docs/banksy.md
In satijalab/seurat-wrappers: Community-Provided Methods and Extensions for the Seurat Object
Running BANKSY with Seurat
Compiled: April 04, 2024
- Introduction
- Overview
- Running BANKSY within Seurat’s spatial
framework
- Running BANKSY with locations provided
explicitly
- Multi-sample analysis
- Spatial data integration with
Harmony
- Getting help
Introduction
In this vignette, we describe how to run BANKSY with Seurat objects. If
you use BANKSY in your research, please cite
BANKSY unifies cell typing and tissue domain segmentation for
scalable spatial omics data analysis
Vipul Singhal, Nigel Chou, Joseph Lee, Yifei Yue, Jinyue Liu, Wan Kee
Chock, Li Lin, Yun-Ching Chang, Erica Mei Ling Teo, Jonathan Aow, Hwee
Kuan Lee, Kok Hao Chen & Shyam Prabhakar
Nature Genetics, 2024
BANKSY is a method that incorporates neighborhood information for
clustering spatial omics data. By doing so, BANKSY is able to
- improve cell-type assignment in noisy data
- distinguish subtly different cell-types stratified by microenvironment
- identify spatial domains sharing the same microenvironment
The amount of neighborhood information incorporated is controlled by a
parameter lambda in [0,1], with higher values giving more weight to
the neighbourhood information during clustering.
Overview
The RunBanksy function implemented with the SeuratWrappers package
allows users to run BANKSY with Seurat objects. We describe two options
of running RunBanksy. The first is within Seurat’s spatial framework
(see here
and
here)
and requires a Seurat object and a lambda parameter as mandatory input.
The second option works with Seurat objects that do not have spatial
information stored within, and therefore requires an additional argument
giving the locations of the cell centroids or spots.
Caveat: ScaleData should not be run after a call to RunBanksy;
RunBanksy populates the scale.data slot with the scaled BANKSY
matrix. Calling ScaleData after RunBanksy performs gene-wise
z-scaling, negating the effect of lambda.
Prerequisites to install:
library(Banksy)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
library(ggplot2)
library(gridExtra)
library(pals)
# Kelly palette for visualization
mypal <- kelly()[-1]
Running BANKSY within Seurat’s spatial framework
We demonstrate how to run BANKSY within Seurat’s spatial analysis
framework with a mouse hippocampus Slide-seq v2 dataset from the
SeuratData package.
After installing SeuratData, the data can be accessed as follows:
InstallData('ssHippo')
ss.hippo <- LoadData("ssHippo")
We perform simple preprocessing by filtering beads with high mito
percentage and keeping only beads within the 5th and 98th percentile of
total UMI counts. To keep runtime of this vignette short, we downsample
the data to 10,000 beads.
# Filtering
ss.hippo[['percent.mt']] <- PercentageFeatureSet(ss.hippo, pattern = '^MT-')
ss.hippo <- subset(ss.hippo, percent.mt < 10 &
nCount_Spatial > quantile(ss.hippo$nCount_Spatial, 0.05) &
nCount_Spatial < quantile(ss.hippo$nCount_Spatial, 0.98))
# Downsample
set.seed(42)
ss.hippo <- ss.hippo[,sample(colnames(ss.hippo), 1e4)]
Next, normalize the data and find variable genes:
# Normalize
ss.hippo <- NormalizeData(ss.hippo)
ss.hippo <- FindVariableFeatures(ss.hippo)
ss.hippo <- ScaleData(ss.hippo)
To run BANKSY, we specify the following:
lambda: a numeric value in [0,1]. With low values of lambda,
BANKSY operates in cell-typing mode, while high values of lambda find
spatial domains.assay and slot: determines where to pull the expression data fromfeatures: specifies features for downstream analysis. This can be
'all', 'variable' or a subset of features. k_geom: the number of neighbors that defines a cell’s neighborhood
Call ?RunBanksy for more details on function parameters.
# Run BANKSY
ss.hippo <- RunBanksy(ss.hippo, lambda = 0.2, verbose=TRUE,
assay = 'Spatial', slot = 'data', features = 'variable',
k_geom = 15)
ss.hippo
## An object of class Seurat
## 27264 features across 10000 samples within 2 assays
## Active assay: BANKSY (4000 features, 0 variable features)
## 2 layers present: data, scale.data
## 1 other assay present: Spatial
## 1 image present: image
Note that the RunBanksy function sets the default assay to BANKSY (
determined by the assay_name argument) and fills the scale.data
slot. Users should not call ScaleData on the BANKSY assay as this
negates the effects of lambda.
The rest of the pipeline is similar to the ‘default’ Seurat pipline. We
scale the data and run dimensionality reduction with PCA and UMAP:
# Run PCA and UMAP
ss.hippo <- RunPCA(ss.hippo, assay = 'BANKSY', features = rownames(ss.hippo), npcs = 30)
ss.hippo <- RunUMAP(ss.hippo, dims = 1:30)
Next, find BANKSY clusters:
# Clustering
ss.hippo <- FindNeighbors(ss.hippo, dims = 1:30)
ss.hippo <- FindClusters(ss.hippo, resolution = 0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10000
## Number of edges: 365658
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9033
## Number of communities: 13
## Elapsed time: 1 seconds
Visualize the UMAP and Spatial plot:
# Viz
grid.arrange(
DimPlot(ss.hippo, pt.size = 0.25, label = TRUE, label.size = 3, repel = TRUE),
SpatialDimPlot(ss.hippo, stroke = NA, label = TRUE, label.size = 3,
repel = TRUE, alpha = 0.5, pt.size.factor = 2),
ncol = 2
)
Find markers based on the BANKSY clusters and visualize them. Here, we
find differentially expressed genes between the CA1 and CA3 regions.
