Dataset explanation
10X genomics recently launched a new platform to obtain spatial expression data using a Visium Spatial Gene Expression slide.
The Visium brain data to run this tutorial can be found here
Visium technology:
High resolution png from original tissue:
![]images/general_figs/mouse_brain_highres.png)
Set up Giotto Environment
# Ensure Giotto Suite is installed.
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
# Ensure the Python environment for Giotto has been installed.
genv_exists = Giotto::checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment.
Giotto::installGiottoEnvironment()
}1. Create Giotto Visium Object and visualize
library(Giotto)
# 1. set working directory
results_folder = 'path/to/result'
# Optional: Specify a path to a Python executable within a conda or miniconda
# environment. If set to NULL (default), the Python executable within the previously
# installed Giotto environment will be used.
my_python_path = NULL # alternatively, "/local/python/path/python" if desired.
# 3. Create Giotto Instructions
instrs = createGiottoInstructions(save_dir = results_folder,
save_plot = TRUE,
show_plot = FALSE,
python_path = my_python_path)
## provide path to visium folder
data_path = '/path/to/Brain_data/'
## directly from visium folder
visium_brain = createGiottoVisiumObject(visium_dir = data_path,
expr_data = 'raw',
png_name = 'tissue_lowres_image.png',
gene_column_index = 2,
instructions = instrs)
## show associated images with giotto object
showGiottoImageNames(visium_brain) # "image" is the default name
## check metadata
pDataDT(visium_brain)
## show plot
spatPlot2D(gobject = visium_brain,
cell_color = 'in_tissue',
point_size = 2,
cell_color_code = c('0' = 'lightgrey', '1' = 'blue'),
show_image = T,
image_name = 'image')
2. Process Giotto Visium Object
## subset on spots that were covered by tissue
metadata = pDataDT(visium_brain)
in_tissue_barcodes = metadata[in_tissue == 1]$cell_ID
visium_brain = subsetGiotto(visium_brain,
cell_ids = in_tissue_barcodes)
## filter
visium_brain <- filterGiotto(gobject = visium_brain,
expression_threshold = 1,
feat_det_in_min_cells = 50,
min_det_feats_per_cell = 1000,
expression_values = c('raw'),
verbose = T)
## normalize
visium_brain <- normalizeGiotto(gobject = visium_brain,
scalefactor = 6000,
verbose = T)
## add gene & cell statistics
visium_brain <- addStatistics(gobject = visium_brain)
## visualize
spatPlot2D(gobject = visium_brain,
show_image = T,
point_alpha = 0.7,
cell_color = 'nr_feats',
color_as_factor = F)
3. Dimension Reduction
## highly variable features / genes (HVF)
visium_brain <- calculateHVF(gobject = visium_brain,
save_plot = TRUE)
## run PCA on expression values (default)
gene_metadata = fDataDT(visium_brain)
featgenes = gene_metadata[hvf == 'yes' & perc_cells > 3 & mean_expr_det > 0.4]$feat_ID
## run PCA on expression values (default)
visium_brain <- runPCA(gobject = visium_brain,
feats_to_use = featgenes)
screePlot(visium_brain, ncp = 30)
dimPlot2D(gobject = visium_brain,
dim_reduction_to_use = "pca")
## run UMAP and tSNE on PCA space (default)
visium_brain <- runUMAP(visium_brain,
dimensions_to_use = 1:10)
plotUMAP(gobject = visium_brain)
4. Clustering
## sNN network (default)
visium_brain <- createNearestNetwork(gobject = visium_brain,
dimensions_to_use = 1:10,
k = 15)
## Leiden clustering
visium_brain <- doLeidenCluster(gobject = visium_brain,
resolution = 0.4,
n_iterations = 1000)
plotUMAP(gobject = visium_brain,
