# Util libs
library(assertthat)
library(ggplot2)
library(zeallot)
library(conflicted)
library(Matrix)
library(stringr)
# Data processing libs
if (!suppressWarnings(require(COTAN))) {
devtools::load_all("~/dev/COTAN/COTAN/")
}
library(SingleCellExperiment)
library(S4Vectors)
library(hdf5r)
library(hdf5r.Extra)
conflicts_prefer(zeallot::`%->%`, zeallot::`%<-%`)
conflicts_prefer(base::setdiff)
conflicts_prefer(Matrix::unname)
options(parallelly.fork.enable = TRUE)
setLoggingLevel(2L)
setLoggingFile(file.path(".", "Dataset_Cleanup.log"))Mouse Brain Open Problem preview
Preamble
List input files
Data come from site.
A murine brain atlas with adjacent cell types as assumed benchmark truth, inferred from deconvolution proportion correlations using matching 10x Visium slides (see Dimitrov et al., 2022).
The sequencing is SMARTer (Smart-seq–type full-length scRNA-seq, plate-based) so it is micro-wells based (no UMI).
inDir <- file.path("MouseBrainOpenProblem/")
outDir <- file.path("MouseBrainOpenProblem/")
list.files(path = inDir)[1] "dataset.h5ad" "DatasetCleaning.log"
[3] "Mouse.MitoCarta3.0.xls" "MouseBrainOP_COTAN_raw.RDS"
[5] "MouseBrainOPCOTAN-Cleaned.RDS" "Note" h5adFile <- "MouseBrainOpenProblem/dataset.h5ad"Inspect h5ad file
h5 <- H5File$new(h5adFile, mode = "r")
for (nm in names(h5)) {
cat("Name:", nm, " -> class:", class(h5[[nm]]), "\n")
}Name: layers -> class: H5Group H5RefClass R6
Name: obs -> class: H5Group H5RefClass R6
Name: obsm -> class: H5Group H5RefClass R6
Name: obsp -> class: H5Group H5RefClass R6
Name: uns -> class: H5Group H5RefClass R6
Name: var -> class: H5Group H5RefClass R6
Name: varm -> class: H5Group H5RefClass R6
Name: varp -> class: H5Group H5RefClass R6 h5$close_all()Load matrices and genes/cells meta-data
## 1) counts: layers/counts -> genes × cells
counts <- h5Read(h5adFile, name = "layers/counts", transpose = FALSE)
# counts: rows = genes, cols = cells
## 2) obs / var (you already handled categoricals)
obs <- h5Read(h5adFile, name = "obs")
var <- h5Read(h5adFile, name = "var")
nGenes <- nrow(var)
nCells <- nrow(obs)
stopifnot(
nGenes == nrow(counts),
nCells == ncol(counts)
)
## Ensure row/colnames are consistent
# AnnData convention: obs index = cell IDs, var index = gene IDs
if (is.null(rownames(obs))) {
# if needed, derive from a specific column
# rownames(obs) <- obs$some_cell_id_column
}
if (is.null(rownames(var))) {
# rownames(var) <- var$some_gene_id_column
}
# Now align the matrix names to metadata
if (is.null(colnames(counts))) colnames(counts) <- rownames(obs)
if (is.null(rownames(counts))) rownames(counts) <- rownames(var)
assert_that(
identical(colnames(counts), rownames(obs)),
identical(rownames(counts), rownames(var))
)[1] TRUERetrieve clusterizations as factors
table(obs$cell_type, useNA = "ifany")
Vip Lamp5 Sst Sncg Serpinf1 Pvalb Endo
1728 1122 1741 125 27 1337 94
Peri L6.CT L6b L6.IT L2.3.IT CR L5.PT
32 960 358 1872 982 7 544
NP L4 L5.IT Oligo Meis2 Astro Macrophage
362 1401 880 91 45 368 51
VLMC SMC
67 55 Create SingleCellExperiment
sce <- SingleCellExperiment(
assays = list(
counts = counts
)
)
# Attach metadata
colData(sce) <- DataFrame(obs) # cells metadata
rowData(sce) <- DataFrame(var) # genes metadata
# load processed matrices
if (FALSE) {
X <- h5Read(h5adFile, name = "X", transpose = FALSE) # genes × cells
assert_that(all(dim(X) == dim(counts)))
assays(sce)[["logcounts"]] <- X # or "normcounts", depending on what X really is
}
if (FALSE) {
obsm <- h5Read(h5adFile, name = "obsm")
names(obsm)