# Find markers
DefaultAssay(ss.hippo) <- 'Spatial'
markers <- FindMarkers(ss.hippo, ident.1 = 4, ident.2 = 9, only.pos = F,
logfc.threshold = 1, min.pct = 0.5)
markers <- markers[markers$p_val_adj < 0.01,]
markers
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## SNAP25 1.127235e-46 -1.260312 0.658 0.823 2.622400e-42
## CHGB 9.840001e-44 -1.985343 0.439 0.697 2.289178e-39
## STMN2 1.281230e-24 -1.430138 0.335 0.574 2.980653e-20
## SYN2 3.272800e-23 -1.609355 0.332 0.564 7.613842e-19
## ATP2B1 1.545647e-22 1.251540 0.639 0.474 3.595793e-18
## CPLX2 4.619232e-21 -1.220110 0.289 0.522 1.074618e-16
## PRKCB 1.276453e-18 1.394809 0.552 0.341 2.969539e-14
## PCP4 2.006224e-18 -1.269671 0.379 0.578 4.667279e-14
## TUBB2A 1.330787e-16 -1.054176 0.450 0.629 3.095942e-12
## DDN 1.784378e-14 1.401976 0.592 0.396 4.151176e-10
## SNCA 7.596526e-12 -1.022314 0.397 0.544 1.767256e-07
genes <- c('ATP2B1', 'CHGB')
SpatialFeaturePlot(ss.hippo, features = genes, pt.size.factor = 3,
stroke = NA, alpha = 0.5, max.cutoff = 'q95')
Running BANKSY with locations provided explicitly
One can also call RunBanksy on a Seurat object created from counts by
providing the location of cell centroids or spots explicitly. In this
case, the locations must be stored as metadata. Here, we use a mouse
hippocampus VeraFISH dataset provided with the Banksy package.
data(hippocampus)
head(hippocampus$expression[,1:5])
## cell_1276 cell_8890 cell_691 cell_396 cell_9818
## Sparcl1 45 0 11 22 0
## Slc1a2 17 0 6 5 0
## Map 10 0 12 16 0
## Sqstm1 26 0 0 2 0
## Atp1a2 0 0 4 3 0
## Tnc 0 0 0 0 0
head(hippocampus$locations)
## sdimx sdimy
## cell_1276 -13372.899 15776.37
## cell_8890 8941.101 15866.37
## cell_691 -14882.899 15896.37
## cell_396 -15492.899 15835.37
## cell_9818 11308.101 15846.37
## cell_11310 14894.101 15810.37
Construct the Seurat object by storing the locations of cell centroids
as metadata. We keep cells with total count between 5th and 98th
percentile:
# Create manually
vf.hippo <- CreateSeuratObject(counts = hippocampus$expression,
meta.data = hippocampus$locations)
vf.hippo <- subset(vf.hippo,
nCount_RNA > quantile(vf.hippo$nCount_RNA, 0.05) &
nCount_RNA < quantile(vf.hippo$nCount_RNA, 0.98))
Next, we normalize the data by library size and scale the data:
# Normalize
vf.hippo <- NormalizeData(vf.hippo, scale.factor = 100, normalization.method = 'RC')
vf.hippo <- ScaleData(vf.hippo)
Now, run BANKSY. Here, we provide the column names of the x and y
spatial coordinates as stored in the metadata to dimx and dimy
respectively:
# Run BANKSY
vf.hippo <- RunBanksy(vf.hippo, lambda = 0.2, dimx = 'sdimx', dimy = 'sdimy',
assay = 'RNA', slot = 'data', features = 'all', k_geom = 10)
Note that the RunBanksy function sets the default assay to BANKSY (
determined by the assay_name argument) and fills the scale.data
slot. Users should not call ScaleData on the BANKSY assay as this
negates the effects of lambda.
Run PCA on the BANKSY matrix:
# PCA
vf.hippo <- RunPCA(vf.hippo, assay = 'BANKSY', features = rownames(vf.hippo), npcs = 20)
Find BANKSY clusters:
# Cluster
vf.hippo <- FindNeighbors(vf.hippo, dims = 1:20)
vf.hippo <- FindClusters(vf.hippo, resolution = 0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10205
## Number of edges: 446178
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9099
## Number of communities: 15
## Elapsed time: 1 seconds
Visualise BANKSY clusters in spatial dimensions:
# Viz
FeatureScatter(vf.hippo, 'sdimx', 'sdimy', cols = mypal, pt.size = 0.75)
FeatureScatter(vf.hippo, 'sdimx', 'sdimy', cols = mypal, pt.size = 0.1) + facet_wrap(~ colors)
Find markers and visualise them. Here, we do so for a cluster defined by
a thin layer of cells expressing Gfap. We also write a simple function
genePlot that plots marker genes in spatial dimensions.
# Find markers
DefaultAssay(vf.hippo) <- 'RNA'
markers <- FindMarkers(vf.hippo, ident.1 = 6, only.pos = TRUE)
genePlot <- function(object, dimx, dimy, gene, assay = 'RNA',
slot = 'scale.data', q.low = 0.01, q.high = 0.99,
col.low='blue', col.high='red') {
val <- GetAssayData(object, assay=assay, slot=slot)[gene,]
val.low <- quantile(val, q.low)
val.high <- quantile(val, q.high)
val[val < val.low] <- val.low
val[val > val.high] <- val.high
pdf <- data.frame(x=object[[dimx]], y=object[[dimy]], gene=val)
colnames(pdf) <- c('sdimx','sdimy', 'gene')
ggplot(pdf, aes(x=sdimx,y=sdimy,color=gene)) + geom_point(size = 1) +
theme_minimal() + theme(legend.title = element_blank()) +
scale_color_gradient2(low = col.low, high = col.high) +
ggtitle(gene)
}
genePlot(vf.hippo, 'sdimx', 'sdimy', 'Gfap')
Multi-sample analysis
This section demonstrate demonstrates multi-sample analysis. Such an
approach is appropriate when analysing multiple spatial omics datasets
with non-contiguous spatial coordinates, and when large batch effects
are not present.
Here, we use a mouse hippocampus VeraFISH dataset provided with the
Banksy package.
data(hippocampus)
head(hippocampus$expression[,1:5])
## cell_1276 cell_8890 cell_691 cell_396 cell_9818
## Sparcl1 45 0 11 22 0
## Slc1a2 17 0 6 5 0
## Map 10 0 12 16 0
## Sqstm1 26 0 0 2 0
## Atp1a2 0 0 4 3 0
## Tnc 0 0 0 0 0
head(hippocampus$locations)
## sdimx sdimy
## cell_1276 -13372.899 15776.37
## cell_8890 8941.101 15866.37
## cell_691 -14882.899 15896.37
## cell_396 -15492.899 15835.37
## cell_9818 11308.101 15846.37
## cell_11310 14894.101 15810.37
For demonstration purposes, we create three separate datasets by
splitting the data.