cell_color = 'leiden_clus',
show_NN_network = T,
point_size = 2.5)
# spatial and dimension plots
spatDimPlot(gobject = visium_brain,
cell_color = 'leiden_clus',
dim_point_size = 2,
spat_point_size = 2.5)
spatDimPlot(gobject = visium_brain,
cell_color = 'nr_feats',
color_as_factor = F,
dim_point_size = 2,
spat_point_size = 2.5)
# dimension plots grouped by cluster
spatPlot2D(visium_brain,
cell_color = 'leiden_clus',
coord_fix_ratio = 1)
Plot with group by:
spatPlot2D(visium_brain,
cell_color = 'leiden_clus',
group_by = 'leiden_clus',
coord_fix_ratio = 1,
cow_n_col = 6,
show_legend = F,
save_param = list(base_width = 14, base_height = 14))
Highlight one or more groups:
spatPlot2D(visium_brain,
cell_color = 'leiden_clus',
select_cell_groups = '8',
coord_fix_ratio = 1,
show_other_cells = TRUE,
cell_color_code = c('8' = 'red'),
other_cell_color = "grey",
other_point_size = 1.5,
save_param = list(base_width = 7, base_height = 7))
5. Subset data
# create and show subset
DG_subset = subsetGiottoLocs(visium_brain,
x_max = 6500, x_min = 3000,
y_max = -2500, y_min = -5500,
return_gobject = TRUE)
spatDimPlot(gobject = DG_subset,
cell_color = 'leiden_clus',
spat_point_size = 5)
6. marker gene detection for clusters
## ------------------ ##
## Gini markers
gini_markers_subclusters = findMarkers_one_vs_all(gobject = visium_brain,
method = 'gini',
expression_values = 'normalized',
cluster_column = 'leiden_clus',
min_feats = 20,
min_expr_gini_score = 0.5,
min_det_gini_score = 0.5)
topgenes_gini = gini_markers_subclusters[, head(.SD, 2), by = 'cluster']$feats
# violinplot
violinPlot(visium_brain,
feats = unique(topgenes_gini),
cluster_column = 'leiden_clus',
strip_text = 8,
strip_position = 'right',
save_param = list(base_width = 5, base_height = 10))
# cluster heatmap
plotMetaDataHeatmap(visium_brain,
selected_feats = unique(topgenes_gini),
metadata_cols = c('leiden_clus'),
x_text_size = 10, y_text_size = 10)
# umap plots
dimFeatPlot2D(visium_brain,
expression_values = 'scaled',
feats = gini_markers_subclusters[, head(.SD, 1), by = 'cluster']$feats,
cow_n_col = 4,
point_size = 0.75,
save_param = list(base_width = 8, base_height = 8))
## ------------------ ##
# Scran Markers
scran_markers_subclusters = findMarkers_one_vs_all(gobject = visium_brain,
method = 'scran',
expression_values = 'normalized',
cluster_column = 'leiden_clus')
topgenes_scran = scran_markers_subclusters[, head(.SD, 2), by = 'cluster']$feats
# violinplot
violinPlot(visium_brain,
feats = unique(topgenes_scran),
cluster_column = 'leiden_clus',
strip_text = 10, strip_position = 'right',
save_param = list(base_width = 5))
# cluster heatmap
plotMetaDataHeatmap(visium_brain,
selected_feats = topgenes_scran,
metadata_cols = c('leiden_clus'))
# umap plots
dimFeatPlot2D(visium_brain,
expression_values = 'scaled',
feats = scran_markers_subclusters[, head(.SD, 1), by = 'cluster']$feats,
cow_n_col = 3,
point_size = 1,
save_param = list(base_width = 8, base_height = 8))
7. Cell type enrichment
Visium spatial transcriptomics does not provide single-cell resolution, making cell type annotation a harder problem. Giotto provides several ways to calculate enrichment of specific cell-type signature gene lists:
- PAGE
- hypergeometric test
- Rank
- DWLS Deconvolution Corresponded Single cell dataset can be generated from here. Giotto_SC is processed from the downsampled Loom file and can also be downloaded from GiottoData::getSpatialDataset().
