Analysing 20k cells
2025-02-24
Last updated: 2025-02-24
Checks: 7 0
Knit directory: muse/
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Ignored: data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
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Ignored: data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6daf5ce | Dave Tang | 2025-02-24 | Saving and loading on-disk backed Seurat objects |
| html | 4dc6978 | Dave Tang | 2025-02-23 | Build site. |
| Rmd | 9e472aa | Dave Tang | 2025-02-23 | Convert to sparse matrix for annotation |
| html | ffad125 | Dave Tang | 2025-02-23 | Build site. |
| Rmd | 0e6715c | Dave Tang | 2025-02-23 | Annotate using SingleR |
| html | 3a0e769 | Dave Tang | 2025-02-23 | Build site. |
| Rmd | aad6fa2 | Dave Tang | 2025-02-23 | Normalised and scaled data are stored as iterable matrices |
| html | 9a02923 | Dave Tang | 2025-02-22 | Build site. |
| Rmd | 6c0d4f0 | Dave Tang | 2025-02-22 | Convert sparse matrix to iterable matrix |
| html | f6301ec | Dave Tang | 2025-02-22 | Build site. |
| Rmd | 22af89e | Dave Tang | 2025-02-22 | Analysing 20k cells |
Data
Read HDF5 files into a list.
hdf5_files <- list.files(path = "data", pattern = "5k_Human", full.names = TRUE)
hdf5_files[1] "data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
[2] "data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
[3] "data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
[4] "data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"Read raw counts into a list of matrices.
mats <- purrr::map(seq_along(hdf5_files), function(x){
my_mat <- Seurat::Read10X_h5(hdf5_files[x])
colnames(my_mat) <- paste0('donor', x, '_', colnames(my_mat))
my_mat
})
str(mats, max.level = 1)List of 4
$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slotsSeurat object
Create Seurat object from the list of matrices.
pbmc20k <- CreateSeuratObject(
counts = mats,
min.cells = 3,
min.features = 200
)
pbmc20kAn object of class Seurat
27385 features across 22061 samples within 1 assay
Active assay: RNA (27385 features, 0 variable features)
4 layers present: counts.1, counts.2, counts.3, counts.4Create one count layer.
pbmc20k <- JoinLayers(pbmc20k)
pbmc20kAn object of class Seurat
27385 features across 22061 samples within 1 assay
Active assay: RNA (27385 features, 0 variable features)
1 layer present: countsDonor information in orig.ident.
head(pbmc20k@meta.data) orig.ident nCount_RNA nFeature_RNA
donor1_AAACCAAAGGTGACGA-1 donor1 42833 7079
donor1_AAACCCTGTGACGAGT-1 donor1 4890 2102
donor1_AAACGAATCAGGCTAC-1 donor1 12498 3564
donor1_AAACGACAGATTGACT-1 donor1 22193 4366
donor1_AAACGATGTCTTGAAC-1 donor1 10305 2945
donor1_AAACGATGTGCGCGAA-1 donor1 15947 4160Use {BPCells} to convert the matrices in your already created Seurat objects to on-disk matrices. Note, that this is only possible for V5 assays. Convert the counts matrix of the RNA assay to a BPCells matrix.
BPCells::write_matrix_dir(
mat = BPCells::convert_matrix_type(matrix = pbmc20k@assays$RNA$counts, type = "uint32_t"),
dir = 'data/pbmc20k',
overwrite = TRUE
)27385 x 22061 IterableMatrix object with class MatrixDir
Row names: ENSG00000238009, ENSG00000239945 ... AMELY
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1
Data type: uint32_t
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20kpbmc20k.mat <- open_matrix_dir(dir = "data/pbmc20k")
pbmc20k@assays$RNA$counts <- pbmc20k.mat
pbmc20k@assays$RNA$counts27385 x 22061 IterableMatrix object with class RenameDims
Row names: ENSG00000238009, ENSG00000239945 ... AMELY
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1
Data type: uint32_t
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20k
2. Reset dimnamesSeurat workflow
Process with the Seurat 4 workflow.
