Celltype prediction

Bioconductor Toolkit

Assignment of cell identities based on gene expression patterns using reference data.
Authors

Åsa Björklund

Paulo Czarnewski

Susanne Reinsbach

Roy Francis

Published

28-Jan-2025

Note

Code chunks run R commands unless otherwise specified.

Celltype prediction can either be performed on indiviudal cells where each cell gets a predicted celltype label, or on the level of clusters. All methods are based on similarity to other datasets, single cell or sorted bulk RNAseq, or uses known marker genes for each cell type.
Ideally celltype predictions should be run on each sample separately and not using the integrated data. In this case we will select one sample from the Covid data, ctrl_13 and predict celltype by cell on that sample.
Some methods will predict a celltype to each cell based on what it is most similar to, even if that celltype is not included in the reference. Other methods include an uncertainty so that cells with low similarity scores will be unclassified.
There are multiple different methods to predict celltypes, here we will just cover a few of those.

We will use a reference PBMC dataset from the scPred package which is provided as a Seurat object with counts. And we will test classification based on the scPred and scMap methods. Finally we will use gene set enrichment predict celltype based on the DEGs of each cluster.

1 Read data

First, lets load required libraries

suppressPackageStartupMessages({
    library(scater)
    library(scran)
    library(dplyr)
    library(patchwork)
    library(ggplot2)
    library(pheatmap)
    library(scPred)
    library(scmap)
    library(SingleR)
})

Let’s read in the saved Covid-19 data object from the clustering step.

# download pre-computed data if missing or long compute
fetch_data <- TRUE

# url for source and intermediate data
path_data <- "https://nextcloud.dc.scilifelab.se/public.php/webdav"
curl_upass <- "-u zbC5fr2LbEZ9rSE:scRNAseq2025"

path_file <- "data/covid/results/bioc_covid_qc_dr_int_cl.rds"
if (!dir.exists(dirname(path_file))) dir.create(dirname(path_file), recursive = TRUE)
if (fetch_data && !file.exists(path_file)) download.file(url = file.path(path_data, "covid/results_bioc/bioc_covid_qc_dr_int_cl.rds"), destfile = path_file, method = "curl", extra = curl_upass)

alldata <- readRDS(path_file)

Let’s read in the saved Covid-19 data object from the clustering step.

ctrl.sce <- alldata[, alldata$sample == "ctrl.13"]

# remove all old dimensionality reductions as they will mess up the analysis further down
reducedDims(ctrl.sce) <- NULL

2 Reference data

Load the reference dataset with annotated labels that is provided by the scPred package, it is a subsampled set of cells from human PBMCs.

reference <- scPred::pbmc_1
reference
An object of class Seurat 
32838 features across 3500 samples within 1 assay 
Active assay: RNA (32838 features, 0 variable features)
 2 layers present: counts, data

Convert to a SCE object.

ref.sce <- Seurat::as.SingleCellExperiment(reference)

Rerun analysis pipeline. Run normalization, feature selection and dimensionality reduction

# Normalize
ref.sce <- computeSumFactors(ref.sce)
ref.sce <- logNormCounts(ref.sce)

# Variable genes
var.out <- modelGeneVar(ref.sce, method = "loess")
hvg.ref <- getTopHVGs(var.out, n = 1000)

# Dim reduction
ref.sce <- runPCA(ref.sce,
    exprs_values = "logcounts", scale = T,
    ncomponents = 30, subset_row = hvg.ref
)
ref.sce <- runUMAP(ref.sce, dimred = "PCA")
plotReducedDim(ref.sce, dimred = "UMAP", colour_by = "cell_type")

Run all steps of the analysis for the ctrl sample as well. Use the clustering from the integration lab with resolution 0.5.

# Normalize
ctrl.sce <- computeSumFactors(ctrl.sce)
ctrl.sce <- logNormCounts(ctrl.sce)

# Variable genes
var.out <- modelGeneVar(ctrl.sce, method = "loess")
hvg.ctrl <- getTopHVGs(var.out, n = 1000)

# Dim reduction
ctrl.sce <- runPCA(ctrl.sce, exprs_values = "logcounts", scale = T, ncomponents = 30, subset_row = hvg.ctrl)
ctrl.sce <- runUMAP(ctrl.sce, dimred = "PCA")
plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "leiden_k20")

3 scMap

The scMap package is one method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. It can be run on different levels, either projecting by cluster or by single cell, here we will try out both.

For scmap cell type labels must be stored in the cell_type1 column of the colData slots, and gene ids that are consistent across both datasets must be stored in the feature_symbol column of the rowData slots.

