cell annotation picture is saved in "save_path".
See Also
Other Section_5_Other_Functions_Provided_by_SlimR:
Celltype_annotation_Cellmarker2(),
Celltype_annotation_PanglaoDB(),
Celltype_annotation_Seurat()
Examples
## Not run:
Celltype_annotation_Excel(seurat_obj = sce,
gene_list = Markers_list_Excel,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_Excel")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
## End(Not run)
Uses "marker_list" from PanglaoDB for cell annotation
Description
Uses "marker_list" from PanglaoDB for cell annotation
Usage
Celltype_annotation_PanglaoDB(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = NULL,
metric_names = NULL,
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
Enter the standard "Marker_list" generated by the PanglaoDB database for the SlimR package, generated by the "Markers_filter_PanglaoDB ()" function. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = 'seurat_clusters'". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
save_path |
The output path of the cell annotation picture. Example parameters use "save_path = './SlimR/Celltype_annotation_PanglaoDB/'". |
metric_names |
Warning: Do not enter information. This parameter is used to check if "Marker_list" conforms to the PanglaoDB database output. |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
colour_low_mertic |
Color for lowest mertic level. (default = "white") |
colour_high_mertic |
Color for highest mertic level. (default = "navy") |
Value
The cell annotation picture is saved in "save_path".
See Also
Other Section_5_Other_Functions_Provided_by_SlimR:
Celltype_annotation_Cellmarker2(),
Celltype_annotation_Excel(),
Celltype_annotation_Seurat()
Examples
## Not run:
Celltype_annotation_PanglaoDB(seurat_obj = sce,
gene_list = Markers_list_panglaoDB,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_PanglaoDB")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
## End(Not run)
Uses "marker_list" from Seurat object for cell annotation
Description
Uses "marker_list" from Seurat object for cell annotation
Usage
Celltype_annotation_Seurat(
seurat_obj,
gene_list,
species,
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = NULL,
metric_names = NULL,
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy"
)
Arguments
seurat_obj |
Enter the Seurat object with annotation columns such as "seurat_cluster" in meta.data to be annotated. |
gene_list |
Enter the standard "Marker_list" generated by the Seurat object database for the SlimR package, generated by the "read_seurat_markers()" function. |
species |
This parameter selects the species "Human" or "Mouse" for standard gene format correction of markers entered by "Marker_list". |
cluster_col |
Enter annotation columns such as "seurat_cluster" in meta.data of the Seurat object to be annotated. Default parameters use "cluster_col = 'seurat_clusters'". |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = 'RNA'". |
save_path |
The output path of the cell annotation picture. Example parameters use "save_path = './SlimR/Celltype_annotation_Seurat/'". |
metric_names |
Change the row name for the input mertics, not recommended unless necessary. (NULL is used as default parameter) |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "navy") |
colour_low_mertic |
Color for lowest mertic level. (default = "white") |
colour_high_mertic |
Color for highest mertic level. (default = "navy") |
Value
The cell annotation picture is saved in "save_path".