# e.g. "X_pca", "X_umap", "X_tsne", ...
# Simple mapping from AnnData keys to SCE reducedDim names
rdMap <- c(
X_pca = "PCA",
X_umap = "UMAP",
X_tsne = "TSNE",
X_mde = "MDE",
X_scvi = "SCVI"
)
for (key in intersect(names(obsm), names(rdMap))) {
mat <- obsm[[key]] # cells × k
# sanity check
stopifnot(nrow(mat) == ncol(sce))
reducedDims(sce)[[ rdMap[[key]] ]] <- as.matrix(mat)
}
}
uns <- h5Read(h5adFile, name = "uns")
# root-level attributes
h5 <- H5File$new(h5adFile, mode = "r")
rootAttrs <- h5attributes(h5)
h5$close_all()
# Store them in SCE metadata
metadata(sce)$uns <- uns
metadata(sce)$h5ad_attrs <- rootAttrs
metadata(sce)$source_file <- h5adFile
metadata(sce)$source_format <- "AnnData h5ad"Check all OK
sceclass: SingleCellExperiment
dim: 34617 14249
metadata(4): uns h5ad_attrs source_file source_format
assays(1): counts
rownames(34617): 0610005C13Rik 0610006L08Rik ... l7Rn6 n-R5s136
rowData names(4): n_cells feature_name hvg hvg_score
colnames(14249): F1S4_160108_001_A01 F1S4_160108_001_B01 ...
FYS4_171004_104_G01 FYS4_171004_104_H01
colData names(6): label sizeFactor ... cell_type size_factors
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):assayNames(sce)[1] "counts"colData(sce)[1:3, ]DataFrame with 3 rows and 6 columns
label sizeFactor subclass n_counts cell_type
<factor> <numeric> <factor> <numeric> <factor>
F1S4_160108_001_A01 Vip 1.08531 Vip 1730700 Vip
F1S4_160108_001_B01 Lamp5 1.19750 Lamp5 1909605 Lamp5
F1S4_160108_001_C01 Lamp5 1.24475 Lamp5 1984948 Lamp5
size_factors
<numeric>
F1S4_160108_001_A01 1730700
F1S4_160108_001_B01 1909605
F1S4_160108_001_C01 1984948rowData(sce)[1:3, ]DataFrame with 3 rows and 4 columns
n_cells feature_name hvg hvg_score
<integer> <character> <logical> <numeric>
0610005C13Rik 334 0610005C13Rik FALSE 0.133368
0610006L08Rik 17 0610006L08Rik FALSE 1.367953
0610007P14Rik 13189 0610007P14Rik FALSE -0.991799reducedDimNames(sce)character(0)names(metadata(sce))[1] "uns" "h5ad_attrs" "source_file" "source_format"Convert SCE objects to COTAN
## helper: convert a single SCE + its global attrs into a COTAN object
sceToCotan <- function(sce, globalAttrs = list()) {
# 1. SCE -> COTAN (handles counts, rowData, colData, cluster columns, etc.)
objCOTAN <- convertFromSingleCellExperiment(objSCE = sce)