options(future.globals.maxSize = 2 * 1024^3)
fixed_PrepDR5 <- function(object, features = NULL, layer = 'scale.data', verbose = TRUE) {
layer <- layer[1L]
olayer <- layer
layer <- SeuratObject::Layers(object = object, search = layer)
if (is.null(layer)) {
abort(paste0("No layer matching pattern '", olayer, "' not found. Please run ScaleData and retry"))
}
data.use <- SeuratObject::LayerData(object = object, layer = layer)
features <- features %||% VariableFeatures(object = object)
if (!length(x = features)) {
stop("No variable features, run FindVariableFeatures() or provide a vector of features", call. = FALSE)
}
if (is(data.use, "IterableMatrix")) {
features.var <- BPCells::matrix_stats(matrix=data.use, row_stats="variance")$row_stats["variance",]
} else {
features.var <- apply(X = data.use, MARGIN = 1L, FUN = var)
}
features.keep <- features[features.var > 0]
if (!length(x = features.keep)) {
stop("None of the requested features have any variance", call. = FALSE)
} else if (length(x = features.keep) < length(x = features)) {
exclude <- setdiff(x = features, y = features.keep)
if (isTRUE(x = verbose)) {
warning(
"The following ",
length(x = exclude),
" features requested have zero variance; running reduction without them: ",
paste(exclude, collapse = ', '),
call. = FALSE,
immediate. = TRUE
)
}
}
features <- features.keep
features <- features[!is.na(x = features)]
features.use <- features[features %in% rownames(data.use)]
if(!isTRUE(all.equal(features, features.use))) {
missing_features <- setdiff(features, features.use)
if(length(missing_features) > 0) {
warning_message <- paste("The following features were not available: ",
paste(missing_features, collapse = ", "),
".", sep = "")
warning(warning_message, immediate. = TRUE)
}
}
data.use <- data.use[features.use, ]
return(data.use)
}
assignInNamespace('PrepDR5', fixed_PrepDR5, 'Seurat')
seurat_wf_v4 <- function(seurat_obj, scale_factor = 1e4, num_features = 2000, num_pcs = 30, cluster_res = 0.5, debug_flag = FALSE){
seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = scale_factor, verbose = debug_flag)
seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = num_features, verbose = debug_flag)
seurat_obj <- ScaleData(seurat_obj, verbose = debug_flag)
seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
seurat_obj <- RunHarmony(seurat_obj, "orig.ident")
seurat_obj <- RunUMAP(seurat_obj, reduction = "harmony", dims = 1:num_pcs, verbose = debug_flag)
seurat_obj
}
pbmc20k <- seurat_wf_v4(pbmc20k)Transposing data matrixInitializing state using k-means centroids initializationHarmony 1/10Harmony 2/10Harmony 3/10Harmony converged after 3 iterationsWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per sessionNormalised and scaled data are stored as IterableMatrix
objects.
pbmc20k@assays$RNA$data27385 x 22061 IterableMatrix object with class RenameDims
Row names: ENSG00000238009, ENSG00000239945 ... AMELY
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20k
2. Reset dimnames
3. Convert type from uint32_t to double
4. Scale by 1e+04
5. Scale columns by 2.33e-05, 0.000204 ... 7.7e-05
6. Transform log1p
7. Reset dimnamespbmc20k@assays$RNA$scale.data2000 x 22061 IterableMatrix object with class RenameDims
Row names: HES4, ISG15 ... ENSG00000265995
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20k
2. Select rows: 19, 20 ... 27288 and cols: all
3. Reset dimnames
4. Convert type from uint32_t to double
5. Scale by 1e+04
6. Scale columns by 2.33e-05, 0.000204 ... 7.7e-05
7. Transform log1p
8. Select rows: 489, 1810 ... 438 and cols: all
9. Reset dimnames
10. Transform min by row: 2.8, 2.49 ... 1
11. Scale rows by 3.59, 4.04 ... 10.1
12. Shift rows by -0.0687, -0.0609 ... -0.12
13. Select rows: 1243, 443 ... 1553 and cols: all
14. Reset dimnamesUMAP.
DimPlot(pbmc20k, reduction = "umap", group.by = "orig.ident", pt.size = .1)
| Version | Author | Date |
|---|---|---|
| f6301ec | Dave Tang | 2025-02-22 |
Annotation
Annotate using {SingleR}.
monaco_immune <- fetchReference("monaco_immune", "2024-02-26")
monaco_immuneclass: SummarizedExperiment
dim: 46077 114
metadata(0):
assays(1): logcounts
rownames(46077): A1BG A1BG-AS1 ... ZYX ZZEF1
rowData names(0):
colnames(114): DZQV_CD8_naive DZQV_CD8_CM ... G4YW_Neutrophils
G4YW_Basophils
colData names(3): label.main label.fine label.ontpbmc20k.anno <- SingleR(
test=as(pbmc20k@assays$RNA$data, "sparseMatrix"),
ref=monaco_immune,
labels=colData(monaco_immune)$label.main
)Warning: Converting to a dense matrix may use excessive memory
This message is displayed once every 8 hours.Warning in asMethod(object): sparse->dense coercion: allocating vector of size
4.5 GiBhead(pbmc20k.anno)DataFrame with 6 rows and 4 columns
scores labels
<matrix> <character>
donor1_AAACCAAAGGTGACGA-1 0.324853:0.258408:0.483121:... T cells
donor1_AAACCCTGTGACGAGT-1 0.151325:0.124804:0.304293:... CD4+ T cells
donor1_AAACGAATCAGGCTAC-1 0.252303:0.235463:0.436783:... CD4+ T cells
donor1_AAACGACAGATTGACT-1 0.318234:0.338696:0.122619:... Monocytes
donor1_AAACGATGTCTTGAAC-1 0.230288:0.199487:0.418062:... CD4+ T cells
donor1_AAACGATGTGCGCGAA-1 0.470569:0.243399:0.252875:... B cells
delta.next pruned.labels
<numeric> <character>
donor1_AAACCAAAGGTGACGA-1 0.0820203 T cells
donor1_AAACCCTGTGACGAGT-1 0.0984110 CD4+ T cells
donor1_AAACGAATCAGGCTAC-1 0.0642314 CD4+ T cells
donor1_AAACGACAGATTGACT-1 0.1762658 Monocytes
donor1_AAACGATGTCTTGAAC-1 0.0913007 CD4+ T cells
donor1_AAACGATGTGCGCGAA-1 0.1458069 B cellsAdd annotations to metadata.