# download data to results directory ####
# if wget is installed, set method = 'wget'
# if you run into authentication issues with wget, then add " extra = '--no-check-certificate' "
GiottoData::getSpatialDataset(dataset = 'scRNA_mouse_brain', directory = results_folder)
sc_expression = paste0(results_folder, "/brain_sc_expression_matrix.txt.gz")
sc_metadata = paste0(results_folder,"/brain_sc_metadata.csv")
giotto_SC = createGiottoObject(expression = sc_expression,
instructions = instrs)
giotto_SC = addCellMetadata(giotto_SC,
new_metadata = data.table::fread(sc_metadata))
giotto_SC = normalizeGiotto(giotto_SC)7.1 PAGE enrichment
# Create PAGE matrix
# PAGE matrix should be a binary matrix with each row represent a gene marker and each column represent a cell type
# There are several ways to create PAGE matrix
# 1.1 create binary matrix of cell signature genes
# small example #
gran_markers = c("Nr3c2", "Gabra5", "Tubgcp2", "Ahcyl2",
"Islr2", "Rasl10a", "Tmem114", "Bhlhe22",
"Ntf3", "C1ql2")
oligo_markers = c("Efhd1", "H2-Ab1", "Enpp6", "Ninj2",
"Bmp4", "Tnr", "Hapln2", "Neu4",
"Wfdc18", "Ccp110")
di_mesench_markers = c("Cartpt", "Scn1a", "Lypd6b", "Drd5",
"Gpr88", "Plcxd2", "Cpne7", "Pou4f1",
"Ctxn2", "Wnt4")
PAGE_matrix_1 = makeSignMatrixPAGE(sign_names = c('Granule_neurons',
'Oligo_dendrocytes',
'di_mesenchephalon'),
sign_list = list(gran_markers,
oligo_markers,
di_mesench_markers))
# ----
# 1.2 [shortcut] fully pre-prepared matrix for all cell types
sign_matrix_path = system.file("extdata", "sig_matrix.txt", package = 'GiottoData')
brain_sc_markers = data.table::fread(sign_matrix_path)
PAGE_matrix_2 = as.matrix(brain_sc_markers[,-1])
rownames(PAGE_matrix_2) = brain_sc_markers$Event
# ---
# 1.3 make PAGE matrix from single cell dataset
markers_scran = findMarkers_one_vs_all(gobject = giotto_SC,
method = "scran",
expression_values = "normalized",
cluster_column = "Class",
min_feats = 3)
top_markers = markers_scran[, head(.SD, 10), by = "cluster"]
celltypes = levels(factor(markers_scran$cluster))
sign_list = list()
for (i in 1:length(celltypes)){
sign_list[[i]] = top_markers[which(top_markers$cluster == celltypes[i]),]$feats
}
PAGE_matrix_3 = makeSignMatrixPAGE(sign_names = celltypes,
sign_list = sign_list)
# 1.4 enrichment test with PAGE
# runSpatialEnrich() can also be used as a wrapper for all currently provided enrichment options
visium_brain <- runPAGEEnrich(gobject = visium_brain,
sign_matrix = PAGE_matrix_2)
# 1.5 heatmap of enrichment versus annotation (e.g. clustering result)
cell_types_PAGE = colnames(PAGE_matrix_2)
plotMetaDataCellsHeatmap(gobject = visium_brain,
metadata_cols = 'leiden_clus',
value_cols = cell_types_PAGE,
spat_enr_names = 'PAGE',
x_text_size = 8,
y_text_size = 8)
# 1.6 visualizations
spatCellPlot2D(gobject = visium_brain,
spat_enr_names = 'PAGE',
cell_annotation_values = cell_types_PAGE[1:4],
cow_n_col = 2,
coord_fix_ratio = 1,
point_size = 1.25,
show_legend = T)
spatDimCellPlot2D(gobject = visium_brain,
spat_enr_names = 'PAGE',
cell_annotation_values = cell_types_PAGE[1:4],
cow_n_col = 1,
spat_point_size = 1,
plot_alignment = 'horizontal',
save_param = list(base_width = 7, base_height = 10))
7.2 HyperGeometric test
visium_brain <- runHyperGeometricEnrich(gobject = visium_brain,