# 2. Initialize metaDataset with standard tags (use whatever you have)
# If your globalAttrs already contain these, pick from there.
geoVal <-
if ("GEO" %in% names(globalAttrs)) globalAttrs[["GEO"]] else ""
seqVal <-
if ("method" %in% names(globalAttrs)) globalAttrs[["method"]] else ""
condVal <-
if ("condition" %in% names(globalAttrs)) globalAttrs[["condition"]] else ""
objCOTAN <- initializeMetaDataset(
objCOTAN,
GEO = as.character(geoVal),
sequencingMethod = as.character(seqVal),
sampleCondition = as.character(condVal)
)
# 3. Add all remaining global attributes as extra metadata rows
usedTags <- c("GEO", "method", "condition")
extraTags <- base::setdiff(names(globalAttrs), usedTags)
for (tag in extraTags) {
val <- globalAttrs[[tag]]
# COTAN expects a character vector; coerce anything else
objCOTAN <- addElementToMetaDataset(
objCOTAN,
tag = tag,
value = as.character(val)
)
}
objCOTAN
}metadata <- metadata(sce)
metadata_cells <- NA
metadata_genes <- NA
metadata_dataset <- NA
for (n in c(1:length(metadata$uns))) {
label <- names(metadata$uns[n])
#print(length(metadata$uns[[n]]))
if (length(metadata$uns[[n]]) == dim(colData(sce))[1]) {
metadata_cells <- cbind(metadata_cells, metadata$uns[[n]])
metadata_cells <- as.data.frame(metadata_cells)
colnames(metadata_cells)[length(colnames(metadata_cells))] <- label
}else if(length(metadata$uns[[n]]) == dim(rowData(sce))[1]) {
metadata_genes <- cbind(metadata_genes, metadata$uns[[n]])
metadata_genes <- as.data.frame(metadata_genes)
colnames(metadata_genes)[length(colnames(metadata_genes))] <- label
}else{
print(label)
metadata_dataset <- c(metadata_dataset,metadata$uns[[n]])
names(metadata_dataset)[length(metadata_dataset)] <- label
}
}[1] "_from_cache"
[1] "ccc_target"
[1] "data_reference"
[1] "data_url"
[1] "dataset_description"
[1] "dataset_id"
[1] "dataset_name"
[1] "dataset_organism"
[1] "dataset_reference"
[1] "dataset_summary"
[1] "dataset_url"
[1] "knn"
[1] "normalization_id"
[1] "pca_variance"
[1] "target_organism"metadata_cells$metadata_cells <- rownames(colData(sce))
identical(str_split(metadata_cells$metadata_cells,pattern = "_",simplify = T)[,1],metadata_cells$orig.ident)[1] TRUEhead(metadata_cells) metadata_cells age_days brain_hemisphere brain_region brain_subregion
1 F1S4_160108_001_A01 53 L VISp L2/3
2 F1S4_160108_001_B01 53 L VISp L2/3
3 F1S4_160108_001_C01 53 L VISp L2/3
4 F1S4_160108_001_D01 53 L VISp L2/3
5 F1S4_160108_001_E01 53 L VISp L2/3
6 F1S4_160108_001_F01 53 L VISp L2/3
class cluster cluster_correlation complexity_cg
1 GABAergic Vip Arhgap36 Hmcn1 0.8372286 0.3549598
2 GABAergic Lamp5 Lsp1 0.8787432 0.3506406
3 GABAergic Lamp5 Lsp1 0.8870837 0.3608883
4 GABAergic Vip Crispld2 Htr2c 0.8435516 0.3689915
5 GABAergic Lamp5 Plch2 Dock5 0.8549944 0.3785353
6 GABAergic Sst Tac1 Tacr3 0.8560629 0.3575420
confusion_score core_intermediate_call donor driver_lines eye_condition
1 0.4385 Intermediate 225675 Slc32a1-IRES-Cre Normal
2 0.1025 Core 225675 Slc32a1-IRES-Cre Normal
3 0.0195 Core 225675 Slc32a1-IRES-Cre Normal
4 0.2734 Core 225675 Slc32a1-IRES-Cre Normal
5 0.7532 Core 225675 Slc32a1-IRES-Cre Normal