# Number of groups
n_groups = 3
group_names = paste0('group', seq(n_groups))
group_size = 1000
starts = seq(1, by=group_size, length.out=n_groups)
ends = starts + group_size - 1
# List of Seurat objects
seu_list = lapply(seq(n_groups), function(i) {
idx = seq(starts[i], ends[i])
seu = CreateSeuratObject(
counts = hippocampus$expression[,idx],
meta.data = data.frame(scale(hippocampus$locations[idx,], scale = FALSE))
)
# Set original identity of cell
seu$orig.ident = group_names[i]
seu
})
seu_list
## [[1]]
## An object of class Seurat
## 120 features across 1000 samples within 1 assay
## Active assay: RNA (120 features, 0 variable features)
## 1 layer present: counts
##
## [[2]]
## An object of class Seurat
## 120 features across 1000 samples within 1 assay
## Active assay: RNA (120 features, 0 variable features)
## 1 layer present: counts
##
## [[3]]
## An object of class Seurat
## 120 features across 1000 samples within 1 assay
## Active assay: RNA (120 features, 0 variable features)
## 1 layer present: counts
Perform normalisation for each dataset.
seu_list = lapply(seu_list, NormalizeData,
scale.factor = 100, normalization.method = 'RC')
Merge the datasets. Note that the spatial coordinates overlap.
# Merge
seu = Reduce(merge, seu_list)
seu = JoinLayers(seu) # run this for Seurat v5 objects
# Plot spatial coordinates colored by group
plot(FetchData(seu, c('sdimx', 'sdimy')), col = factor(seu$orig.ident))
Now run BANKSY. For multi-sample analysis, the argument group must be
provided, which specifies the name of the metadata column that gives the
assignment of each cell or spot to its original Seurat object. Here, we
use orig.ident. Internally, providing the group argument tells the
function to compute neighborhood matrices based on locations staggered
by group, ensuring that cells from different spatial datasets do not
overlap. The staggered locations are stored in the metadata for sanity
checking. The split.scale argument allows for within-group scaling,
accounting for minor differences in datasets.
# Grouping variable
head(seu@meta.data)
## orig.ident nCount_RNA nFeature_RNA sdimx sdimy
## cell_1276 group1 266 51 -11933.19 1366.934
## cell_8890 group1 13 3 10380.81 1456.934
## cell_691 group1 132 36 -13443.19 1486.934
## cell_396 group1 95 27 -14053.19 1425.934
## cell_9818 group1 10 5 12747.81 1436.934
## cell_11310 group1 15 5 16333.81 1400.934
table(seu$orig.ident)
##
## group1 group2 group3
## 1000 1000 1000
# Run BANKSY
seu = RunBanksy(seu, lambda = 0.2, assay = 'RNA', slot = 'data',
dimx = 'sdimx', dimy = 'sdimy', features = 'all',
group = 'orig.ident', split.scale = TRUE, k_geom = 15)
# Staggered locations added to metadata
head(seu@meta.data)
## orig.ident nCount_RNA nFeature_RNA sdimx sdimy
## cell_1276 group1 266 51 -11933.19 1366.934
## cell_8890 group1 13 3 10380.81 1456.934
## cell_691 group1 132 36 -13443.19 1486.934
## cell_396 group1 95 27 -14053.19 1425.934
## cell_9818 group1 10 5 12747.81 1436.934
## cell_11310 group1 15 5 16333.81 1400.934
## staggered_sdimx staggered_sdimy
## cell_1276 3728.686 1366.934
## cell_8890 26042.686 1456.934
## cell_691 2218.686 1486.934
## cell_396 1608.686 1425.934
## cell_9818 28409.686 1436.934
## cell_11310 31995.686 1400.934
The rest of the workflow follows as before:
seu = RunPCA(seu, assay = 'BANKSY', features = rownames(seu), npcs = 30)
seu = RunUMAP(seu, dims = 1:30)
seu = FindNeighbors(seu, dims = 1:30)
seu = FindClusters(seu, resolution = 1)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3000
## Number of edges: 171757
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8094
## Number of communities: 12
## Elapsed time: 0 seconds
Visualise clusters:
mypal <- kelly()[-1]
DimPlot(seu, pt.size = 0.25, label = TRUE, label.size = 3, cols = mypal)
FeatureScatter(seu, 'staggered_sdimx', 'staggered_sdimy', pt.size = 0.75, cols = mypal)
Spatial data integration with Harmony
BANKSY can be used with Harmony for integrating multiple spatial omics
datasets in the presence of strong batch effects.
Download the data.
library(spatialLIBD)
library(ExperimentHub)
library(harmony)
ehub <- ExperimentHub::ExperimentHub()
spe <- spatialLIBD::fetch_data(type = "spe", eh = ehub)
imgData(spe) <- NULL
assay(spe, "logcounts") <- NULL
reducedDims(spe) <- NULL
rowData(spe) <- NULL
colData(spe) <- DataFrame(
sample_id = spe$sample_id,
clust_annotation = factor(
addNA(spe$layer_guess_reordered_short),
exclude = NULL, labels = seq(8)
),
in_tissue = spe$in_tissue,
row.names = colnames(spe)
)
invisible(gc())
# Subset to first sample of each subject
sample_names <- c("151507", "151669", "151673")
spe_list <- lapply(sample_names, function(x) spe[, spe$sample_id == x])
rm(spe)
invisible(gc())
Normalise the data and compute highly variable features.
# Convert to Seurat and Normalize data
seu_list <- lapply(spe_list, function(x) {
x <- as.Seurat(x, data = NULL)
NormalizeData(x, scale.factor = 3000, normalization.method = 'RC')
})
# Compute HVGs for each dataset and take the union
hvgs <- lapply(seu_list, function(x) {
VariableFeatures(FindVariableFeatures(x, nfeatures = 2000))
})
hvgs <- Reduce(union, hvgs)
# Subset to HVGs
seu_list <- lapply(seu_list, function(x) x[hvgs,])
seu <- Reduce(merge, seu_list)
locs <- do.call(rbind.data.frame, lapply(spe_list, spatialCoords))
seu@meta.data <- cbind(seu@meta.data, locs)
seu
Run BANKSY. When analysing multiple samples, the argument group must
be provided, which specifies the name of the metadata column that gives
the assignment of each cell or spot to its original Seurat object. Here,
we use sample_id. Internally, providing the group argument tells the
function to compute neighborhood matrices based on locations staggered
by group, ensuring that cells from different spatial datasets do not
overlap. The staggered locations are stored in the metadata for sanity
checking. Within-group scaling has little effect in the presence of
strong batch effects, hence, we set split.scale=FALSE for efficiency.
