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 4.4.1 (2024-06-14)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Ventura 13.6.7
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] BumpyMatrix_1.14.0 scran_1.34.0
#> [3] scater_1.34.0 scuttle_1.16.0
#> [5] ggpubr_0.6.0 STexampleData_1.13.3
#> [7] SpatialExperiment_1.16.0 SingleCellExperiment_1.28.0
#> [9] SummarizedExperiment_1.36.0 Biobase_2.66.0
#> [11] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0
#> [13] IRanges_2.40.0 S4Vectors_0.44.0
#> [15] MatrixGenerics_1.18.0 matrixStats_1.4.1
#> [17] ExperimentHub_2.14.0 AnnotationHub_3.14.0
#> [19] BiocFileCache_2.14.0 dbplyr_2.5.0
#> [21] BiocGenerics_0.52.0 escheR_1.6.0
#> [23] ggplot2_3.5.1 BiocStyle_2.34.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_1.8.9 magrittr_2.0.3
#> [4] ggbeeswarm_0.7.2 magick_2.8.5 farver_2.1.2
#> [7] rmarkdown_2.29 zlibbioc_1.52.0 vctrs_0.6.5
#> [10] memoise_2.0.1 rstatix_0.7.2 tinytex_0.54
#> [13] htmltools_0.5.8.1 S4Arrays_1.6.0 curl_6.0.0
#> [16] BiocNeighbors_2.0.0 broom_1.0.7 SparseArray_1.6.0
#> [19] Formula_1.2-5 sass_0.4.9 bslib_0.8.0
#> [22] cachem_1.1.0 igraph_2.1.1 mime_0.12
#> [25] lifecycle_1.0.4 pkgconfig_2.0.3 rsvd_1.0.5
#> [28] Matrix_1.7-1 R6_2.5.1 fastmap_1.2.0
#> [31] GenomeInfoDbData_1.2.13 digest_0.6.37 colorspace_2.1-1
#> [34] AnnotationDbi_1.68.0 dqrng_0.4.1 irlba_2.3.5.1
#> [37] RSQLite_2.3.7 beachmat_2.22.0 filelock_1.0.3
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#> [52] hexbin_1.28.4 ggsignif_0.6.4 rappdirs_0.3.3
#> [55] DelayedArray_0.32.0 rjson_0.2.23 bluster_1.16.0
#> [58] tools_4.4.1 vipor_0.4.7 beeswarm_0.4.0
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#> [103] scales_1.3.0 purrr_1.0.2 crayon_1.5.3
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