View source: R/Seurat.Utils.Visualization.R
plot3D.umap.gene | R Documentation |
Plot a 3D umap with gene expression. Uses plotly. Based on github.com/Dragonmasterx87.
plot3D.umap.gene(
gene = "TOP2A",
obj = combined.obj,
annotate.by = GetNamedClusteringRuns(obj = obj, v = FALSE)[1],
quantileCutoff = 0.99,
def.assay = c("integrated", "RNA")[2],
suffix = NULL,
alpha = 0.5,
dotsize = 1.25,
col.names = c("umap_1", "umap_2", "umap_3"),
assay = "RNA",
...
)
gene |
The gene of interest. Default: 'TOP2A' |
obj |
The Seurat object for which the 3D umap plot will be generated. Default: combined.obj |
annotate.by |
The cluster or grouping to be used for automatic annotation. Default: First returned result from GetNamedClusteringRuns(obj) function. |
quantileCutoff |
Cutoff value for the quantile for clipping outliers in the gene expression data. Default: 0.99 |
def.assay |
The default assay to be used. Choose between "integrated" and "RNA". Default: "RNA" |
suffix |
A suffix added to the filename. Default: NULL |
alpha |
Opacity of the points in the plot. Default: 0.5 |
dotsize |
The size of the dots in the plot. Default: 1.25 |
... |
Pass any other parameter to the internally called |
## Not run:
if (interactive()) {
plot3D.umap.gene(obj = combined.obj, gene = "DDIT4", quantileCutoff = .95)
plot3D.umap.gene(obj = combined.obj, gene = "percent.mito", quantileCutoff = .95) # for continous meta variables
plot3D.umap.gene(obj = combined.obj, gene = "nFeature_RNA", quantileCutoff = .95) # for continous meta variables
}
## End(Not run)
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