# Grouping variable
head(seu@meta.data)
table(seu$sample_id)
sdimx <- 'pxl_col_in_fullres'
sdimy <- 'pxl_row_in_fullres'
# Run BANKSY
seu <- RunBanksy(seu, lambda = 0.2, assay = 'originalexp', slot = 'data',
dimx = sdimx, dimy = sdimy, features = 'all',
group = 'sample_id', split.scale = FALSE, k_geom = 6)
Compute a spatially-aware embedding with PCA on the BANKSY matrix, and
run Harmony on this embedding.
seu <- RunPCA(seu, assay = 'BANKSY', features = rownames(seu), npcs = 10)
seu <- RunHarmony(seu, group.by.vars='sample_id')
The rest of the workflow follows as before:
seu <- RunUMAP(seu, dims = 1:10, reduction = 'harmony')
seu <- FindNeighbors(seu, dims = 1:10, reduction = 'harmony')
seu <- FindClusters(seu, resolution = 0.4)
Visualise clusters:
DimPlot(seu, pt.size = 0.25, label = TRUE, label.size = 3, cols = mypal)
FeatureScatter(seu, 'staggered_sdimx', 'staggered_sdimy', cols = mypal, pt.size = 0.75)
Getting help
For more information, visit https://github.com/prabhakarlab/Banksy.
Vignette runtime
## Time difference of 1.434785 mins
Session info
sessionInfo()
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.2.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/London
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] pals_1.8 gridExtra_2.3 ggplot2_3.4.4
## [4] SeuratWrappers_0.3.4 ssHippo.SeuratData_3.1.4 SeuratData_0.2.2.9001
## [7] Seurat_5.0.1 SeuratObject_5.0.1 sp_2.1-3
## [10] Banksy_0.99.9
##
## loaded via a namespace (and not attached):
## [1] RcppHungarian_0.3 RcppAnnoy_0.0.22
## [3] splines_4.3.2 later_1.3.2
## [5] bitops_1.0-7 tibble_3.2.1
## [7] R.oo_1.26.0 polyclip_1.10-6
## [9] fastDummies_1.7.3 lifecycle_1.0.4
## [11] aricode_1.0.3 globals_0.16.2
## [13] lattice_0.22-5 MASS_7.3-60.0.1
## [15] magrittr_2.0.3 limma_3.58.1
## [17] plotly_4.10.4 rmarkdown_2.25
## [19] yaml_2.3.8 remotes_2.4.2.1
## [21] httpuv_1.6.14 sctransform_0.4.1
## [23] spam_2.10-0 spatstat.sparse_3.0-3
## [25] reticulate_1.35.0 mapproj_1.2.11
## [27] cowplot_1.1.3 pbapply_1.7-2
## [29] RColorBrewer_1.1-3 maps_3.4.2
## [31] abind_1.4-5 zlibbioc_1.48.0
## [33] Rtsne_0.17 GenomicRanges_1.54.1
## [35] purrr_1.0.2 R.utils_2.12.3
## [37] BiocGenerics_0.48.1 RCurl_1.98-1.14
## [39] rappdirs_0.3.3 GenomeInfoDbData_1.2.11
## [41] IRanges_2.36.0 S4Vectors_0.40.2
## [43] ggrepel_0.9.5 irlba_2.3.5.1
## [45] listenv_0.9.1 spatstat.utils_3.0-4
## [47] goftest_1.2-3 RSpectra_0.16-1
## [49] spatstat.random_3.2-2 fitdistrplus_1.1-11
## [51] parallelly_1.37.0 leiden_0.4.3.1
## [53] codetools_0.2-19 DelayedArray_0.28.0
## [55] tidyselect_1.2.0 farver_2.1.1
## [57] matrixStats_1.2.0 stats4_4.3.2
## [59] spatstat.explore_3.2-6 jsonlite_1.8.8
## [61] ellipsis_0.3.2 progressr_0.14.0
## [63] ggridges_0.5.6 survival_3.5-7
## [65] dbscan_1.1-12 tools_4.3.2
## [67] ica_1.0-3 Rcpp_1.0.12
## [69] glue_1.7.0 SparseArray_1.2.4
## [71] xfun_0.42 MatrixGenerics_1.14.0
## [73] GenomeInfoDb_1.38.6 dplyr_1.1.4
## [75] withr_3.0.0 BiocManager_1.30.22
## [77] fastmap_1.1.1 fansi_1.0.6
## [79] digest_0.6.34 rsvd_1.0.5
## [81] R6_2.5.1 mime_0.12
## [83] colorspace_2.1-0 scattermore_1.2
## [85] sccore_1.0.4 tensor_1.5
## [87] dichromat_2.0-0.1 spatstat.data_3.0-4
## [89] R.methodsS3_1.8.2 utf8_1.2.4
## [91] tidyr_1.3.1 generics_0.1.3
## [93] data.table_1.15.0 httr_1.4.7
## [95] htmlwidgets_1.6.4 S4Arrays_1.2.0
## [97] uwot_0.1.16 pkgconfig_2.0.3
## [99] gtable_0.3.4 lmtest_0.9-40
## [101] SingleCellExperiment_1.24.0 XVector_0.42.0
## [103] htmltools_0.5.7 dotCall64_1.1-1
## [105] scales_1.3.0 Biobase_2.62.0
## [107] png_0.1-8 SpatialExperiment_1.12.0
## [109] knitr_1.45 rstudioapi_0.15.0
## [111] reshape2_1.4.4 rjson_0.2.21
## [113] nlme_3.1-164 zoo_1.8-12
## [115] stringr_1.5.1 KernSmooth_2.23-22
## [117] parallel_4.3.2 miniUI_0.1.1.1
## [119] pillar_1.9.0 grid_4.3.2
## [121] vctrs_0.6.5 RANN_2.6.1
## [123] promises_1.2.1 xtable_1.8-4
## [125] cluster_2.1.6 evaluate_0.23
## [127] magick_2.8.2 cli_3.6.2
## [129] compiler_4.3.2 rlang_1.1.3
## [131] crayon_1.5.2 future.apply_1.11.1
## [133] labeling_0.4.3 mclust_6.0.1
## [135] plyr_1.8.9 stringi_1.8.3
## [137] viridisLite_0.4.2 deldir_2.0-2
## [139] munsell_0.5.0 lazyeval_0.2.2
## [141] spatstat.geom_3.2-8 Matrix_1.6-5
## [143] RcppHNSW_0.6.0 patchwork_1.2.0
## [145] future_1.33.1 statmod_1.5.0
## [147] shiny_1.8.0 highr_0.10
## [149] SummarizedExperiment_1.32.0 ROCR_1.0-11
## [151] leidenAlg_1.1.2 igraph_2.0.1.1
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Running BANKSY with Seurat
Compiled: April 04, 2024
- Introduction
- Overview
- Running BANKSY within Seurat’s spatial framework
- Running BANKSY with locations provided explicitly
- Multi-sample analysis
- Spatial data integration with Harmony
- Getting help
Introduction
In this vignette, we describe how to run BANKSY with Seurat objects. If you use BANKSY in your research, please cite
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Vipul Singhal, Nigel Chou, Joseph Lee, Yifei Yue, Jinyue Liu, Wan Kee Chock, Li Lin, Yun-Ching Chang, Erica Mei Ling Teo, Jonathan Aow, Hwee Kuan Lee, Kok Hao Chen & Shyam Prabhakar
Nature Genetics, 2024
BANKSY is a method that incorporates neighborhood information for clustering spatial omics data. By doing so, BANKSY is able to
- improve cell-type assignment in noisy data
- distinguish subtly different cell-types stratified by microenvironment
- identify spatial domains sharing the same microenvironment
The amount of neighborhood information incorporated is controlled by a
parameter lambda in [0,1], with higher values giving more weight to
the neighbourhood information during clustering.
