RunSlingshot | R Documentation |
Runs the Slingshot algorithm on a Seurat object.
RunSlingshot(
srt,
group.by,
reduction = NULL,
dims = NULL,
start = NULL,
end = NULL,
prefix = NULL,
reverse = FALSE,
align_start = FALSE,
show_plot = TRUE,
lineage_palette = "Dark2",
seed = 11,
...
)
srt |
A Seurat object. |
group.by |
The variable to group the cells by. |
reduction |
The reduction technique to use for dimensionality reduction. Default is NULL, which uses the default reduction for the Seurat object. |
dims |
The dimensions to use for the Slingshot algorithm. Default is NULL, which uses first two dimensions. |
start |
The starting group for the Slingshot algorithm. Default is NULL. |
end |
The ending group for the Slingshot algorithm. Default is NULL. |
prefix |
The prefix to add to the column names of the resulting pseudotime variable. Default is NULL. |
reverse |
Logical value indicating whether to reverse the pseudotime variable. Default is FALSE. |
align_start |
Logical value indicating whether to align the starting pseudotime values at the maximum pseudotime. Default is FALSE. |
show_plot |
Logical value indicating whether to show the dimensionality plot. Default is TRUE. |
lineage_palette |
The color palette to use for the lineages in the plot. Default is "Dark2". |
seed |
The random seed to use for reproducibility. Default is 11. |
... |
Additional arguments to be passed to the |
CellDimPlot
RunDynamicFeatures
data("pancreas_sub")
pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP")
pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "PCA", dims = 1:10)
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:2), lineages_span = 0.1)
# 3D lineage
pancreas_sub <- Standard_SCP(pancreas_sub)
pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "StandardpcaUMAP3D")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:3), lineages_span = 0.1, lineages_trim = c(0.05, 0.95))
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