See Also
Other Section_5_Other_Functions_Provided_by_SlimR:
Celltype_annotation_Cellmarker2(),
Celltype_annotation_Excel(),
Celltype_annotation_PanglaoDB()
Examples
## Not run:
Celltype_annotation_Seurat(seurat_obj = sce,
gene_list = Markers_list_Seurat,
species = "Human",
cluster_col = "seurat_clusters",
assay = "RNA",
save_path = file.path(tempdir(),"SlimR_Celltype_annotation_Seurat")
colour_low = "white",
colour_high = "navy",
colour_low_mertic = "white",
colour_high_mertic = "navy",
)
## End(Not run)
Create Marker_list from the Cellmarkers2 database
Description
Create Marker_list from the Cellmarkers2 database
Usage
Markers_filter_Cellmarker2(
df,
species = NULL,
tissue_class = NULL,
tissue_type = NULL,
cancer_type = NULL,
cell_type = NULL
)
Arguments
df |
Standardized Cellmarkers2 database. It is read as data(Cellmarkers2) in the SlimR library. |
species |
Species information in Cellmarkers2 database. The default input is "Human" or "Mouse".The input can be retrieved by "Cellmarkers2_table". For more information,please refer to http://117.50.127.228/CellMarker/ on Cellmarkers2's official website. |
tissue_class |
Tissue_class information in Cellmarkers2 database. The input can be retrieved by "Cellmarkers2_table". For more information, please refer to http://117.50.127.228/CellMarker/ on Cellmarkers2's official website. |
tissue_type |
Tissue_type information in Cellmarkers2 database. The input can be retrieved by "Cellmarkers2_table". For more information, please refer to http://117.50.127.228/CellMarker/ on Cellmarkers2's official website. |
cancer_type |
Cancer_type information in Cellmarkers2 database. The input can be retrieved by "Cellmarkers2_table". For more information, please refer to http://117.50.127.228/CellMarker/ on Cellmarkers2's official website. |
cell_type |
Cell_type information in Cellmarkers2 database. The input can be retrieved by "Cellmarkers2_table". For more information, please refer to http://117.50.127.228/CellMarker/ on Cellmarkers2's official website. |
Value
The standardized "Marker_list" in the SlimR package
See Also
Other Section_2_Standardized_Markers_List_Input:
Markers_filter_PanglaoDB(),
Read_excel_markers(),
Read_seurat_markers()
Examples
Cellmarker2 <- SlimR::Cellmarker2
Markers_list_Cellmarker2 <- Markers_filter_Cellmarker2(
Cellmarker2,
species = "Human",
tissue_class = "Intestine",
tissue_type = NULL,
cancer_type = NULL,
cell_type = NULL
)
Create Marker_list from the PanglaoDB database
Description
Create Marker_list from the PanglaoDB database
Usage
Markers_filter_PanglaoDB(df, species_input, organ_input)
Arguments
df |
Standardized PanglaoDB database. It is read as data(PanglaoDB) in the SlimR library. |
species_input |
Species information in PanglaoDB database. The default input is "Human" or "Mouse".The input can be retrieved by "PanglaoDB_table". For more information,please refer to https://panglaodb.se/ on PanglaoDB's official website. |
organ_input |
Organ type information in the PanglaoDB database. The input can be retrieved by "PanglaoDB_table".For more information, please refer to https://panglaodb.se/ on PanglaoDB's official website. |
Value
The standardized "Marker_list" in the SlimR package
See Also
Other Section_2_Standardized_Markers_List_Input:
Markers_filter_Cellmarker2(),
Read_excel_markers(),
Read_seurat_markers()
Examples
PanglaoDB <- SlimR::PanglaoDB
Markers_list_panglaoDB <- Markers_filter_PanglaoDB(
PanglaoDB,
species_input = 'Human',
organ_input = 'GI tract'
)
List of cell type markers in the TCellSI dataset
Description
A dataset containing marker genes for different T cell types from TCellSI
Usage
Markers_list_TCellSI
Format
A list with ten tables.
Details
This list is a table of 10 types of T cell markers obtained from TCellSI. The data source is "https://github.com/GuoBioinfoLab/TCellSI/blob/main/data/markers.rda", and the reference literature is: Yang et al. (2024) doi:10.1002/imt2.231.
Source
https://github.com/GuoBioinfoLab/TCellSI/
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_raw,
Cellmarker2_table,
Markers_list_scIBD,
PanglaoDB,
PanglaoDB_raw,
PanglaoDB_table
List of cell type markers in the scIBD dataset
Description
A dataset containing marker genes for different human intestine cell types from scIBD
Usage
Markers_list_scIBD
Format
A list with one hundred and one tables.
