Reference-Based Single-Cell RNA-Seq Annotation

Installation

Install SingleR.

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SingleR")
BiocManager::install("scRNAseq")
BiocManager::install("scuttle")
BiocManager::install("scran")
install.packages("viridis")
install.packages("pheatmap")

Vignette

Following Using SingleR to annotate single-cell RNA-seq data.

SingleR is an automatic annotation method for single-cell RNA sequencing (scRNAseq) data (Aran et al. 2019). Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference. Thus, the burden of manually interpreting clusters and defining marker genes only has to be done once, for the reference dataset, and this biological knowledge can be propagated to new datasets in an automated manner.

The easiest way to use SingleR is to annotate cells against built-in references. In particular, the celldex package provides access to several reference datasets (mostly derived from bulk RNA-seq or microarray data) through dedicated retrieval functions. Here, we will use the Human Primary Cell Atlas (Mabbott et al. 2013), represented as a SummarizedExperiment object containing a matrix of log-expression values with sample-level labels.

suppressPackageStartupMessages(library(celldex))
hpca.se <- HumanPrimaryCellAtlasData()
hpca.se

class: SummarizedExperiment 
dim: 19363 713 
metadata(0):
assays(1): logcounts
rownames(19363): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
rowData names(0):
colnames(713): GSM112490 GSM112491 ... GSM92233 GSM92234
colData names(3): label.main label.fine label.ont

Human Embryonic Stem Cells

Our test dataset consists of some human embryonic stem cells (La Manno et al. 2016) from the scRNAseq package. For the sake of speed, we will only label the first 100 cells from this dataset.

suppressPackageStartupMessages(library(scRNAseq))
hESCs <- LaMannoBrainData('human-es')
hESCs <- hESCs[,1:100]

We use our hpca.se reference to annotate each cell in hESCs via the SingleR() function. This identifies marker genes from the reference and uses them to compute assignment scores (based on the Spearman correlation across markers) for each cell in the test dataset against each label in the reference. The label with the highest score is the assigned to the test cell, possibly with further fine-tuning to resolve closely related labels.

suppressPackageStartupMessages(library(SingleR))
pred.hesc <- SingleR(
  test = hESCs,
  ref = hpca.se,
  assay.type.test=1,
  labels = hpca.se$label.main
)

Each row of the output DataFrame contains prediction results for a single cell. Labels are shown before (labels) and after pruning (pruned.labels), along with the associated scores.

pred.hesc

DataFrame with 100 rows and 4 columns
                                        scores               labels delta.next
                                      <matrix>          <character>  <numeric>
1772122_301_C02 0.347652:0.139036:0.109547:... Neuroepithelial_cell 0.08332864
1772122_180_E05 0.361187:0.155395:0.134934:...              Neurons 0.07283500
1772122_300_H02 0.446411:0.218052:0.190084:... Neuroepithelial_cell 0.13882912
1772122_180_B09 0.373512:0.172438:0.143537:... Neuroepithelial_cell 0.00317443
1772122_180_G04 0.357341:0.157275:0.126511:... Neuroepithelial_cell 0.09717938
...                                        ...                  ...        ...
1772122_299_E07 0.371989:0.202363:0.169379:... Neuroepithelial_cell  0.0837521
1772122_180_D02 0.353314:0.146049:0.115864:... Neuroepithelial_cell  0.0842804
1772122_300_D09 0.348789:0.129193:0.136732:... Neuroepithelial_cell  0.0595056
1772122_298_F09 0.332361:0.173357:0.141439:... Neuroepithelial_cell  0.1200606
1772122_302_A11 0.324928:0.127518:0.101609:...            Astrocyte  0.0509478
                       pruned.labels
                         <character>
1772122_301_C02 Neuroepithelial_cell
1772122_180_E05              Neurons
1772122_300_H02 Neuroepithelial_cell
1772122_180_B09 Neuroepithelial_cell
1772122_180_G04 Neuroepithelial_cell
...                              ...
1772122_299_E07 Neuroepithelial_cell
1772122_180_D02 Neuroepithelial_cell
1772122_300_D09 Neuroepithelial_cell
1772122_298_F09 Neuroepithelial_cell
1772122_302_A11            Astrocyte

SingleR is workflow/package agnostic. The above example uses SummarizedExperiment objects, but the same functions will accept any (log-)normalized expression matrix.