Overview
The RunBanksy function implemented with the SeuratWrappers package
allows users to run BANKSY with Seurat objects. We describe two options
of running RunBanksy. The first is within Seurat’s spatial framework
(see here
and
here)
and requires a Seurat object and a lambda parameter as mandatory input.
The second option works with Seurat objects that do not have spatial
information stored within, and therefore requires an additional argument
giving the locations of the cell centroids or spots.
Caveat: ScaleData should not be run after a call to RunBanksy;
RunBanksy populates the scale.data slot with the scaled BANKSY
matrix. Calling ScaleData after RunBanksy performs gene-wise
z-scaling, negating the effect of lambda.
Prerequisites to install:
library(Banksy)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
library(ggplot2)
library(gridExtra)
library(pals)
# Kelly palette for visualization
mypal <- kelly()[-1]
Running BANKSY within Seurat’s spatial framework
We demonstrate how to run BANKSY within Seurat’s spatial analysis framework with a mouse hippocampus Slide-seq v2 dataset from the SeuratData package.
After installing SeuratData, the data can be accessed as follows:
InstallData('ssHippo')
ss.hippo <- LoadData("ssHippo")
We perform simple preprocessing by filtering beads with high mito percentage and keeping only beads within the 5th and 98th percentile of total UMI counts. To keep runtime of this vignette short, we downsample the data to 10,000 beads.
# Filtering
ss.hippo[['percent.mt']] <- PercentageFeatureSet(ss.hippo, pattern = '^MT-')
ss.hippo <- subset(ss.hippo, percent.mt < 10 &
nCount_Spatial > quantile(ss.hippo$nCount_Spatial, 0.05) &
nCount_Spatial < quantile(ss.hippo$nCount_Spatial, 0.98))
# Downsample
set.seed(42)
ss.hippo <- ss.hippo[,sample(colnames(ss.hippo), 1e4)]
Next, normalize the data and find variable genes:
# Normalize
ss.hippo <- NormalizeData(ss.hippo)
ss.hippo <- FindVariableFeatures(ss.hippo)
ss.hippo <- ScaleData(ss.hippo)
To run BANKSY, we specify the following:
lambda: a numeric value in [0,1]. With low values of lambda, BANKSY operates in cell-typing mode, while high values of lambda find spatial domains.assayandslot: determines where to pull the expression data fromfeatures: specifies features for downstream analysis. This can be'all','variable'or a subset of features.k_geom: the number of neighbors that defines a cell’s neighborhood
Call ?RunBanksy for more details on function parameters.
# Run BANKSY
ss.hippo <- RunBanksy(ss.hippo, lambda = 0.2, verbose=TRUE,
assay = 'Spatial', slot = 'data', features = 'variable',
k_geom = 15)
ss.hippo
## An object of class Seurat
## 27264 features across 10000 samples within 2 assays
## Active assay: BANKSY (4000 features, 0 variable features)
## 2 layers present: data, scale.data
## 1 other assay present: Spatial
## 1 image present: image
Note that the RunBanksy function sets the default assay to BANKSY (
determined by the assay_name argument) and fills the scale.data
slot. Users should not call ScaleData on the BANKSY assay as this
negates the effects of lambda.
The rest of the pipeline is similar to the ‘default’ Seurat pipline. We scale the data and run dimensionality reduction with PCA and UMAP:
# Run PCA and UMAP
ss.hippo <- RunPCA(ss.hippo, assay = 'BANKSY', features = rownames(ss.hippo), npcs = 30)
ss.hippo <- RunUMAP(ss.hippo, dims = 1:30)
Next, find BANKSY clusters:
# Clustering
ss.hippo <- FindNeighbors(ss.hippo, dims = 1:30)
ss.hippo <- FindClusters(ss.hippo, resolution = 0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10000
## Number of edges: 365658
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9033
## Number of communities: 13
## Elapsed time: 1 seconds
Visualize the UMAP and Spatial plot:
# Viz
grid.arrange(
DimPlot(ss.hippo, pt.size = 0.25, label = TRUE, label.size = 3, repel = TRUE),
SpatialDimPlot(ss.hippo, stroke = NA, label = TRUE, label.size = 3,
repel = TRUE, alpha = 0.5, pt.size.factor = 2),
ncol = 2
)
Find markers based on the BANKSY clusters and visualize them. Here, we find differentially expressed genes between the CA1 and CA3 regions.
