Select cells from reduction maps and spatial locations

This vignette demonstrates how to use dST build functions to interactively select or pick cells/spots from a plot using your mouse and keyboard. While tools like CellxGene are widely used for this purpose, to the best of our knowledge, an efficient solution for performing this task within the R environment is still lacking.

Prepare the data.

We use demo data from SeuratData for simplity.

require(Seurat)
require(SeuratData)
require(dST)

InstallData("stxBrain")
Warning: The following packages are already installed and will not be
reinstalled: stxBrain
brain <- LoadData("stxBrain", type = "anterior1")
brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)
brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
SpatialPlot(brain)

Select spots from spatial plot

sel.1 <- SpatialSelector(brain)

Press ESC on your keyboard to exist the selection!

Now the selected cells will be exported to sel.1. If you want return a object, try to set return.object=TRUE in the function.

Select cells from dimension reduction plot

We can also select cells from dimension reduction plot.

DimPlot(brain)

sel.1 <- DimSelector(brain)

Press ESC on your keyboard to exist the selection!

Select cells based on a feature expression

FeaturePlot(brain, features = c('Ttr'), order=TRUE)

FeatureSelector(brain, feature = c('Ttr'), order=TRUE)

Please note in FeatureSelector() only one feature is support at each selection. Therefore, I designed the parameter feature instead of orignal features here.

Select cells from Image-based spatial data

Here we will use 10x Genomics Xenium data, generated from Seurat’s tutorial.

xenium.obj <- readRDS("xenium.rds")
ImageDimPlot(xenium.obj, border.color = "white", border.size = 0.1, cols = "polychrome")
Warning: No FOV associated with assay 'SCT', using global default FOV

ImageDimSelector(xenium.obj, border.color = "white", border.size = 0.1)

Generate 2D concave hull of selected region

In spatial transcriptomics, it is sometimes necessary to manually define capsule or membrane regions, as these areas are often thin, mixed with neighboring cells, and difficult to identify accurately. Manually specifying these regions can be beneficial for downstream analyses, such as zonation analysis, where precise spatial organization of cells is critical for understanding tissue architecture and functional gradients.

sel <- SpatialConcaveHull(brain)

Questions?

If you have any questions regarding this vignette, the usage of dST or suggestions, please feel free to report them through the discussion forum.

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