Using single-cell references

Here, we will use two human pancreas datasets from the scRNAseq package. The aim is to use one pre-labelled dataset to annotate the other unlabelled dataset. First, we set up the Muraro et al. (2016) dataset to be our reference.

suppressPackageStartupMessages(library(scuttle))
sceM <- MuraroPancreasData()

# One should normally do cell-based quality control at this point, but for
# brevity's sake, we will just remove the unlabelled libraries here.
sceM <- sceM[,!is.na(sceM$label)]

# SingleR() expects reference datasets to be normalized and log-transformed.
sceM <- logNormCounts(sceM)
sceM

class: SingleCellExperiment 
dim: 19059 2126 
metadata(0):
assays(2): counts logcounts
rownames(19059): A1BG-AS1__chr19 A1BG__chr19 ... ZZEF1__chr17
  ZZZ3__chr1
rowData names(2): symbol chr
colnames(2126): D28-1_1 D28-1_2 ... D30-8_93 D30-8_94
colData names(4): label donor plate sizeFactor
reducedDimNames(0):
mainExpName: endogenous
altExpNames(1): ERCC

Label tally.

table(colData(sceM)$label)

     acinar       alpha        beta       delta        duct endothelial 
        219         812         448         193         245          21 
    epsilon mesenchymal          pp     unclear 
          3          80         101           4 

We then set up our test dataset from Grun et al. (2016).

sceG <- GrunPancreasData()
sceG <- sceG[,colSums(counts(sceG)) > 0] # Remove libraries with no counts.
sceG <- logNormCounts(sceG) 
sceG

class: SingleCellExperiment 
dim: 20064 1718 
metadata(0):
assays(2): counts logcounts
rownames(20064): A1BG-AS1__chr19 A1BG__chr19 ... ZZEF1__chr17
  ZZZ3__chr1
rowData names(2): symbol chr
colnames(1718): D2ex_1 D2ex_2 ... D17TGFB_95 D17TGFB_96
colData names(3): donor sample sizeFactor
reducedDimNames(0):
mainExpName: endogenous
altExpNames(1): ERCC

We then run SingleR() as described previously but with a marker detection mode that considers the variance of expression across cells. Here, we will use the Wilcoxon ranked sum test to identify the top markers for each pairwise comparison between labels. This is slower but more appropriate for single-cell data compared to the default marker detection algorithm (which may fail for low-coverage data where the median is frequently zero).

pred.grun <- SingleR(
  test=sceG,
  ref=sceM,
  labels=sceM$label,
  de.method="wilcox"
)

table(pred.grun$labels)

     acinar       alpha        beta       delta        duct endothelial 
        657         245         276          57         367          34 
    epsilon mesenchymal          pp     unclear 
          1          41          35           5 

Annotation diagnostics

plotScoreHeatmap() displays the scores for all cells across all reference labels, which allows users to inspect the confidence of the predicted labels across the dataset. Ideally, each cell (i.e., column of the heatmap) should have one score that is obviously larger than the rest, indicating that it is unambiguously assigned to a single label. A spread of similar scores for a given cell indicates that the assignment is uncertain, though this may be acceptable if the uncertainty is distributed across similar cell types that cannot be easily resolved.

plotScoreHeatmap(pred.grun)

Past versions of plot_score_heatmap-1.png

Another diagnostic is based on the per-cell “deltas”, i.e., the difference between the score for the assigned label and the median across all labels for each cell. Low deltas indicate that the assignment is uncertain, which is especially relevant if the cell’s true label does not exist in the reference. We can inspect these deltas across cells for each label using the plotDeltaDistribution() function.

plotDeltaDistribution(pred.grun, ncol = 3)

Warning: Groups with fewer than two datapoints have been dropped.
ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.