3.1 scMap cluster

# add in slot cell_type1
ref.sce$cell_type1 <- ref.sce$cell_type
# create a rowData slot with feature_symbol
rd <- data.frame(feature_symbol = rownames(ref.sce))
rownames(rd) <- rownames(ref.sce)
rowData(ref.sce) <- rd

# same for the ctrl dataset
# create a rowData slot with feature_symbol
rd <- data.frame(feature_symbol = rownames(ctrl.sce))
rownames(rd) <- rownames(ctrl.sce)
rowData(ctrl.sce) <- rd

Then we can select variable features in both datasets.

# select features
counts(ctrl.sce) <- as.matrix(counts(ctrl.sce))
logcounts(ctrl.sce) <- as.matrix(logcounts(ctrl.sce))
ctrl.sce <- selectFeatures(ctrl.sce, suppress_plot = TRUE)

counts(ref.sce) <- as.matrix(counts(ref.sce))
logcounts(ref.sce) <- as.matrix(logcounts(ref.sce))
ref.sce <- selectFeatures(ref.sce, suppress_plot = TRUE)

Then we need to index the reference dataset by cluster, default is the clusters in cell_type1.

ref.sce <- indexCluster(ref.sce)

Now we project the Covid-19 dataset onto that index.

project_cluster <- scmapCluster(
    projection = ctrl.sce,
    index_list = list(
        ref = metadata(ref.sce)$scmap_cluster_index
    )
)

# projected labels
table(project_cluster$scmap_cluster_labs)

     B cell  CD4 T cell  CD8 T cell         cDC       cMono      ncMono 
         74         109         113          31         203         138 
    NK cell         pDC Plasma cell  unassigned 
        279           2           1         152

Then add the predictions to metadata and plot UMAP.

# add in predictions
ctrl.sce$scmap_cluster <- project_cluster$scmap_cluster_labs

plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cluster")

4 scMap cell

We can instead index the refernce data based on each single cell and project our data onto the closest neighbor in that dataset.

ref.sce <- indexCell(ref.sce)

Again we need to index the reference dataset.

project_cell <- scmapCell(
    projection = ctrl.sce,
    index_list = list(
        ref = metadata(ref.sce)$scmap_cell_index
    )
)

We now get a table with index for the 5 nearest neigbors in the reference dataset for each cell in our dataset. We will select the celltype of the closest neighbor and assign it to the data.

cell_type_pred <- colData(ref.sce)$cell_type1[project_cell$ref[[1]][1, ]]
table(cell_type_pred)
cell_type_pred
     B cell  CD4 T cell  CD8 T cell         cDC       cMono      ncMono 
         97         181         249          49         170         173 
    NK cell         pDC Plasma cell 
        180           2           1

Then add the predictions to metadata and plot umap.

# add in predictions
ctrl.sce$scmap_cell <- cell_type_pred

plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cell")

Plot both:

wrap_plots(
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cluster"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cell"),
    ncol = 2
)

5 SinlgeR

SingleR is performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

There are multiple datasets included in the celldex package that can be used for celltype prediction, here we will test two different ones, the DatabaseImmuneCellExpressionData and the HumanPrimaryCellAtlasData. In addition we will use the same reference dataset that we used for label transfer above but using SingleR instead.

5.1 Immune cell reference

immune = celldex::DatabaseImmuneCellExpressionData()
singler.immune <- SingleR(test = ctrl.sce, ref = immune, assay.type.test=1,
    labels = immune$label.main)

head(singler.immune)
DataFrame with 6 rows and 4 columns
                                               scores        labels delta.next
                                             <matrix>   <character>  <numeric>
AGGTCATGTGCGAACA-13  0.0679680:0.0760411:0.186694:... T cells, CD4+  0.1375513
CCTATCGGTCCCTCAT-13  0.0027079:0.0960641:0.386088:...      NK cells  0.1490740
TCCTCCCTCGTTCATT-13  0.0361115:0.1067465:0.394579:...      NK cells  0.1220681
TACGGTATCGGATTAC-13 -0.0131813:0.0717678:0.283882:...      NK cells  0.0620657
AATAGAGAGTTCGGTT-13  0.0841091:0.1367749:0.273738:... T cells, CD4+  0.0660296
TGACGCGTCGCAATTG-13 -0.0160540:0.0912025:0.396588:...      NK cells  0.2057787
                    pruned.labels
                      <character>
AGGTCATGTGCGAACA-13 T cells, CD4+
CCTATCGGTCCCTCAT-13      NK cells
TCCTCCCTCGTTCATT-13      NK cells
TACGGTATCGGATTAC-13      NK cells
AATAGAGAGTTCGGTT-13 T cells, CD4+
TGACGCGTCGCAATTG-13      NK cells

