Hex Binning
spe$counts_MOBP <- counts(spe)[which(rowData(spe)$gene_name=="MOBP"),]
spe$ground_truth <- factor(spe$ground_truth)
# Point Binning version
make_escheR(
spe,
dimred = "PCA"
) |>
add_ground_bin(
var = "ground_truth"
) |>
add_fill_bin(
var = "counts_MOBP"
) +
# Customize aesthetics
scale_fill_gradient(low = "white", high = "black", name = "MOBP Count")+
scale_color_discrete(name = "Spatial Domains") +
theme_minimal()
Note 1: The strategy of binning to avoid overplotting is previously
proposed in schex.
While we provide an implementation in escheR, we would
caution our users that the binning strategy could lead to intermixing of
cluster memberships. In our implementation, the majority membership of
the data points belonging to a bin is selected as the label of the bin.
Users should use the binning strategy under their own discretion, and
interpret the visualization carefully.
Note 2: add_fill_bin() shoudl be applied after
add_ground_bin() for the better visualization outcome.
Image-based SpatialExperiment Object
To demonstrate the principle that escheR can be used to
visualize image-based spatially-resolved data pending optimization, we
include two image-based spatially resolved transcriptomics data
generated via seqFish platform and Slide-seq V2 platform respectively.
The two datasets have been previously curated in the STexampleData
package
seqFISH
library(STexampleData)
library(escheR)
spe_seqFISH <- seqFISH_mouseEmbryo()
make_escheR(spe_seqFISH) |>
add_fill(var = "embryo")
NOTE: trimming down the colData(spe) before piping into
make-escheR could reduce the computation time to make the plots,
specifically when colData(spe) contains extremely large
number of irrelavent features/columns.
SlideSeqV2
library(STexampleData)
library(escheR)
spe_slideseq <- SlideSeqV2_mouseHPC()
make_escheR(spe_slideseq) |>
add_fill(var = "celltype")
Beyond Bioconductor Eco-system
We aim to provide accessibility to all users regardless of their
programming background and preferred single-cell analysis pipelines.
Nevertheless , with limited resource, our sustaining efforts will
prioritize towards the maintenance of the established functionality and
the optimization for image-based spatially resolved data. We regret we
are not be able to provide seamless interface to other R pipelines such
as Seurat and Giotto in foreseeable
future.
Instead, we provide a generic function that works with a
data.frame object as input. For example, relevant features
in Suerat can be easily exported as a
data.frame object manually or via
tidyseurat[https://github.com/stemangiola/tidyseurat]. The exported
data frame can be pipe into escheR.
library(escheR)
library(Seurat)
pbmc_small <- SeuratObject::pbmc_small
pbmc_2pc <- pbmc_small@reductions$pca@cell.embeddings[,1:2]
pbmc_meta <- pbmc_small@meta.data
#> Call generic function for make_escheR.data.frame
make_escheR(
object = pbmc_meta,
.x = pbmc_2pc[,1],
.y = pbmc_2pc[,2]) |>
add_fill(var = "groups")