Warning in max(data$density, na.rm = TRUE): no non-missing arguments to max;
returning -Inf

Warning: Computation failed in `stat_ydensity()`.
Caused by error in `$<-.data.frame`:
! replacement has 1 row, data has 0

Past versions of plot_delta_dist-1.png

The pruneScores() function will remove potentially poor-quality or ambiguous assignments based on the deltas. The minimum threshold on the deltas is defined using an outlier-based approach that accounts for differences in the scale of the correlations in various contexts - see ?pruneScores for more details. SingleR() will also report the pruned scores automatically in the pruned.labels field where low-quality assignments are replaced with NA.

summary(is.na(pred.grun$pruned.labels))

   Mode   FALSE    TRUE 
logical    1651      67 

Finally, a simple yet effective diagnostic is to examine the expression of the marker genes for each label in the test dataset. We extract the identity of the markers from the metadata of the SingleR() results and use them in the plotMarkerHeatmap() function, as shown below for beta cell markers. If a cell in the test dataset is confidently assigned to a particular label, we would expect it to have strong expression of that label’s markers. At the very least, it should exhibit upregulation of those markers relative to cells assigned to other labels.

plotMarkerHeatmap(pred.grun, sceG, label="beta")

Past versions of plot_marker_heatmap-1.png

pbmc3k

pbmc3k.

suppressPackageStartupMessages(library(Seurat))
pbmc.data <- Read10X(data.dir = "data/pbmc3k/filtered_gene_bc_matrices/hg19/")

pbmc3k <- CreateSeuratObject(
  counts = pbmc.data,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

pbmc3k <- NormalizeData(pbmc3k)

Normalizing layer: counts

Use Monaco.

suppressPackageStartupMessages(library(celldex))
monaco_immune <- fetchReference("monaco_immune", "2024-02-26")
monaco_immune

class: 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.ont

Annotate.

pbmc3k.anno <- SingleR(
  test=pbmc3k@assays$RNA$data,
  ref=monaco_immune,
  labels=colData(monaco_immune)$label.main
)

cbind(
  pbmc3k@meta.data,
  as.data.frame(pbmc3k.anno)
) -> pbmc3k@meta.data

head(pbmc3k@meta.data)

                 orig.ident nCount_RNA nFeature_RNA scores.B.cells
AAACATACAACCAC-1     pbmc3k       2419          779      0.2180441
AAACATTGAGCTAC-1     pbmc3k       4903         1352      0.3757485
AAACATTGATCAGC-1     pbmc3k       3147         1129      0.2021054
AAACCGTGCTTCCG-1     pbmc3k       2639          960      0.2612174
AAACCGTGTATGCG-1     pbmc3k        980          521      0.1354756
AAACGCACTGGTAC-1     pbmc3k       2163          781      0.2428544
                 scores.Basophils scores.CD4..T.cells scores.CD8..T.cells
AAACATACAACCAC-1        0.1383833           0.3191626           0.3271229
AAACATTGAGCTAC-1        0.1595869           0.2226758           0.2314824
AAACATTGATCAGC-1        0.1398993           0.3603814           0.3381419
AAACCGTGCTTCCG-1        0.1925995           0.1409225           0.1433708
AAACCGTGTATGCG-1        0.1062113           0.1834073           0.2328461
AAACGCACTGGTAC-1        0.1773750           0.3236777           0.3062542
                 scores.Dendritic.cells scores.Monocytes scores.Neutrophils
AAACATACAACCAC-1              0.1813302        0.1823273         0.10993201
AAACATTGAGCTAC-1              0.2636019        0.2165097         0.12052022
AAACATTGATCAGC-1              0.1654294        0.1732526         0.12294480
AAACCGTGCTTCCG-1              0.3438019        0.3694793         0.23698599
AAACCGTGTATGCG-1              0.1082860        0.1210741         0.07931607
AAACGCACTGGTAC-1              0.2057659        0.2115375         0.10881833
                 scores.NK.cells scores.Progenitors scores.T.cells       labels
AAACATACAACCAC-1       0.2818949          0.2027839      0.3311532      T cells
AAACATTGAGCTAC-1       0.2137517          0.2441976      0.2270059      B cells
AAACATTGATCAGC-1       0.2701806          0.1943829      0.3374040 CD4+ T cells
AAACCGTGCTTCCG-1       0.1803712          0.2258147      0.1466077    Monocytes
AAACCGTGTATGCG-1       0.2725055          0.1577020      0.2524348     NK cells
AAACGCACTGGTAC-1       0.2690438          0.2098472      0.3002346 CD4+ T cells
                   delta.next pruned.labels
AAACATACAACCAC-1 0.0009944445       T cells
AAACATTGAGCTAC-1 0.1121465835       B cells
AAACATTGATCAGC-1 0.0850874976  CD4+ T cells
AAACCGTGCTTCCG-1 0.1135955003     Monocytes
AAACCGTGTATGCG-1 0.0623907247      NK cells
AAACGCACTGGTAC-1 0.0574108543  CD4+ T cells