6 0.4291 Core 225675 Slc32a1-IRES-Cre Normal
facs_container facs_date facs_sort_criteria genes_detected_cpm_criterion
1 F1S4_160108_001 1/8/2016 RFP-positive 10445
2 F1S4_160108_001 1/8/2016 RFP-positive 11600
3 F1S4_160108_001 1/8/2016 RFP-positive 11848
4 F1S4_160108_001 1/8/2016 RFP-positive 9494
5 F1S4_160108_001 1/8/2016 RFP-positive 10012
6 F1S4_160108_001 1/8/2016 RFP-positive 10696
genes_detected_fpkm_criterion genotype gfp_cpm
1 9222 Slc32a1-IRES-Cre/wt;Ai14(RCL-tdT)/wt 248.86
2 10370 Slc32a1-IRES-Cre/wt;Ai14(RCL-tdT)/wt 289.61
3 10734 Slc32a1-IRES-Cre/wt;Ai14(RCL-tdT)/wt 281.06
4 8561 Slc32a1-IRES-Cre/wt;Ai14(RCL-tdT)/wt 390.02
5 8791 Slc32a1-IRES-Cre/wt;Ai14(RCL-tdT)/wt 253.92
6 9145 Slc32a1-IRES-Cre/wt;Ai14(RCL-tdT)/wt 195.80
injection_exclusion_criterion injection_label_direction injection_material
1 No Injection No Injection No Injection
2 No Injection No Injection No Injection
3 No Injection No Injection No Injection
4 No Injection No Injection No Injection
5 No Injection No Injection No Injection
6 No Injection No Injection No Injection
injection_primary injection_secondary injection_tract
1 No Injection No Injection No Injection
2 No Injection No Injection No Injection
3 No Injection No Injection No Injection
4 No Injection No Injection No Injection
5 No Injection No Injection No Injection
6 No Injection No Injection No Injection
library_prep_avg_size_bp library_prep_set nCount_RNA nFeature_RNA
1 436 L8S4_160205_01_A02 1730700 9029
2 445 L8S4_160205_01_B02 1909605 10207
3 441 L8S4_160205_01_C02 1984948 10578
4 489 L8S4_160205_01_D02 2291552 8482
5 424 L8S4_160205_01_E02 1757463 8697
6 432 L8S4_160205_01_F02 1837003 8990
organism orig.ident percent_aligned_reads_total percent_ecoli_reads
1 Mus musculus F1S4 100 0.01961939
2 Mus musculus F1S4 100 0.01472401
3 Mus musculus F1S4 100 0.01271205
4 Mus musculus F1S4 100 0.01806790
5 Mus musculus F1S4 100 0.03056065
6 Mus musculus F1S4 100 0.02158139
percent_exon_reads percent_intergenic_reads percent_intron_reads
1 74.16672 4.909982 20.923297
2 77.55741 5.064451 17.378139
3 83.13226 4.254840 12.612901
4 89.32453 3.051048 7.624426
5 84.23305 4.425087 11.341862
6 74.88262 5.324116 19.793264
percent_mt_exon_reads percent_reads_unique percent_rrna_reads
1 0.000104709 83.76778 0.13552879
2 0.000401765 84.28387 0.11711441
3 0.000312293 84.28835 0.11565239
4 0.000226192 83.99171 0.25740658
5 0.000380690 80.91289 0.16015630
6 0.000227679 81.93723 0.09208001
percent_synth_reads reporter_lines rna_amplification_set sample_id
1 0.004889926 Ai14(RCL-tdT) A8S4_160202_01 527128530
2 0.003900800 Ai14(RCL-tdT) A8S4_160202_01 527128536
3 0.003320712 Ai14(RCL-tdT) A8S4_160202_01 527128542
4 0.005803442 Ai14(RCL-tdT) A8S4_160202_01 527128548
5 0.005922395 Ai14(RCL-tdT) A8S4_160202_01 527128554
6 0.004896081 Ai14(RCL-tdT) A8S4_160202_01 527128560
sample_type seq_batch seq_name seq_tube sex subclass tdt_cpm
1 Cells R8S4-160225 LS-15006_S09_E1-50 LS-15006 M Vip 248.86
2 Cells R8S4-160225 LS-15006_S10_E1-50 LS-15006 M Lamp5 289.61
3 Cells R8S4-160225 LS-15006_S11_E1-50 LS-15006 M Lamp5 281.06
4 Cells R8S4-160225 LS-15006_S12_E1-50 LS-15006 M Vip 390.02