Details
This list is a table of 101 types of human intestine cell types markers obtained from scIBD. The article doi source is "https://doi.org/10.1038/s43588-023-00464-9", and the reference literature is: Nie et al. (2023) doi:10.1038/s43588-023-00464-9. Note: The 'Markers_list_scIBD' was generated using section 2.5.2 and the parameters 'sort_by = "logFC"' and 'gene_filter = 20' were set.
Source
doi:10.1038/s43588-023-00464-9
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_raw,
Cellmarker2_table,
Markers_list_TCellSI,
PanglaoDB,
PanglaoDB_raw,
PanglaoDB_table
PanglaoDB dataset
Description
A dataset containing marker genes for different cell types from PanglaoDB
Usage
PanglaoDB
Format
A data frame with 9 columns:
Details
This dataset is used to filter and create a standardized marker list.'
Source
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_raw,
Cellmarker2_table,
Markers_list_TCellSI,
Markers_list_scIBD,
PanglaoDB_raw,
PanglaoDB_table
PanglaoDB raw dataset
Description
A dataset containing marker genes for different cell types from PanglaoDB
Usage
PanglaoDB_raw
Format
A data frame with 14 columns contined in the PanglaoDB database:
Details
This dataset is used to filter and create a standardized marker list.'
Source
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_raw,
Cellmarker2_table,
Markers_list_TCellSI,
Markers_list_scIBD,
PanglaoDB,
PanglaoDB_table
PanglaoDB table
Description
A dataset containing marker genes for different cell types from PanglaoDB
Usage
PanglaoDB_table
Format
A list contain different types like species, organ, cell type.
Details
This list is used to choose filters for creation of standardized marker list.
Source
See Also
Other Section_0_Database:
Cellmarker2,
Cellmarker2_raw,
Cellmarker2_table,
Markers_list_TCellSI,
Markers_list_scIBD,
PanglaoDB,
PanglaoDB_raw
Adaptive Parameter Tuning for Single-Cell Data Annotation in SlimR
Description
This function uses machine learning to automatically determine optimal min_expression and specificity_weight parameters for single-cell data analysis based on dataset characteristics.
Usage
Parameter_Calculate(
seurat_obj,
features,
assay = NULL,
cluster_col = NULL,
method = "ensemble",
n_models = 3,
return_model = FALSE,
verbose = TRUE
)
Arguments
seurat_obj |
A Seurat object containing single-cell data |
features |
Character vector of feature names (genes) to analyze |
assay |
Name of assay to use (default: default assay) |
cluster_col |
Column name in metadata containing cluster information |
method |
Machine learning method: "rf" (random forest), "gbm" (gradient boosting), "svm" (support vector machine), or "ensemble" (default) |
n_models |
Number of models for ensemble learning (default: 3) |
return_model |
Whether to return trained model (default: FALSE) |
verbose |
Whether to print progress messages (default: TRUE) |
Value
A list containing:
min_expression: Recommended expression threshold
specificity_weight: Recommended specificity weight
performance: Model performance metric (R-squared)
dataset_features: Extracted dataset characteristics
model: Trained model (if return_model = TRUE)
See Also
Other Section_3_Automated_Annotation_Workflow:
Celltype_Annotation(),
Celltype_Calculate(),
Celltype_Verification()
Examples
## Not run:
# Basic usage
SlimR_params <- Parameter_Calculate(
seurat_obj = sce,
features = c("CD3E", "CD4", "CD8A"),
assay = "RNA",
cluster_col = "seurat_clusters",
method = "ensemble",
n_models = 3,
return_model = FALSE,
verbose = TRUE
)
# Use with custom method
SlimR_params <- Parameter_Calculate(
seurat_obj = sce,
features = unique(Markers_list_Cellmarker2$`B cell`$marker),
assay = "RNA",
cluster_col = "seurat_clusters",
method = "rf",
return_model = FALSE,
verbose = TRUE
)
## End(Not run)
Create "Marker_list" from Excel files ".xlsx"
Description
Create "Marker_list" from Excel files ".xlsx"
Usage
Read_excel_markers(path)
Arguments
path |
The path information of Marker files stored in ".xlsx" format. The Sheet name in the file is filled with cell type. The first line of each Sheet is the table head, the first column is filled with markers information, and the following column is filled with mertic information. |
Value
The standardized "Marker_list" in the SlimR package.