cbind(
pbmc20k@meta.data,
as.data.frame(pbmc20k.anno)
) -> pbmc20k@meta.dataUMAP with annotations.
DimPlot(pbmc20k, reduction = "umap", group.by = "labels", pt.size = .1, label = TRUE, repel = TRUE)
| Version | Author | Date |
|---|---|---|
| ffad125 | Dave Tang | 2025-02-23 |
Saving and loading
If you save your object and load it in in the future, Seurat will
access the on-disk matrices by their path, which is stored in the assay
level data. To make it easy to ensure these are saved in the same place,
we provide new functionality to the SaveSeuratRds()
function. In this function, you specify your filename. The pointer to
the path in the Seurat object will change to the current directory.
This also makes it easy to share your Seurat objects with BPCells matrices by sharing a folder that contains both the object and the BPCells directory.
Make sure you use a different directory than where the on-disk matrices are stored or they will be recursively copied.
output_dir <- "data/pbmc20k_seurat"
if(!dir.exists(output_dir)){
dir.create(output_dir)
}
SaveSeuratRds(
object = pbmc20k,
file = paste0(output_dir, "/pbmc20k.rds")
)Warning: Trying to move '/home/rstudio/muse/data/pbmc20k' to itself, skipping
Trying to move '/home/rstudio/muse/data/pbmc20k' to itself, skipping
Trying to move '/home/rstudio/muse/data/pbmc20k' to itself, skippinglist.files(output_dir)[1] "pbmc20k" "pbmc20k.rds"list.files(paste0(output_dir, "/pbmc20k")) [1] "col_names" "idxptr" "index_data"
[4] "index_idx" "index_idx_offsets" "index_starts"
[7] "row_names" "shape" "storage_order"
[10] "val_data" "val_idx" "val_idx_offsets"
[13] "version" Need to use LoadSeuratRds() to load or else none of the
layers will be imported.
pbmc20k_import <- LoadSeuratRds(paste0(output_dir, '/pbmc20k.rds'))
pbmc20k_importAn object of class Seurat
27385 features across 22061 samples within 1 assay
Active assay: RNA (27385 features, 2000 variable features)
3 layers present: counts, data, scale.data
3 dimensional reductions calculated: pca, harmony, umappbmc20k_import@assays$RNA$counts27385 x 22061 IterableMatrix object with class RenameDims
Row names: ENSG00000238009, ENSG00000239945 ... AMELY
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1
Data type: uint32_t
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20k
2. Reset dimnames
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] celldex_1.16.0 SingleR_2.8.0
[3] SummarizedExperiment_1.36.0 Biobase_2.66.0
[5] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[7] IRanges_2.40.1 S4Vectors_0.44.0
[9] BiocGenerics_0.52.0 MatrixGenerics_1.18.1
[11] matrixStats_1.4.1 BPCells_0.3.0
[13] Seurat_5.1.0 SeuratObject_5.0.2
[15] sp_2.1-4 harmony_1.2.1
[17] Rcpp_1.0.13 patchwork_1.3.0
[19] lubridate_1.9.3 forcats_1.0.0
[21] stringr_1.5.1 dplyr_1.1.4
[23] purrr_1.0.2 readr_2.1.5
[25] tidyr_1.3.1 tibble_3.2.1
[27] ggplot2_3.5.1 tidyverse_2.0.0
[29] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.4 spatstat.sparse_3.1-0