# Find markers
DefaultAssay(ss.hippo) <- 'Spatial'
markers <- FindMarkers(ss.hippo, ident.1 = 4, ident.2 = 9, only.pos = F,
logfc.threshold = 1, min.pct = 0.5)
markers <- markers[markers$p_val_adj < 0.01,]
markers
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## SNAP25 1.127235e-46 -1.260312 0.658 0.823 2.622400e-42
## CHGB 9.840001e-44 -1.985343 0.439 0.697 2.289178e-39
## STMN2 1.281230e-24 -1.430138 0.335 0.574 2.980653e-20
## SYN2 3.272800e-23 -1.609355 0.332 0.564 7.613842e-19
## ATP2B1 1.545647e-22 1.251540 0.639 0.474 3.595793e-18
## CPLX2 4.619232e-21 -1.220110 0.289 0.522 1.074618e-16
## PRKCB 1.276453e-18 1.394809 0.552 0.341 2.969539e-14
## PCP4 2.006224e-18 -1.269671 0.379 0.578 4.667279e-14
## TUBB2A 1.330787e-16 -1.054176 0.450 0.629 3.095942e-12
## DDN 1.784378e-14 1.401976 0.592 0.396 4.151176e-10
## SNCA 7.596526e-12 -1.022314 0.397 0.544 1.767256e-07
genes <- c('ATP2B1', 'CHGB')
SpatialFeaturePlot(ss.hippo, features = genes, pt.size.factor = 3,
stroke = NA, alpha = 0.5, max.cutoff = 'q95')
Running BANKSY with locations provided explicitly
One can also call RunBanksy on a Seurat object created from counts by
providing the location of cell centroids or spots explicitly. In this
case, the locations must be stored as metadata. Here, we use a mouse
hippocampus VeraFISH dataset provided with the Banksy package.
data(hippocampus)
head(hippocampus$expression[,1:5])
## cell_1276 cell_8890 cell_691 cell_396 cell_9818
## Sparcl1 45 0 11 22 0
## Slc1a2 17 0 6 5 0
## Map 10 0 12 16 0
## Sqstm1 26 0 0 2 0
## Atp1a2 0 0 4 3 0
## Tnc 0 0 0 0 0
head(hippocampus$locations)
## sdimx sdimy
## cell_1276 -13372.899 15776.37
## cell_8890 8941.101 15866.37
## cell_691 -14882.899 15896.37
## cell_396 -15492.899 15835.37
## cell_9818 11308.101 15846.37
## cell_11310 14894.101 15810.37
Construct the Seurat object by storing the locations of cell centroids as metadata. We keep cells with total count between 5th and 98th percentile:
# Create manually
vf.hippo <- CreateSeuratObject(counts = hippocampus$expression,
meta.data = hippocampus$locations)
vf.hippo <- subset(vf.hippo,
nCount_RNA > quantile(vf.hippo$nCount_RNA, 0.05) &
nCount_RNA < quantile(vf.hippo$nCount_RNA, 0.98))
Next, we normalize the data by library size and scale the data:
# Normalize
vf.hippo <- NormalizeData(vf.hippo, scale.factor = 100, normalization.method = 'RC')
vf.hippo <- ScaleData(vf.hippo)
Now, run BANKSY. Here, we provide the column names of the x and y
spatial coordinates as stored in the metadata to dimx and dimy
respectively:
# Run BANKSY
vf.hippo <- RunBanksy(vf.hippo, lambda = 0.2, dimx = 'sdimx', dimy = 'sdimy',
assay = 'RNA', slot = 'data', features = 'all', k_geom = 10)
Note that the RunBanksy function sets the default assay to BANKSY (
determined by the assay_name argument) and fills the scale.data
slot. Users should not call ScaleData on the BANKSY assay as this
negates the effects of lambda.
Run PCA on the BANKSY matrix:
# PCA
vf.hippo <- RunPCA(vf.hippo, assay = 'BANKSY', features = rownames(vf.hippo), npcs = 20)
Find BANKSY clusters:
# Cluster
vf.hippo <- FindNeighbors(vf.hippo, dims = 1:20)
vf.hippo <- FindClusters(vf.hippo, resolution = 0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10205
## Number of edges: 446178
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9099
## Number of communities: 15
## Elapsed time: 1 seconds
Visualise BANKSY clusters in spatial dimensions:
# Viz
FeatureScatter(vf.hippo, 'sdimx', 'sdimy', cols = mypal, pt.size = 0.75)
FeatureScatter(vf.hippo, 'sdimx', 'sdimy', cols = mypal, pt.size = 0.1) + facet_wrap(~ colors)
Find markers and visualise them. Here, we do so for a cluster defined by
a thin layer of cells expressing Gfap. We also write a simple function
genePlot that plots marker genes in spatial dimensions.
# Find markers
DefaultAssay(vf.hippo) <- 'RNA'
markers <- FindMarkers(vf.hippo, ident.1 = 6, only.pos = TRUE)
genePlot <- function(object, dimx, dimy, gene, assay = 'RNA',
slot = 'scale.data', q.low = 0.01, q.high = 0.99,
col.low='blue', col.high='red') {
val <- GetAssayData(object, assay=assay, slot=slot)[gene,]
val.low <- quantile(val, q.low)
val.high <- quantile(val, q.high)
val[val < val.low] <- val.low
val[val > val.high] <- val.high
pdf <- data.frame(x=object[[dimx]], y=object[[dimy]], gene=val)
colnames(pdf) <- c('sdimx','sdimy', 'gene')
ggplot(pdf, aes(x=sdimx,y=sdimy,color=gene)) + geom_point(size = 1) +
theme_minimal() + theme(legend.title = element_blank()) +
scale_color_gradient2(low = col.low, high = col.high) +
ggtitle(gene)
}
genePlot(vf.hippo, 'sdimx', 'sdimy', 'Gfap')
Multi-sample analysis
This section demonstrate demonstrates multi-sample analysis. Such an approach is appropriate when analysing multiple spatial omics datasets with non-contiguous spatial coordinates, and when large batch effects are not present.
Here, we use a mouse hippocampus VeraFISH dataset provided with the Banksy package.
data(hippocampus)
head(hippocampus$expression[,1:5])
## cell_1276 cell_8890 cell_691 cell_396 cell_9818
## Sparcl1 45 0 11 22 0
## Slc1a2 17 0 6 5 0
## Map 10 0 12 16 0
## Sqstm1 26 0 0 2 0
## Atp1a2 0 0 4 3 0
## Tnc 0 0 0 0 0
head(hippocampus$locations)
## sdimx sdimy
## cell_1276 -13372.899 15776.37
## cell_8890 8941.101 15866.37
## cell_691 -14882.899 15896.37
## cell_396 -15492.899 15835.37
## cell_9818 11308.101 15846.37
## cell_11310 14894.101 15810.37
For demonstration purposes, we create three separate datasets by splitting the data.
