Starting from Version 1.2.0, escheR
package supports additional two data structures as input, including SpatialExperiment
and data.frame
from base
R. In addition, escheR
supports in-situ visualization of image-based spatially resolved data, which will be the focus of future development.
SingleCellExperiment
SpatialExperiment
inherits SingleCellExperiment
Following the same syntax, one can also visualize dimensionality reduced embeddings of a SpatialExperiment
object by providing the argument dimred
with a non-null value. Hence, the first 2 columns of the corresponding reducedDim(spe)
assay will be used as the x-y coordinate of the plot, replacing spatialCoords(spe)
.
library(escheR)
library(STexampleData)
library(scater)
library(scran)
spe <- Visium_humanDLPFC() |>
logNormCounts()
spe <- spe[, spe$in_tissue == 1]
spe <- spe[, !is.na(spe$ground_truth)]
top.gene <- getTopHVGs(spe, n=500)
set.seed(100) # See below.
spe <- runPCA(spe, subset_row = top.gene)
make_escheR(
spe,
dimred = "PCA"
) |>
add_fill(var = "ground_truth") +
theme_minimal()
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 inescheR
, 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 afteradd_ground_bin()
for the better visualization outcome.
SpatialExperiment
ObjectTo 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
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 whencolData(spe)
contains extremely large number of irrelavent features/columns.
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")
utils::sessionInfo()
#> R version 4.4.0 RC (2024-04-16 r86468)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
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#> attached base packages:
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