5.2 HPCA reference

hpca <- HumanPrimaryCellAtlasData()
singler.hpca <- SingleR(test = ctrl.sce, ref = hpca, assay.type.test=1,
    labels = hpca$label.main)

head(singler.hpca)
DataFrame with 6 rows and 4 columns
                                            scores      labels delta.next
                                          <matrix> <character>  <numeric>
AGGTCATGTGCGAACA-13 0.141378:0.310009:0.275987:...     T_cells  0.4918992
CCTATCGGTCCCTCAT-13 0.145926:0.300045:0.277827:...     NK_cell  0.3241970
TCCTCCCTCGTTCATT-13 0.132119:0.311754:0.274127:...     NK_cell  0.0640608
TACGGTATCGGATTAC-13 0.125120:0.283118:0.250322:...     T_cells  0.1545913
AATAGAGAGTTCGGTT-13 0.191441:0.374422:0.329988:...     T_cells  0.5063484
TGACGCGTCGCAATTG-13 0.121131:0.279338:0.249160:...     NK_cell  0.3018100
                    pruned.labels
                      <character>
AGGTCATGTGCGAACA-13       T_cells
CCTATCGGTCCCTCAT-13       NK_cell
TCCTCCCTCGTTCATT-13       NK_cell
TACGGTATCGGATTAC-13       T_cells
AATAGAGAGTTCGGTT-13       T_cells
TGACGCGTCGCAATTG-13       NK_cell

5.3 With own reference data

singler.ref <- SingleR(test=ctrl.sce, ref=ref.sce, labels=ref.sce$cell_type, de.method="wilcox")
head(singler.ref)
DataFrame with 6 rows and 4 columns
                                            scores      labels delta.next
                                          <matrix> <character>  <numeric>
AGGTCATGTGCGAACA-13 0.741719:0.840093:0.805977:...  CD4 T cell  0.0423204
CCTATCGGTCCCTCAT-13 0.649491:0.736753:0.815987:...     NK cell  0.0451715
TCCTCCCTCGTTCATT-13 0.669603:0.731356:0.823308:...     NK cell  0.0865526
TACGGTATCGGATTAC-13 0.708827:0.776244:0.808044:...  CD8 T cell  0.0905218
AATAGAGAGTTCGGTT-13 0.729010:0.847462:0.816299:...  CD4 T cell  0.0409309
TGACGCGTCGCAATTG-13 0.625165:0.681015:0.794166:...     NK cell  0.0597978
                    pruned.labels
                      <character>
AGGTCATGTGCGAACA-13    CD4 T cell
CCTATCGGTCCCTCAT-13       NK cell
TCCTCCCTCGTTCATT-13       NK cell
TACGGTATCGGATTAC-13    CD8 T cell
AATAGAGAGTTCGGTT-13    CD4 T cell
TGACGCGTCGCAATTG-13       NK cell

Compare results:

ctrl.sce$singler.immune = singler.immune$pruned.labels
ctrl.sce$singler.hpca = singler.hpca$pruned.labels
ctrl.sce$singler.ref = singler.ref$pruned.labels

wrap_plots(
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.immune"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.hpca"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.ref"),
    ncol = 3
)

6 Compare results

Now we will compare the output of the two methods using the convenient function in scPred crossTab() that prints the overlap between two metadata slots.

table(ctrl.sce$scmap_cell, ctrl.sce$singler.hpca)
             
              B_cell Macrophage Monocyte NK_cell Platelets T_cells
  B cell          96          0        0       0         0       0
  CD4 T cell       0          0        0       2         1     177
  CD8 T cell       0          0        0     136         0     105
  cDC              1          0       46       0         0       0
  cMono            0          1      157       0         0       0
  ncMono           0          1      170       0         0       0
  NK cell          0          0        0     168         0       7
  pDC              1          0        0       0         0       0
  Plasma cell      1          0        0       0         0       0

Or plot onto umap:

wrap_plots(
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cluster"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cell"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.immune"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.hpca"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.ref"),
    ncol = 3
)

As you can see, the methods using the same reference all have similar results. While for instance singleR with different references give quite different predictions. This really shows that a relevant reference is the key in having reliable celltype predictions rather than the method used.