expression_values = "normalized",
sign_matrix = PAGE_matrix_2)
cell_types_HyperGeometric = colnames(PAGE_matrix_2)
spatCellPlot(gobject = visium_brain,
spat_enr_names = 'hypergeometric',
cell_annotation_values = cell_types_HyperGeometric[1:4],
cow_n_col = 2,
coord_fix_ratio = NULL,
point_size = 1.75)
7.3 Rank Enrichment
# Create rank matrix, not that rank matrix is different from PAGE
# A count matrix and a vector for all cell labels will be needed
rank_matrix = makeSignMatrixRank(sc_matrix = getExpression(giotto_SC,
values = "normalized",
output = "matrix"),
sc_cluster_ids = pDataDT(giotto_SC)$Class)
colnames(rank_matrix) = levels(factor(pDataDT(giotto_SC)$Class))
visium_brain <- runRankEnrich(gobject = visium_brain,
sign_matrix = rank_matrix,
expression_values = "normalized")
# Plot Rank enrichment result
spatCellPlot2D(gobject = visium_brain,
spat_enr_names = 'rank',
cell_annotation_values = colnames(rank_matrix)[1:4],
cow_n_col = 2,
coord_fix_ratio = 1,
point_size = 1,
save_param = list(save_name = "spat_enr_Rank_plot"))
7.4 DWLS spatial deconvolution
# Create DWLS matrix, not that DWLS matrix is different from PAGE and rank
# A count matrix a vector for a list of gene signatures and a vector for all cell labels will be needed
DWLS_matrix = makeSignMatrixDWLSfromMatrix(matrix = getExpression(giotto_SC,
values = "normalized",
output = "matrix"),
cell_type = pDataDT(giotto_SC)$Class,
sign_gene = top_markers$feats)
visium_brain <- runDWLSDeconv(gobject = visium_brain,
sign_matrix = DWLS_matrix)
# Plot DWLS deconvolution result
spatCellPlot2D(gobject = visium_brain,
spat_enr_names = 'DWLS',
cell_annotation_values = levels(factor(pDataDT(giotto_SC)$Class))[1:4],
cow_n_col = 2,
coord_fix_ratio = 1,
point_size = 1,
save_param = list(save_name = "DWLS_plot"))
# Plot DWLS deconvolution result with Pie plots
spatDeconvPlot(visium_brain,
show_image = T,
radius = 50,
save_param = list(save_name = "spat_DWLS_pie_plot"))
8. Spatial Grid
visium_brain <- createSpatialGrid(gobject = visium_brain,
sdimx_stepsize = 400,
sdimy_stepsize = 400,
minimum_padding = 0)
showGiottoSpatGrids(visium_brain)
spatPlot2D(visium_brain,
cell_color = 'leiden_clus',
show_grid = T,
grid_color = 'red',
spatial_grid_name = 'spatial_grid')
9. spatial network
visium_brain <- createSpatialNetwork(gobject = visium_brain,
method = 'kNN',
k = 5,
maximum_distance_knn = 400,
name = 'spatial_network')
showGiottoSpatNetworks(visium_brain)
spatPlot2D(gobject = visium_brain,
show_network= T,
network_color = 'blue',
spatial_network_name = 'spatial_network')
10. Spatial Genes
## rank binarization
ranktest = binSpect(visium_brain,
bin_method = 'rank',
calc_hub = T,
hub_min_int = 5,
spatial_network_name = 'spatial_network')
spatFeatPlot2D(visium_brain,
expression_values = 'scaled',
feats = ranktest$feats[1:6],
cow_n_col = 2,
point_size = 1.5)
11. Spatial Co-Expression modules
# cluster the top 500 spatial genes into 20 clusters
ext_spatial_genes = ranktest[1:1500,]$feats
# here we use existing detectSpatialCorGenes function to calculate pairwise distances between genes (but set network_smoothing=0 to use default clustering)
spat_cor_netw_DT = detectSpatialCorFeats(visium_brain,
method = 'network',
spatial_network_name = 'spatial_network',
subset_feats = ext_spatial_genes)