total <- nrow(pbmc3k@meta.data)
half <- floor(total / 2)
first_half <- row.names(pbmc3k@meta.data)[1:half]
second_half <- row.names(pbmc3k@meta.data)[(half+1):total]

sce_ref <- SingleCellExperiment(
  assays = list(counts = pbmc3k@assays$RNA$counts[, first_half])
)
colLabels(sce_ref) <- pbmc3k@meta.data[first_half, 'labels']
sce_ref <- logNormCounts(sce_ref)
sce_ref

class: SingleCellExperiment 
dim: 13714 1350 
metadata(0):
assays(2): counts logcounts
rownames(13714): AL627309.1 AP006222.2 ... PNRC2.1 SRSF10.1
rowData names(0):
colnames(1350): AAACATACAACCAC-1 AAACATTGAGCTAC-1 ... CTATAGCTTCGCTC-1
  CTATAGCTTGCCTC-1
colData names(2): label sizeFactor
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

sce_query <- SingleCellExperiment(
  assays = list(counts = pbmc3k@assays$RNA$counts[, second_half])
)
sce_query <- logNormCounts(sce_query)
sce_query

class: SingleCellExperiment 
dim: 13714 1350 
metadata(0):
assays(2): counts logcounts
rownames(13714): AL627309.1 AP006222.2 ... PNRC2.1 SRSF10.1
rowData names(0):
colnames(1350): CTATCAACGAACTC-1 CTATCAACGCAGAG-1 ... TTTGCATGAGAGGC-1
  TTTGCATGCCTCAC-1
colData names(1): sizeFactor
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

sce.pred <- SingleR(
  test=sce_query,
  ref=sce_ref,
  labels=sce_ref$label,
  de.method="wilcox"
)

table(sce_ref$label)

        B cells    CD4+ T cells    CD8+ T cells Dendritic cells       Monocytes 
            179             455             160              23             317 
       NK cells     Progenitors         T cells 
             87               6             123 

table(sce.pred$labels)

        B cells    CD4+ T cells    CD8+ T cells Dendritic cells       Monocytes 
            175             508             126              34             312 
       NK cells     Progenitors         T cells 
             72               9             114 

pbmc3k_ref <- SingleCellExperiment(
  assays = list(counts = pbmc3k@assays$RNA$counts)
)
colLabels(pbmc3k_ref) <- pbmc3k@meta.data$labels
pbmc3k_ref <- logNormCounts(pbmc3k_ref)

pbmc3k_query <- SingleCellExperiment(
  assays = list(counts = pbmc3k@assays$RNA$counts)
)
pbmc3k_query <- logNormCounts(pbmc3k_query)

pbmc3k.self.pred <- SingleR(
  test=pbmc3k_query,
  ref=pbmc3k_ref,
  labels=pbmc3k_ref$label,
  de.method="wilcox"
)

stopifnot(all(row.names(pbmc3k.self.pred) == row.names(pbmc3k@meta.data)))
table(
  pbmc3k@meta.data$labels,
  pbmc3k.self.pred$labels
)

                 
                  B cells CD4+ T cells CD8+ T cells Dendritic cells Monocytes
  B cells             351            1            0               0         3
  CD4+ T cells          1          892           12               0         0
  CD8+ T cells          0           62          171               0         0
  Dendritic cells       1            0            0              31        13
  Monocytes             0            1            0               2       638
  NK cells              0            1            3               0         0
  Progenitors           0            2            0               1         0
  T cells               1           54           17               0         0
                 