# Number of groups
n_groups = 3
group_names = paste0('group', seq(n_groups))
group_size = 1000
starts = seq(1, by=group_size, length.out=n_groups)
ends = starts + group_size - 1
# List of Seurat objects
seu_list = lapply(seq(n_groups), function(i) {
idx = seq(starts[i], ends[i])
seu = CreateSeuratObject(
counts = hippocampus$expression[,idx],
meta.data = data.frame(scale(hippocampus$locations[idx,], scale = FALSE))
)
# Set original identity of cell
seu$orig.ident = group_names[i]
seu
})
seu_list
## [[1]]
## An object of class Seurat
## 120 features across 1000 samples within 1 assay
## Active assay: RNA (120 features, 0 variable features)
## 1 layer present: counts
##
## [[2]]
## An object of class Seurat
## 120 features across 1000 samples within 1 assay
## Active assay: RNA (120 features, 0 variable features)
## 1 layer present: counts
##
## [[3]]
## An object of class Seurat
## 120 features across 1000 samples within 1 assay
## Active assay: RNA (120 features, 0 variable features)
## 1 layer present: counts
Perform normalisation for each dataset.
seu_list = lapply(seu_list, NormalizeData,
scale.factor = 100, normalization.method = 'RC')
Merge the datasets. Note that the spatial coordinates overlap.
# Merge
seu = Reduce(merge, seu_list)
seu = JoinLayers(seu) # run this for Seurat v5 objects
# Plot spatial coordinates colored by group
plot(FetchData(seu, c('sdimx', 'sdimy')), col = factor(seu$orig.ident))
Now run BANKSY. For multi-sample analysis, the argument group must be
provided, which specifies the name of the metadata column that gives the
assignment of each cell or spot to its original Seurat object. Here, we
use orig.ident. Internally, providing the group argument tells the
function to compute neighborhood matrices based on locations staggered
by group, ensuring that cells from different spatial datasets do not
overlap. The staggered locations are stored in the metadata for sanity
checking. The split.scale argument allows for within-group scaling,
accounting for minor differences in datasets.
# Grouping variable
head(seu@meta.data)
## orig.ident nCount_RNA nFeature_RNA sdimx sdimy
## cell_1276 group1 266 51 -11933.19 1366.934
## cell_8890 group1 13 3 10380.81 1456.934
## cell_691 group1 132 36 -13443.19 1486.934
## cell_396 group1 95 27 -14053.19 1425.934
## cell_9818 group1 10 5 12747.81 1436.934
## cell_11310 group1 15 5 16333.81 1400.934
table(seu$orig.ident)
##
## group1 group2 group3
## 1000 1000 1000
# Run BANKSY
seu = RunBanksy(seu, lambda = 0.2, assay = 'RNA', slot = 'data',
dimx = 'sdimx', dimy = 'sdimy', features = 'all',
group = 'orig.ident', split.scale = TRUE, k_geom = 15)
# Staggered locations added to metadata
head(seu@meta.data)
## orig.ident nCount_RNA nFeature_RNA sdimx sdimy
## cell_1276 group1 266 51 -11933.19 1366.934
## cell_8890 group1 13 3 10380.81 1456.934
## cell_691 group1 132 36 -13443.19 1486.934
## cell_396 group1 95 27 -14053.19 1425.934
## cell_9818 group1 10 5 12747.81 1436.934
## cell_11310 group1 15 5 16333.81 1400.934
## staggered_sdimx staggered_sdimy
## cell_1276 3728.686 1366.934
## cell_8890 26042.686 1456.934
## cell_691 2218.686 1486.934
## cell_396 1608.686 1425.934
## cell_9818 28409.686 1436.934
## cell_11310 31995.686 1400.934
The rest of the workflow follows as before:
seu = RunPCA(seu, assay = 'BANKSY', features = rownames(seu), npcs = 30)
seu = RunUMAP(seu, dims = 1:30)
seu = FindNeighbors(seu, dims = 1:30)
seu = FindClusters(seu, resolution = 1)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3000
## Number of edges: 171757
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8094
## Number of communities: 12
## Elapsed time: 0 seconds
Visualise clusters:
mypal <- kelly()[-1]
DimPlot(seu, pt.size = 0.25, label = TRUE, label.size = 3, cols = mypal)
FeatureScatter(seu, 'staggered_sdimx', 'staggered_sdimy', pt.size = 0.75, cols = mypal)
Spatial data integration with Harmony
BANKSY can be used with Harmony for integrating multiple spatial omics datasets in the presence of strong batch effects.
Download the data.
library(spatialLIBD)
library(ExperimentHub)
library(harmony)
ehub <- ExperimentHub::ExperimentHub()
spe <- spatialLIBD::fetch_data(type = "spe", eh = ehub)
imgData(spe) <- NULL
assay(spe, "logcounts") <- NULL
reducedDims(spe) <- NULL
rowData(spe) <- NULL
colData(spe) <- DataFrame(
sample_id = spe$sample_id,
clust_annotation = factor(
addNA(spe$layer_guess_reordered_short),
exclude = NULL, labels = seq(8)
),
in_tissue = spe$in_tissue,
row.names = colnames(spe)
)
invisible(gc())
# Subset to first sample of each subject
sample_names <- c("151507", "151669", "151673")
spe_list <- lapply(sample_names, function(x) spe[, spe$sample_id == x])
rm(spe)
invisible(gc())
Normalise the data and compute highly variable features.
# Convert to Seurat and Normalize data
seu_list <- lapply(spe_list, function(x) {
x <- as.Seurat(x, data = NULL)
NormalizeData(x, scale.factor = 3000, normalization.method = 'RC')
})
# Compute HVGs for each dataset and take the union
hvgs <- lapply(seu_list, function(x) {
VariableFeatures(FindVariableFeatures(x, nfeatures = 2000))
})
hvgs <- Reduce(union, hvgs)
# Subset to HVGs
seu_list <- lapply(seu_list, function(x) x[hvgs,])
seu <- Reduce(merge, seu_list)
locs <- do.call(rbind.data.frame, lapply(spe_list, spatialCoords))
seu@meta.data <- cbind(seu@meta.data, locs)
seu
Run BANKSY. When analysing multiple samples, the argument group must
be provided, which specifies the name of the metadata column that gives
the assignment of each cell or spot to its original Seurat object. Here,
we use sample_id. Internally, providing the group argument tells the
function to compute neighborhood matrices based on locations staggered
by group, ensuring that cells from different spatial datasets do not
overlap. The staggered locations are stored in the metadata for sanity
checking. Within-group scaling has little effect in the presence of
strong batch effects, hence, we set split.scale=FALSE for efficiency.