# 2. identify most similar spatially correlated genes for one gene
top10_genes = showSpatialCorFeats(spat_cor_netw_DT,
feats = 'Ptprn',
show_top_feats = 10)
spatFeatPlot2D(visium_brain,
expression_values = 'scaled',
feats = top10_genes$variable[1:4],
point_size = 3)
# cluster spatial genes
spat_cor_netw_DT = clusterSpatialCorFeats(spat_cor_netw_DT,
name = 'spat_netw_clus',
k = 20)
# visualize clusters
heatmSpatialCorFeats(visium_brain,
spatCorObject = spat_cor_netw_DT,
use_clus_name = 'spat_netw_clus',
heatmap_legend_param = list(title = NULL),
save_param = list(base_height = 6, base_width = 8, units = 'cm'))
# 4. rank spatial correlated clusters and show genes for selected clusters
netw_ranks = rankSpatialCorGroups(visium_brain,
spatCorObject = spat_cor_netw_DT,
use_clus_name = 'spat_netw_clus',
save_param = list(base_height = 3, base_width = 5))
top_netw_spat_cluster = showSpatialCorFeats(spat_cor_netw_DT,
use_clus_name = 'spat_netw_clus',
selected_clusters = 6,
show_top_feats = 1)
# 5. create metagene enrichment score for clusters
cluster_genes_DT = showSpatialCorFeats(spat_cor_netw_DT,
use_clus_name = 'spat_netw_clus',
show_top_feats = 1)
cluster_genes = cluster_genes_DT$clus
names(cluster_genes) = cluster_genes_DT$feat_ID
visium_brain <- createMetafeats(visium_brain,
feat_clusters = cluster_genes,
name = 'cluster_metagene')
spatCellPlot(visium_brain,
spat_enr_names = 'cluster_metagene',
cell_annotation_values = netw_ranks$clusters,
point_size = 1,
cow_n_col = 5,
save_param = list(base_width = 15))
12. Spatially informed clusters
# top 30 genes per spatial co-expression cluster
table(spat_cor_netw_DT$cor_clusters$spat_netw_clus)
coexpr_dt = data.table::data.table(genes = names(spat_cor_netw_DT$cor_clusters$spat_netw_clus),
cluster = spat_cor_netw_DT$cor_clusters$spat_netw_clus)
data.table::setorder(coexpr_dt, cluster)
top30_coexpr_dt = coexpr_dt[, head(.SD, 30) , by = cluster]
my_spatial_genes = top30_coexpr_dt$genes
visium_brain <- runPCA(gobject = visium_brain,
feats_to_use = my_spatial_genes,
name = 'custom_pca')
visium_brain <- runUMAP(visium_brain,
dim_reduction_name = 'custom_pca',
dimensions_to_use = 1:20,
name = 'custom_umap')
visium_brain <- createNearestNetwork(gobject = visium_brain,
dim_reduction_name = 'custom_pca',
dimensions_to_use = 1:20,
k = 5,
name = 'custom_NN')
visium_brain <- doLeidenCluster(gobject = visium_brain,
network_name = 'custom_NN',
resolution = 0.15,
n_iterations = 1000,
name = 'custom_leiden')
cell_meta = pDataDT(visium_brain)
cell_clusters = unique(cell_meta$custom_leiden)
selected_colors = getDistinctColors(length(cell_clusters))
names(selected_colors) = cell_clusters
spatPlot2D(visium_brain,
cell_color = 'custom_leiden',
cell_color_code = selected_colors,
coord_fix_ratio = 1)
plotUMAP(gobject = visium_brain,
cell_color = 'custom_leiden',
cell_color_code = selected_colors,
point_size = 1.5)
13. Spatial domains with HMRF
# do HMRF with different betas on top 30 genes per spatial co-expression module
hmrf_folder = paste0(results_folder,'/','11_HMRF/')
if(!file.exists(hmrf_folder)) dir.create(hmrf_folder, recursive = T)
HMRF_spatial_genes = doHMRF(gobject = visium_brain,
expression_values = 'scaled',
spatial_genes = my_spatial_genes,
k = 20,
spatial_network_name="spatial_network",