                  NK cells Progenitors T cells
  B cells                0           0       0
  CD4+ T cells           0           0       6
  CD8+ T cells           1           0      80
  Dendritic cells        0           0       0
  Monocytes              0           0       0
  NK cells             147           0      14
  Progenitors            0          12       0
  T cells                2           0     180

sum(pbmc3k@meta.data$labels == pbmc3k.self.pred$labels) / length(pbmc3k.self.pred$labels)

[1] 0.897037

plotScoreHeatmap(pbmc3k.self.pred, show.pruned = TRUE)

table((pbmc3k@meta.data$labels == pbmc3k.self.pred$labels)[pruneScores(pbmc3k.self.pred)])

FALSE  TRUE 
   17    42 

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] Seurat_5.1.0                SeuratObject_5.0.2         
 [3] sp_2.1-4                    scuttle_1.16.0             
 [5] SingleR_2.8.0               scRNAseq_2.20.0            
 [7] SingleCellExperiment_1.28.1 celldex_1.16.0             
 [9] SummarizedExperiment_1.36.0 Biobase_2.66.0             
[11] GenomicRanges_1.58.0        GenomeInfoDb_1.42.3        
[13] IRanges_2.40.1              S4Vectors_0.44.0           
[15] BiocGenerics_0.52.0         MatrixGenerics_1.18.1      
[17] matrixStats_1.4.1           workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] spatstat.sparse_3.1-0     fs_1.6.4                 
  [3] ProtGenerics_1.38.0       bitops_1.0-9             
  [5] httr_1.4.7                RColorBrewer_1.1-3       
  [7] sctransform_0.4.1         tools_4.4.1              
  [9] alabaster.base_1.6.1      utf8_1.2.4               
 [11] R6_2.5.1                  HDF5Array_1.34.0         
 [13] uwot_0.2.2                lazyeval_0.2.2           
 [15] rhdf5filters_1.18.0       withr_3.0.2              
 [17] gridExtra_2.3             progressr_0.15.0         
 [19] cli_3.6.3                 spatstat.explore_3.3-3   
 [21] fastDummies_1.7.4         alabaster.se_1.6.0       
 [23] labeling_0.4.3            sass_0.4.9               
 [25] spatstat.data_3.1-2       ggridges_0.5.6           
 [27] pbapply_1.7-2             Rsamtools_2.22.0         
 [29] R.utils_2.12.3            parallelly_1.38.0        
 [31] limma_3.62.2              rstudioapi_0.17.1        
 [33] RSQLite_2.3.7             generics_0.1.3           
 [35] BiocIO_1.16.0             spatstat.random_3.3-2    
 [37] ica_1.0-3                 dplyr_1.1.4              
 [39] Matrix_1.7-0              fansi_1.0.6              
 [41] abind_1.4-8               R.methodsS3_1.8.2        
 [43] lifecycle_1.0.4           whisker_0.4.1            
 [45] yaml_2.3.10               edgeR_4.4.2              
 [47] rhdf5_2.50.2              SparseArray_1.6.1        
 [49] BiocFileCache_2.14.0      Rtsne_0.17               
 [51] grid_4.4.1                blob_1.2.4               
 [53] promises_1.3.0            dqrng_0.4.1              
 [55] ExperimentHub_2.14.0      crayon_1.5.3             
 [57] miniUI_0.1.1.1            lattice_0.22-6           
 [59] beachmat_2.22.0           cowplot_1.1.3            
 [61] GenomicFeatures_1.58.0    KEGGREST_1.46.0          
 [63] pillar_1.9.0              knitr_1.48               
 [65] metapod_1.14.0            rjson_0.2.23             
 [67] future.apply_1.11.3       codetools_0.2-20         
 [69] leiden_0.4.3.1            glue_1.8.0               
 [71] getPass_0.2-4             spatstat.univar_3.0-1    
 [73] data.table_1.16.2         vctrs_0.6.5              
 [75] png_0.1-8                 gypsum_1.2.0             