5 Cells R8S4-160225 LS-15006_S13_E1-50 LS-15006 M Lamp5 253.92
6 Cells R8S4-160225 LS-15006_S14_E1-50 LS-15006 M Sst 195.80
total_reads
1 2865074
2 2986823
3 2881912
4 3094715
5 2626809
6 3074500Create the COTAN object
objCOTAN <- sceToCotan(sce, globalAttrs = metadata_dataset[6:14])
objCOTAN@metaDataset[2,2] <-"SMARTer"
identical(rownames(objCOTAN@metaCells),metadata_cells$metadata_cells)[1] TRUEobjCOTAN@metaCells <- cbind(objCOTAN@metaCells,metadata_cells)
metaCellsNames <- colnames(getMetadataCells(objCOTAN))
metaCellsNames [1] "label" "sizeFactor"
[3] "subclass" "n_counts"
[5] "cell_type" "size_factors"
[7] "metadata_cells" "age_days"
[9] "brain_hemisphere" "brain_region"
[11] "brain_subregion" "class"
[13] "cluster" "cluster_correlation"
[15] "complexity_cg" "confusion_score"
[17] "core_intermediate_call" "donor"
[19] "driver_lines" "eye_condition"
[21] "facs_container" "facs_date"
[23] "facs_sort_criteria" "genes_detected_cpm_criterion"
[25] "genes_detected_fpkm_criterion" "genotype"
[27] "gfp_cpm" "injection_exclusion_criterion"
[29] "injection_label_direction" "injection_material"
[31] "injection_primary" "injection_secondary"
[33] "injection_tract" "library_prep_avg_size_bp"
[35] "library_prep_set" "nCount_RNA"
[37] "nFeature_RNA" "organism"
[39] "orig.ident" "percent_aligned_reads_total"
[41] "percent_ecoli_reads" "percent_exon_reads"
[43] "percent_intergenic_reads" "percent_intron_reads"
[45] "percent_mt_exon_reads" "percent_reads_unique"
[47] "percent_rrna_reads" "percent_synth_reads"
[49] "reporter_lines" "rna_amplification_set"
[51] "sample_id" "sample_type"
[53] "seq_batch" "seq_name"
[55] "seq_tube" "sex"
[57] "subclass" "tdt_cpm"
[59] "total_reads" for (clName in metaCellsNames[c(5,7:13)]) {
clusters <- factor(getMetadataCells(objCOTAN)[[clName]])
levels(clusters)[is.na(levels(clusters))] <- "NA"
names(clusters) <- getCells(objCOTAN)
objCOTAN <- addClusterization(objCOTAN, clName = clName, clusters = clusters)
}
for (condName in metaCellsNames[c(7:13,19:23,26,27,49:56)]) {
condition <- getMetadataCells(objCOTAN)[[condName]]
names(condition) <- getCells(objCOTAN)
objCOTAN <- addCondition(objCOTAN, condName = condName, conditions = condition)
}
getMetadataDataset(objCOTAN) V1
1 GEO:
2 scRNAseq method:
3 condition sample:
4 starting n. of cells:
5 genes' COEX is in sync:
6 cells' COEX is in sync:
7 data_reference
8 data_url
9 dataset_description
10 dataset_id
11 dataset_name
12 dataset_organism
13 dataset_reference
14 dataset_summary
15 dataset_url
V2
1
2 SMARTer
3
4 14249
5 FALSE
6 FALSE
7 tasic2016adult
8 https://figshare.com/ndownloader/files/36509385
9 A murine brain atlas with adjacent cell types as assumed benchmark truth, inferred from deconvolution proportion correlations using matching 10x Visium slides (see Dimitrov et al., 2022).
10 openproblems_v1/allen_brain_atlas
11 Mouse Brain Atlas
12 mus_musculus
13 tasic2016adult
14 Adult mouse primary visual cortex
15 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585fileOut <- "MouseBrainOpenProblem/MouseBrainOP_COTAN_raw.RDS"