betas = c(0, 10, 5),
output_folder = paste0(hmrf_folder, '/', 'Spatial_genes/SG_topgenes_k20_scaled'))
visium_brain <- addHMRF(gobject = visium_brain,
HMRFoutput = HMRF_spatial_genes,
k = 20,
betas_to_add = c(0, 10, 20, 30, 40),
hmrf_name = 'HMRF')
spatPlot2D(gobject = visium_brain,
cell_color = 'HMRF_k20_b.40')
14. Session info
R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/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: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] shiny_1.8.0 Giotto_4.0.2 GiottoClass_0.1.3
loaded via a namespace (and not attached):
[1] later_1.3.2 bitops_1.0-7 tibble_3.2.1
[4] R.oo_1.26.0 polyclip_1.10-6 lifecycle_1.0.4
[7] Rdpack_2.6 edgeR_4.0.14 doParallel_1.0.17
[10] globals_0.16.2 lattice_0.22-5 MASS_7.3-60.0.1
[13] backports_1.4.1 magrittr_2.0.3 limma_3.58.1
[16] sass_0.4.8 rmarkdown_2.25 jquerylib_0.1.4
[19] yaml_2.3.8 metapod_1.10.1 httpuv_1.6.14
[22] reticulate_1.35.0 cowplot_1.1.3 RColorBrewer_1.1-3
[25] abind_1.4-5 zlibbioc_1.48.0 Rtsne_0.17
[28] quadprog_1.5-8 GenomicRanges_1.54.1 purrr_1.0.2
[31] R.utils_2.12.3 BiocGenerics_0.48.1 RCurl_1.98-1.14
[34] pracma_2.4.4 tweenr_2.0.2 circlize_0.4.15
[37] GenomeInfoDbData_1.2.11 IRanges_2.36.0 S4Vectors_0.40.2
[40] ggrepel_0.9.5 irlba_2.3.5.1 listenv_0.9.1
[43] terra_1.7-71 dqrng_0.3.2 parallelly_1.36.0
[46] DelayedMatrixStats_1.24.0 colorRamp2_0.1.0 codetools_0.2-19
[49] DelayedArray_0.28.0 scuttle_1.12.0 ggforce_0.4.1
[52] tidyselect_1.2.0 shape_1.4.6 farver_2.1.1
[55] ScaledMatrix_1.10.0 matrixStats_1.2.0 stats4_4.3.2
[58] GiottoData_0.2.7.0 jsonlite_1.8.8 GetoptLong_1.0.5
[61] BiocNeighbors_1.20.2 ellipsis_0.3.2 progressr_0.14.0
[64] iterators_1.0.14 systemfonts_1.0.5 foreach_1.5.2
[67] dbscan_1.1-12 tools_4.3.2 ragg_1.2.7
[70] Rcpp_1.0.12 glue_1.7.0 SparseArray_1.2.3
[73] xfun_0.42 MatrixGenerics_1.14.0 GenomeInfoDb_1.38.6
[76] dplyr_1.1.4 withr_3.0.0 fastmap_1.1.1
[79] bluster_1.12.0 fansi_1.0.6 digest_0.6.34
[82] rsvd_1.0.5 R6_2.5.1 mime_0.12
[85] textshaping_0.3.7 colorspace_2.1-0 Cairo_1.6-2
[88] gtools_3.9.5 R.methodsS3_1.8.2 utf8_1.2.4
[91] tidyr_1.3.1 generics_0.1.3 smfishHmrf_0.1
[94] data.table_1.15.0 FNN_1.1.4 S4Arrays_1.2.0
[97] scatterpie_0.2.1 uwot_0.1.16 pkgconfig_2.0.3
[100] gtable_0.3.4 ComplexHeatmap_2.18.0 GiottoVisuals_0.1.4
[103] SingleCellExperiment_1.24.0 XVector_0.42.0 htmltools_0.5.7
[106] clue_0.3-65 scales_1.3.0 Biobase_2.62.0
[109] GiottoUtils_0.1.5 png_0.1-8 SpatialExperiment_1.12.0
[112] scran_1.30.2 ggfun_0.1.4 knitr_1.45
[115] rstudioapi_0.15.0 reshape2_1.4.4 rjson_0.2.21
[118] checkmate_2.3.1 cachem_1.0.8 GlobalOptions_0.1.2
[121] stringr_1.5.1 parallel_4.3.2 miniUI_0.1.1.1
[124] RcppZiggurat_0.1.6 pillar_1.9.0 grid_4.3.2
[127] vctrs_0.6.5 promises_1.2.1 BiocSingular_1.18.0
[130] beachmat_2.18.0 xtable_1.8-4 cluster_2.1.6
[133] evaluate_0.23 magick_2.8.2 cli_3.6.2
[136] locfit_1.5-9.8 compiler_4.3.2 rlang_1.1.3
[139] crayon_1.5.2 future.apply_1.11.1 labeling_0.4.3
[142] fs_1.6.3 plyr_1.8.9 stringi_1.8.3
[145] BiocParallel_1.36.0 munsell_0.5.0 Matrix_1.6-5
[148] sparseMatrixStats_1.14.0 future_1.33.1 ggplot2_3.4.4
[151] statmod_1.5.0 SummarizedExperiment_1.32.0 rbibutils_2.2.16
[154] Rfast_2.1.0 igraph_2.0.1.1 memoise_2.0.1
[157] RcppParallel_5.1.7 bslib_0.6.1