7 GSEA with celltype markers

Another option, where celltype can be classified on cluster level is to use gene set enrichment among the DEGs with known markers for different celltypes. Similar to how we did functional enrichment for the DEGs in the differential expression exercise. There are some resources for celltype gene sets that can be used. Such as CellMarker, PanglaoDB or celltype gene sets at MSigDB. We can also look at overlap between DEGs in a reference dataset and the dataset you are analyzing.

7.1 DEG overlap

First, lets extract top DEGs for our Covid-19 dataset and the reference dataset. When we run differential expression for our dataset, we want to report as many genes as possible, hence we set the cutoffs quite lenient.

# run differential expression in our dataset, using clustering at resolution 0.3
DGE_list <- scran::findMarkers(
    x = alldata,
    groups = as.character(alldata$leiden_k20),
    pval.type = "all",
    min.prop = 0
)
# Compute differential gene expression in reference dataset (that has cell annotation)
ref_DGE <- scran::findMarkers(
    x = ref.sce,
    groups = as.character(ref.sce$cell_type),
    pval.type = "all",
    direction = "up"
)

# Identify the top cell marker genes in reference dataset
# select top 50 with hihgest foldchange among top 100 signifcant genes.
ref_list <- lapply(ref_DGE, function(x) {
    x$logFC <- rowSums(as.matrix(x[, grep("logFC", colnames(x))]))
    x %>%
        as.data.frame() %>%
        filter(p.value < 0.01) %>%
        top_n(-100, p.value) %>%
        top_n(50, logFC) %>%
        rownames()
})

unlist(lapply(ref_list, length))
     B cell  CD4 T cell  CD8 T cell         cDC       cMono      ncMono 
         50          50          19          17          50          50 
    NK cell         pDC Plasma cell 
         50          50          24

Now we can run GSEA for the DEGs from our dataset and check for enrichment of top DEGs in the reference dataset.

suppressPackageStartupMessages(library(fgsea))

# run fgsea for each of the clusters in the list
res <- lapply(DGE_list, function(x) {
    x$logFC <- rowSums(as.matrix(x[, grep("logFC", colnames(x))]))
    gene_rank <- setNames(x$logFC, rownames(x))
    fgseaRes <- fgsea(pathways = ref_list, stats = gene_rank, nperm = 10000)
    return(fgseaRes)
})
names(res) <- names(DGE_list)

# You can filter and resort the table based on ES, NES or pvalue
res <- lapply(res, function(x) {
    x[x$pval < 0.1, ]
})
res <- lapply(res, function(x) {
    x[x$size > 2, ]
})
res <- lapply(res, function(x) {
    x[order(x$NES, decreasing = T), ]
})
res
$`1`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:       cMono 0.0051546392 0.0067669173  0.9222327  2.294590            0
2:      ncMono 0.0052631579 0.0067669173  0.6362900  1.573839            0
3:      B cell 0.0005097879 0.0011470228 -0.7928592 -1.374113            4
4:  CD8 T cell 0.0047966632 0.0067669173 -0.8495939 -1.386710           45
5: Plasma cell 0.0004142931 0.0011470228 -0.8672090 -1.445820            3
6:     NK cell 0.0001019160 0.0004586221 -0.8480240 -1.473063            0
7:  CD4 T cell 0.0001018537 0.0004586221 -0.9096249 -1.582175            0
    size  leadingEdge
   <int>       <list>
1:    47 S100A8, ....
2:    49 AIF1, S1....
3:    47 RPS5, RP....
4:    18 CCL5, IL....
5:    24 RPL36AL,....
6:    49 B2M, NKG....
7:    50 RPL3, RP....

$`10`
      pathway         pval         padj         ES       NES nMoreExtreme  size
       <char>        <num>        <num>      <num>     <num>        <num> <int>
1:     B cell 0.0095038015 0.0171068427 -0.7417620 -1.227837           94    47
2: CD8 T cell 0.0339512392 0.0436515932 -0.7979091 -1.271178          336    18
3:      cMono 0.0021008403 0.0047268908 -0.7724947 -1.278708           20    47
4:        cDC 0.0309475806 0.0436515932 -0.8059082 -1.279949          306    17
5:    NK cell 0.0002001001 0.0006003002 -0.8092281 -1.340929            1    49
6:     ncMono 0.0001000500 0.0004502251 -0.8579812 -1.421715            0    49
7: CD4 T cell 0.0001000400 0.0004502251 -0.8976778 -1.488726            0    50
    leadingEdge
         <list>
1: FAU, RPL....
2: CCL5, IL....
3: JUND, S1....
4: HLA-DRB1....
5: B2M, HCS....
6: FTH1, S1....
7: RPS29, R....