See Also
Other Section_2_Standardized_Markers_List_Input:
Markers_filter_Cellmarker2(),
Markers_filter_PanglaoDB(),
Read_seurat_markers()
Examples
## Not run:
Markers_list_Excel <- Read_excel_markers(
"D:/Laboratory/Marker_load.xlsx"
)
## End(Not run)
Create "Marker_list" from Seurat object
Description
Create "Marker_list" from Seurat object
Usage
Read_seurat_markers(
df,
sources = c("Seurat", "presto"),
sort_by = "FSS",
gene_filter = 20
)
Arguments
df |
Dataframe generated by "FindAllMarkers" function, recommend to use parameter "group.by = "Cell_type"" and "only.pos = TRUE". |
sources |
Type of markers sources to use. Be one of: |
sort_by |
Marker sorting parameter, for Seurat sources, select "avg_log2FC" or
"p_val_adj" or "FSS" (Feature Significance Score, FSS, product value of |
gene_filter |
The number of markers left for each cell type based on the "sort_by" parameter's level of difference. Default parameters use "gene_fliter = 20" |
Value
The standardized "Marker_list" in the SlimR package.
See Also
Other Section_2_Standardized_Markers_List_Input:
Markers_filter_Cellmarker2(),
Markers_filter_PanglaoDB(),
Read_excel_markers()
Examples
## Not run:
# Example for Seurat sources markers
seurat_markers <- Seurat::FindAllMarkers(
object = sce,
group.by = "Cell_type",
only.pos = TRUE)
Markers_list_Seurat <- Read_seurat_markers(seurat_markers,
sources = "Seurat",
sort_by = "avg_log2FC",
gene_filter = 20
)
# Example for presto sources markers
seurat_markers <- dplyr::filter(
presto::wilcoxauc(
X = sce,
group_by = "Cell_type",
seurat_assay = "RNA"
),
padj < 0.05, logFC > 0.5
)
Markers_list_Seurat <- Read_seurat_markers(seurat_markers,
sources = "presto",
sort_by = "logFC",
gene_filter = 20
)
## End(Not run)
Calculate Cluster Variability
Description
Measures the degree of separation between different cell clusters based on expression patterns.
Usage
calculate_cluster_variability(data.features, features)
Arguments
data.features |
Data frame containing expression data and cluster labels |
features |
Feature names to include in analysis |
Value
Numeric value representing cluster separation strength
See Also
Other Section_1_Functions_Use_in_Package:
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Counts average expression of gene set (Use in package)
Description
Counts average expression of gene set (Use in package)
Usage
calculate_expression(
object,
features,
assay = NULL,
cluster_col = NULL,
colour_low = "white",
colour_high = "navy"
)
Arguments
object |
Enter a Seurat object. |
features |
Enter one or a set of markers. |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = NULL". |
cluster_col |
Enter the meta.data column in the Seurat object to be annotated, such as "seurat_cluster". Default parameters use "cluster_col = NULL". |
colour_low |
Color for lowest expression level. (default = "white") |
colour_high |
Color for highest expression level. (default = "black") |
Value
Average expression genes and relatied informations in the input "Seurat" object given "cluster_col" and given "features".
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Calculate Expression Distribution Skewness
Description
Computes the average skewness of gene expression distributions across all features.