 [77] spam_2.11-0               gtable_0.3.6             
 [79] cachem_1.1.0              xfun_0.48                
 [81] S4Arrays_1.6.0            mime_0.12                
 [83] survival_3.6-4            pheatmap_1.0.12          
 [85] statmod_1.5.0             bluster_1.16.0           
 [87] fitdistrplus_1.2-1        ROCR_1.0-11              
 [89] nlme_3.1-164              bit64_4.5.2              
 [91] alabaster.ranges_1.6.0    filelock_1.0.3           
 [93] RcppAnnoy_0.0.22          rprojroot_2.0.4          
 [95] bslib_0.8.0               irlba_2.3.5.1            
 [97] KernSmooth_2.23-24        colorspace_2.1-1         
 [99] DBI_1.2.3                 tidyselect_1.2.1         
[101] processx_3.8.4            bit_4.5.0                
[103] compiler_4.4.1            curl_5.2.3               
[105] git2r_0.35.0              httr2_1.0.5              
[107] BiocNeighbors_2.0.1       DelayedArray_0.32.0      
[109] plotly_4.10.4             rtracklayer_1.66.0       
[111] scales_1.3.0              lmtest_0.9-40            
[113] callr_3.7.6               rappdirs_0.3.3           
[115] goftest_1.2-3             stringr_1.5.1            
[117] digest_0.6.37             spatstat.utils_3.1-0     
[119] alabaster.matrix_1.6.1    rmarkdown_2.28           
[121] XVector_0.46.0            htmltools_0.5.8.1        
[123] pkgconfig_2.0.3           sparseMatrixStats_1.18.0 
[125] highr_0.11                dbplyr_2.5.0             
[127] fastmap_1.2.0             ensembldb_2.30.0         
[129] htmlwidgets_1.6.4         rlang_1.1.4              
[131] UCSC.utils_1.2.0          shiny_1.9.1              
[133] DelayedMatrixStats_1.28.1 farver_2.1.2             
[135] jquerylib_0.1.4           zoo_1.8-12               
[137] jsonlite_1.8.9            BiocParallel_1.40.0      
[139] R.oo_1.26.0               BiocSingular_1.22.0      
[141] RCurl_1.98-1.16           magrittr_2.0.3           
[143] GenomeInfoDbData_1.2.13   dotCall64_1.2            
[145] patchwork_1.3.0           Rhdf5lib_1.28.0          
[147] munsell_0.5.1             Rcpp_1.0.13              
[149] viridis_0.6.5             reticulate_1.39.0        
[151] stringi_1.8.4             alabaster.schemas_1.6.0  
[153] zlibbioc_1.52.0           MASS_7.3-60.2            
[155] plyr_1.8.9                AnnotationHub_3.14.0     
[157] parallel_4.4.1            listenv_0.9.1            
[159] ggrepel_0.9.6             deldir_2.0-4             
[161] Biostrings_2.74.1         splines_4.4.1            
[163] tensor_1.5                locfit_1.5-9.10          
[165] ps_1.8.1                  igraph_2.1.1             
[167] spatstat.geom_3.3-3       RcppHNSW_0.6.0           
[169] reshape2_1.4.4            ScaledMatrix_1.14.0      
[171] BiocVersion_3.20.0        XML_3.99-0.17            
[173] evaluate_1.0.1            scran_1.34.0             
[175] BiocManager_1.30.25       httpuv_1.6.15            
[177] polyclip_1.10-7           tidyr_1.3.1              
[179] purrr_1.0.2               RANN_2.6.2               
[181] scattermore_1.2           future_1.34.0            
[183] alabaster.sce_1.6.0       ggplot2_3.5.1            
[185] rsvd_1.0.5                xtable_1.8-4             
[187] restfulr_0.0.15           AnnotationFilter_1.30.0  
[189] RSpectra_0.16-2           later_1.3.2              
[191] viridisLite_0.4.2         tibble_3.2.1             
[193] memoise_2.0.1             AnnotationDbi_1.68.0     
[195] GenomicAlignments_1.42.0  cluster_2.1.6            
[197] globals_0.16.3