[3] httr_1.4.7 RColorBrewer_1.1-3
[5] tools_4.4.1 sctransform_0.4.1
[7] alabaster.base_1.6.1 utf8_1.2.4
[9] R6_2.5.1 HDF5Array_1.34.0
[11] lazyeval_0.2.2 uwot_0.2.2
[13] rhdf5filters_1.18.0 withr_3.0.2
[15] gridExtra_2.3 progressr_0.15.0
[17] cli_3.6.3 spatstat.explore_3.3-3
[19] fastDummies_1.7.4 labeling_0.4.3
[21] alabaster.se_1.6.0 sass_0.4.9
[23] spatstat.data_3.1-2 ggridges_0.5.6
[25] pbapply_1.7-2 parallelly_1.38.0
[27] rstudioapi_0.17.1 RSQLite_2.3.7
[29] generics_0.1.3 ica_1.0-3
[31] spatstat.random_3.3-2 Matrix_1.7-0
[33] fansi_1.0.6 abind_1.4-8
[35] lifecycle_1.0.4 whisker_0.4.1
[37] yaml_2.3.10 rhdf5_2.50.2
[39] SparseArray_1.6.1 BiocFileCache_2.14.0
[41] Rtsne_0.17 grid_4.4.1
[43] blob_1.2.4 promises_1.3.0
[45] ExperimentHub_2.14.0 crayon_1.5.3
[47] miniUI_0.1.1.1 lattice_0.22-6
[49] beachmat_2.22.0 cowplot_1.1.3
[51] KEGGREST_1.46.0 pillar_1.9.0
[53] knitr_1.48 future.apply_1.11.3
[55] codetools_0.2-20 leiden_0.4.3.1
[57] glue_1.8.0 getPass_0.2-4
[59] spatstat.univar_3.0-1 data.table_1.16.2
[61] vctrs_0.6.5 png_0.1-8
[63] gypsum_1.2.0 spam_2.11-0
[65] gtable_0.3.6 cachem_1.1.0
[67] xfun_0.48 S4Arrays_1.6.0
[69] mime_0.12 survival_3.6-4
[71] fitdistrplus_1.2-1 ROCR_1.0-11
[73] nlme_3.1-164 bit64_4.5.2
[75] alabaster.ranges_1.6.0 filelock_1.0.3
[77] RcppAnnoy_0.0.22 rprojroot_2.0.4
[79] bslib_0.8.0 irlba_2.3.5.1
[81] KernSmooth_2.23-24 colorspace_2.1-1
[83] DBI_1.2.3 tidyselect_1.2.1
[85] processx_3.8.4 bit_4.5.0
[87] compiler_4.4.1 curl_5.2.3
[89] git2r_0.35.0 httr2_1.0.5
[91] BiocNeighbors_2.0.1 hdf5r_1.3.11
[93] DelayedArray_0.32.0 plotly_4.10.4
[95] scales_1.3.0 lmtest_0.9-40
[97] callr_3.7.6 rappdirs_0.3.3
[99] digest_0.6.37 goftest_1.2-3
[101] spatstat.utils_3.1-0 alabaster.matrix_1.6.1
[103] rmarkdown_2.28 RhpcBLASctl_0.23-42
[105] XVector_0.46.0 htmltools_0.5.8.1
[107] pkgconfig_2.0.3 sparseMatrixStats_1.18.0
[109] highr_0.11 dbplyr_2.5.0
[111] fastmap_1.2.0 rlang_1.1.4
[113] htmlwidgets_1.6.4 UCSC.utils_1.2.0
[115] shiny_1.9.1 DelayedMatrixStats_1.28.1
[117] farver_2.1.2 jquerylib_0.1.4
[119] zoo_1.8-12 jsonlite_1.8.9
[121] BiocParallel_1.40.0 BiocSingular_1.22.0
[123] magrittr_2.0.3 GenomeInfoDbData_1.2.13
[125] dotCall64_1.2 Rhdf5lib_1.28.0
[127] munsell_0.5.1 reticulate_1.39.0
[129] stringi_1.8.4 alabaster.schemas_1.6.0
[131] zlibbioc_1.52.0 MASS_7.3-60.2
[133] AnnotationHub_3.14.0 plyr_1.8.9
[135] parallel_4.4.1 listenv_0.9.1
[137] ggrepel_0.9.6 deldir_2.0-4
[139] Biostrings_2.74.1 splines_4.4.1
[141] tensor_1.5 hms_1.1.3
[143] ps_1.8.1 igraph_2.1.1
[145] spatstat.geom_3.3-3 RcppHNSW_0.6.0
[147] reshape2_1.4.4 ScaledMatrix_1.14.0
[149] BiocVersion_3.20.0 evaluate_1.0.1
[151] BiocManager_1.30.25 tzdb_0.4.0
[153] httpuv_1.6.15 RANN_2.6.2
[155] polyclip_1.10-7 future_1.34.0
[157] scattermore_1.2 rsvd_1.0.5
[159] xtable_1.8-4 RSpectra_0.16-2
[161] later_1.3.2 viridisLite_0.4.2
[163] memoise_2.0.1 AnnotationDbi_1.68.0
[165] cluster_2.1.6 timechange_0.3.0
[167] globals_0.16.3