Usage
calculate_expression_skewness(expression_matrix)
Arguments
expression_matrix |
Matrix of expression values |
Value
Mean absolute skewness across all genes
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Calculate gene set expression and infer probabilities with control datasets (Use in package)
Description
Calculate gene set expression and infer probabilities with control datasets (Use in package)
Usage
calculate_probability(
object,
features,
assay = NULL,
cluster_col = NULL,
min_expression = 0.1,
specificity_weight = 3
)
Arguments
object |
Enter a Seurat object. |
features |
Enter one or a set of markers. |
assay |
Enter the assay used by the Seurat object, such as "RNA". Default parameters use "assay = NULL". |
cluster_col |
Enter the meta.data column in the Seurat object to be annotated, such as "seurat_cluster". Default parameters use "cluster_col = NULL". |
min_expression |
The min_expression parameter defines a threshold value to determine whether a cell's expression of a feature is considered "expressed" or not. It is used to filter out low-expression cells that may contribute noise to the analysis. Default parameters use "min_expression = 0.1". |
specificity_weight |
The specificity_weight parameter controls how much the expression variability (standard deviation) of a feature within a cluster contributes to its "specificity score." It amplifies or suppresses the impact of variability in the final score calculation.Default parameters use "specificity_weight = 3". |
Value
Average expression of genes in the input "Seurat" object given "cluster_col" and given "features".
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Estimate Batch Effect Strength
Description
Roughly estimates the potential impact of batch effects using available metadata.
Usage
estimate_batch_effect(seurat_obj, assay)
Arguments
seurat_obj |
Seurat object |
assay |
Assay name |
Value
Batch effect score (0 indicates no detectable batch effect)
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Extract Dataset Characteristics for Machine Learning
Description
Computes various statistical features from single-cell data that are used as input for the parameter prediction model.
Usage
extract_dataset_features(
seurat_obj,
features,
assay = NULL,
cluster_col = NULL
)
Arguments
seurat_obj |
Seurat object |
features |
Features to analyze |
assay |
Assay name |
cluster_col |
Cluster column name |
Value
List of dataset characteristics including expression statistics, variability measures, and cluster properties
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Generate Training Data for Machine Learning Model
Description
Creates synthetic training data based on empirical rules about optimal parameter relationships with dataset characteristics.
Usage
generate_training_data(dataset_features, n_samples = 1000)
Arguments
dataset_features |
List of actual dataset characteristics |
n_samples |
Number of synthetic samples to generate |
Value
Data frame with synthetic features and optimal parameter targets
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
postprocess_parameters(),
predict_optimal_parameters(),
train_parameter_model()
Post-process Predicted Parameters
Description
Applies constraints and dataset-specific adjustments to ensure predicted parameters are within reasonable ranges.
Usage
postprocess_parameters(predicted_params, dataset_features)
Arguments
predicted_params |
List of raw predicted parameters |
dataset_features |
Characteristics of current dataset |
Value
List of finalized parameters after post-processing
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
predict_optimal_parameters(),
train_parameter_model()
Predict Optimal Parameters Using Trained Model
Description
Applies the trained machine learning model to predict optimal parameters for the current dataset.
Usage
predict_optimal_parameters(model, dataset_features)
Arguments
model |
Trained machine learning model (now a list with two models) |
dataset_features |
Extracted characteristics of current dataset |
Value
List containing predicted min_expression and specificity_weight
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
train_parameter_model()
Train Parameter Prediction Model
Description
Trains machine learning models to predict optimal parameters based on dataset characteristics.
Usage
train_parameter_model(
training_data,
method = "ensemble",
n_models = 3,
verbose = TRUE
)
Arguments
training_data |
Data frame with features and target parameters |
method |
Machine learning method to use |
n_models |
Number of models for ensemble learning |
verbose |
Whether to print training progress |
Value
List containing trained model and performance metrics
See Also
Other Section_1_Functions_Use_in_Package:
calculate_cluster_variability(),
calculate_expression(),
calculate_expression_skewness(),
calculate_probability(),
estimate_batch_effect(),
extract_dataset_features(),
generate_training_data(),
postprocess_parameters(),
predict_optimal_parameters()