# Grouping variable
head(seu@meta.data)
table(seu$sample_id)
sdimx <- 'pxl_col_in_fullres'
sdimy <- 'pxl_row_in_fullres'
# Run BANKSY
seu <- RunBanksy(seu, lambda = 0.2, assay = 'originalexp', slot = 'data',
dimx = sdimx, dimy = sdimy, features = 'all',
group = 'sample_id', split.scale = FALSE, k_geom = 6)
Compute a spatially-aware embedding with PCA on the BANKSY matrix, and run Harmony on this embedding.
seu <- RunPCA(seu, assay = 'BANKSY', features = rownames(seu), npcs = 10)
seu <- RunHarmony(seu, group.by.vars='sample_id')
The rest of the workflow follows as before:
seu <- RunUMAP(seu, dims = 1:10, reduction = 'harmony')
seu <- FindNeighbors(seu, dims = 1:10, reduction = 'harmony')
seu <- FindClusters(seu, resolution = 0.4)
Visualise clusters:
DimPlot(seu, pt.size = 0.25, label = TRUE, label.size = 3, cols = mypal)
FeatureScatter(seu, 'staggered_sdimx', 'staggered_sdimy', cols = mypal, pt.size = 0.75)
Getting help
For more information, visit https://github.com/prabhakarlab/Banksy.
Vignette runtime## Time difference of 1.434785 mins
sessionInfo()
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.2.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/London
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] pals_1.8 gridExtra_2.3 ggplot2_3.4.4
## [4] SeuratWrappers_0.3.4 ssHippo.SeuratData_3.1.4 SeuratData_0.2.2.9001
## [7] Seurat_5.0.1 SeuratObject_5.0.1 sp_2.1-3
## [10] Banksy_0.99.9
##
## loaded via a namespace (and not attached):
## [1] RcppHungarian_0.3 RcppAnnoy_0.0.22
## [3] splines_4.3.2 later_1.3.2
## [5] bitops_1.0-7 tibble_3.2.1
## [7] R.oo_1.26.0 polyclip_1.10-6
## [9] fastDummies_1.7.3 lifecycle_1.0.4
## [11] aricode_1.0.3 globals_0.16.2
## [13] lattice_0.22-5 MASS_7.3-60.0.1
## [15] magrittr_2.0.3 limma_3.58.1
## [17] plotly_4.10.4 rmarkdown_2.25
## [19] yaml_2.3.8 remotes_2.4.2.1
## [21] httpuv_1.6.14 sctransform_0.4.1
## [23] spam_2.10-0 spatstat.sparse_3.0-3
## [25] reticulate_1.35.0 mapproj_1.2.11
## [27] cowplot_1.1.3 pbapply_1.7-2
## [29] RColorBrewer_1.1-3 maps_3.4.2
## [31] abind_1.4-5 zlibbioc_1.48.0
## [33] Rtsne_0.17 GenomicRanges_1.54.1
## [35] purrr_1.0.2 R.utils_2.12.3
## [37] BiocGenerics_0.48.1 RCurl_1.98-1.14
## [39] rappdirs_0.3.3 GenomeInfoDbData_1.2.11
## [41] IRanges_2.36.0 S4Vectors_0.40.2
## [43] ggrepel_0.9.5 irlba_2.3.5.1
## [45] listenv_0.9.1 spatstat.utils_3.0-4
## [47] goftest_1.2-3 RSpectra_0.16-1
## [49] spatstat.random_3.2-2 fitdistrplus_1.1-11
## [51] parallelly_1.37.0 leiden_0.4.3.1
## [53] codetools_0.2-19 DelayedArray_0.28.0
## [55] tidyselect_1.2.0 farver_2.1.1
## [57] matrixStats_1.2.0 stats4_4.3.2
## [59] spatstat.explore_3.2-6 jsonlite_1.8.8
## [61] ellipsis_0.3.2 progressr_0.14.0
## [63] ggridges_0.5.6 survival_3.5-7
## [65] dbscan_1.1-12 tools_4.3.2
## [67] ica_1.0-3 Rcpp_1.0.12
## [69] glue_1.7.0 SparseArray_1.2.4
## [71] xfun_0.42 MatrixGenerics_1.14.0
## [73] GenomeInfoDb_1.38.6 dplyr_1.1.4
## [75] withr_3.0.0 BiocManager_1.30.22
## [77] fastmap_1.1.1 fansi_1.0.6
## [79] digest_0.6.34 rsvd_1.0.5
## [81] R6_2.5.1 mime_0.12
## [83] colorspace_2.1-0 scattermore_1.2
## [85] sccore_1.0.4 tensor_1.5
## [87] dichromat_2.0-0.1 spatstat.data_3.0-4
## [89] R.methodsS3_1.8.2 utf8_1.2.4
## [91] tidyr_1.3.1 generics_0.1.3
## [93] data.table_1.15.0 httr_1.4.7
## [95] htmlwidgets_1.6.4 S4Arrays_1.2.0
## [97] uwot_0.1.16 pkgconfig_2.0.3
## [99] gtable_0.3.4 lmtest_0.9-40
## [101] SingleCellExperiment_1.24.0 XVector_0.42.0
## [103] htmltools_0.5.7 dotCall64_1.1-1
## [105] scales_1.3.0 Biobase_2.62.0
## [107] png_0.1-8 SpatialExperiment_1.12.0
## [109] knitr_1.45 rstudioapi_0.15.0
## [111] reshape2_1.4.4 rjson_0.2.21
## [113] nlme_3.1-164 zoo_1.8-12
## [115] stringr_1.5.1 KernSmooth_2.23-22
## [117] parallel_4.3.2 miniUI_0.1.1.1
## [119] pillar_1.9.0 grid_4.3.2
## [121] vctrs_0.6.5 RANN_2.6.1
## [123] promises_1.2.1 xtable_1.8-4
## [125] cluster_2.1.6 evaluate_0.23
## [127] magick_2.8.2 cli_3.6.2
## [129] compiler_4.3.2 rlang_1.1.3
## [131] crayon_1.5.2 future.apply_1.11.1
## [133] labeling_0.4.3 mclust_6.0.1
## [135] plyr_1.8.9 stringi_1.8.3
## [137] viridisLite_0.4.2 deldir_2.0-2
## [139] munsell_0.5.0 lazyeval_0.2.2
## [141] spatstat.geom_3.2-8 Matrix_1.6-5
## [143] RcppHNSW_0.6.0 patchwork_1.2.0
## [145] future_1.33.1 statmod_1.5.0
## [147] shiny_1.8.0 highr_0.10
## [149] SummarizedExperiment_1.32.0 ROCR_1.0-11
## [151] leidenAlg_1.1.2 igraph_2.0.1.1
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