saveRDS(objCOTAN, file = fileOut)Sys.time()[1] "2026-01-26 10:59:21 CET"sessionInfo()R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 LAPACK version 3.10.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Rome
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] hdf5r.Extra_0.1.0 hdf5r_1.3.12
[3] SingleCellExperiment_1.32.0 SummarizedExperiment_1.38.1
[5] Biobase_2.70.0 GenomicRanges_1.62.1
[7] Seqinfo_1.0.0 IRanges_2.44.0
[9] S4Vectors_0.48.0 BiocGenerics_0.56.0
[11] generics_0.1.3 MatrixGenerics_1.22.0
[13] matrixStats_1.5.0 COTAN_2.11.1
[15] stringr_1.6.0 Matrix_1.7-4
[17] conflicted_1.2.0 zeallot_0.2.0
[19] ggplot2_4.0.1 assertthat_0.2.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.5.2 later_1.4.2
[4] tibble_3.3.0 polyclip_1.10-7 fastDummies_1.7.5
[7] lifecycle_1.0.4 doParallel_1.0.17 globals_0.18.0
[10] lattice_0.22-7 MASS_7.3-65 backports_1.5.0
[13] ggdist_3.3.3 dendextend_1.19.0 magrittr_2.0.4
[16] plotly_4.12.0 rmarkdown_2.29 yaml_2.3.12
[19] httpuv_1.6.16 otel_0.2.0 Seurat_5.4.0
[22] sctransform_0.4.2 spam_2.11-1 sp_2.2-0
[25] spatstat.sparse_3.1-0 reticulate_1.44.1 cowplot_1.2.0
[28] pbapply_1.7-2 RColorBrewer_1.1-3 abind_1.4-8
[31] Rtsne_0.17 purrr_1.2.0 circlize_0.4.16
[34] GenomeInfoDbData_1.2.14 ggrepel_0.9.6 irlba_2.3.5.1
[37] listenv_0.10.0 spatstat.utils_3.2-1 goftest_1.2-3
[40] RSpectra_0.16-2 spatstat.random_3.4-3 fitdistrplus_1.2-2
[43] parallelly_1.46.0 codetools_0.2-20 DelayedArray_0.36.0
[46] tidyselect_1.2.1 shape_1.4.6.1 UCSC.utils_1.4.0
[49] farver_2.1.2 ScaledMatrix_1.16.0 viridis_0.6.5
[52] spatstat.explore_3.7-0 jsonlite_2.0.0 GetoptLong_1.1.0
[55] progressr_0.18.0 ggridges_0.5.6 survival_3.8-3
[58] iterators_1.0.14 foreach_1.5.2 tools_4.5.2
[61] ica_1.0-3 Rcpp_1.1.0 glue_1.8.0
[64] gridExtra_2.3 SparseArray_1.10.8 xfun_0.52
[67] distributional_0.6.0 ggthemes_5.2.0 GenomeInfoDb_1.44.0
[70] dplyr_1.1.4 withr_3.0.2 fastmap_1.2.0
[73] digest_0.6.37 rsvd_1.0.5 parallelDist_0.2.6
[76] R6_2.6.1 mime_0.13 colorspace_2.1-1
[79] scattermore_1.2 tensor_1.5 spatstat.data_3.1-9
[82] tidyr_1.3.1 data.table_1.18.0 httr_1.4.7
[85] htmlwidgets_1.6.4 S4Arrays_1.10.1 uwot_0.2.3
[88] pkgconfig_2.0.3 gtable_0.3.6 ComplexHeatmap_2.26.0
[91] lmtest_0.9-40 S7_0.2.1 XVector_0.50.0
[94] htmltools_0.5.8.1 dotCall64_1.2 zigg_0.0.2
[97] clue_0.3-66 SeuratObject_5.3.0 scales_1.4.0
[100] png_0.1-8 spatstat.univar_3.1-6 knitr_1.50
[103] reshape2_1.4.4 rjson_0.2.23 checkmate_2.3.2
[106] nlme_3.1-168 proxy_0.4-29 cachem_1.1.0
[109] zoo_1.8-14 GlobalOptions_0.1.2 KernSmooth_2.23-26
[112] parallel_4.5.2 miniUI_0.1.2 pillar_1.11.1
[115] grid_4.5.2 vctrs_0.7.0 RANN_2.6.2
[118] promises_1.5.0 BiocSingular_1.26.1 beachmat_2.26.0
[121] xtable_1.8-4 cluster_2.1.8.1 evaluate_1.0.5
[124] cli_3.6.5 compiler_4.5.2 rlang_1.1.7
[127] crayon_1.5.3 future.apply_1.20.0 easy.utils_0.1.0
[130] plyr_1.8.9 stringi_1.8.7 viridisLite_0.4.2
[133] deldir_2.0-4 BiocParallel_1.44.0 lazyeval_0.2.2
[136] spatstat.geom_3.7-0 RcppHNSW_0.6.0 patchwork_1.3.2
[139] bit64_4.6.0-1 future_1.69.0 shiny_1.12.1
[142] ROCR_1.0-11 Rfast_2.1.5.1 igraph_2.2.1
[145] memoise_2.0.1 RcppParallel_5.1.10 fastmatch_1.1-6
[148] bit_4.6.0