$`11`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:      ncMono 0.0002187227 0.0004921260  0.9815952  2.117847            0
2:         cDC 0.0086166744 0.0110785814  0.8857459  1.604149           36
3: Plasma cell 0.0017934003 0.0026901004 -0.8394435 -1.625605            9
4:  CD8 T cell 0.0005265929 0.0009478673 -0.9084665 -1.675881            2
5:      B cell 0.0001838235 0.0004921260 -0.8089817 -1.763016            0
6:     NK cell 0.0001841621 0.0004921260 -0.8476554 -1.858533            0
7:  CD4 T cell 0.0001844678 0.0004921260 -0.9165981 -2.015372            0
    size  leadingEdge
   <int>       <list>
1:    49 LST1, AI....
2:    17 HLA-DPA1....
3:    24 ISG20, R....
4:    18 CCL5, IL....
5:    47 CXCR4, R....
6:    49 NKG7, GN....
7:    50 RPL31, R....

$`12`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:  CD4 T cell 0.0001001101 0.0009009911  0.9518024  1.571553            0
2: Plasma cell 0.0023352625 0.0070057874  0.8574356  1.376587           22
3:      B cell 0.0016033671 0.0070057874  0.7964622  1.311655           15
4:  CD8 T cell 0.0339905525 0.0559006211  0.8136559  1.286572          330
5:     NK cell 0.0395593390 0.0559006211  0.7258037  1.197098          394
6:      ncMono 0.0588235294 0.0661764706 -0.4386277 -1.325709            0
7:       cMono 0.0434782609 0.0559006211 -0.6808314 -2.152243            0
8:         cDC 0.0033333333 0.0075000000 -0.8964181 -2.228026            0
    size  leadingEdge
   <int>       <list>
1:    50 IL7R, RP....
2:    24 RPL36AL,....
3:    47 RPS5, RP....
4:    18 IL32, CD....
5:    49 IFITM1, ....
6:    49 FCER1G, ....
7:    47 S100A8, ....
8:    17 HLA-DRA,....

$`13`
       pathway         pval        padj         ES       NES nMoreExtreme  size
        <char>        <num>       <num>      <num>     <num>        <num> <int>
1:       cMono 0.0002861230 0.000516203  0.9237564  2.137192            0    47
2:      ncMono 0.0002867795 0.000516203  0.8495391  1.979065            0    49
3:         cDC 0.0554525795 0.062384152 -0.7508028 -1.380756          358    17
4:  CD8 T cell 0.0010830883 0.001624633 -0.8666052 -1.605641            6    18
5: Plasma cell 0.0013903909 0.001787645 -0.8330442 -1.618347            8    24
6:     NK cell 0.0001534919 0.000461042 -0.8068779 -1.761721            0    49
7:      B cell 0.0001536807 0.000461042 -0.8446545 -1.831407            0    47
8:  CD4 T cell 0.0001536098 0.000461042 -0.9229947 -2.021512            0    50
    leadingEdge
         <list>
1: S100A8, ....
2: AIF1, S1....
3: HLA-DPB1....
4: IL32, GZ....
5: ISG20, R....
6: GNLY, NK....
7: RPS5, RP....
8: RPL14, R....

$`14`
      pathway        pval         padj         ES       NES nMoreExtreme  size
       <char>       <num>        <num>      <num>     <num>        <num> <int>
1:      cMono 0.000100040 0.0004502251  0.9178528  1.452446            0    47
2:     ncMono 0.000100050 0.0004502251  0.9130436  1.446765            0    49
3:        cDC 0.015832741 0.0284989343  0.8411903  1.275312          155    17
4: CD4 T cell 0.009904952 0.0222861431  0.7618107  1.207520           98    50
5:        pDC 0.052621048 0.0789315726  0.7272444  1.150820          525    47
6: CD8 T cell 0.007575758 0.0222861431 -0.9739951 -2.428217            0    18
    leadingEdge
         <list>
1: S100A9, ....
2: AIF1, S1....
3: HLA-DRA,....
4: RPS13, R....
5: CTSB, NP....
6: IL32, CC....

$`15`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:     NK cell 0.0181818182 0.0181818182  0.8291322  2.166520            0
2:  CD8 T cell 0.0067114094 0.0100671141  0.9883234  2.113793            0
3:         pDC 0.0141794047 0.0159518302 -0.7349782 -1.219553          140
4:      B cell 0.0025140788 0.0049250101 -0.7703560 -1.278256           24
5:         cDC 0.0083282551 0.0107077566 -0.8413649 -1.333409           81
6: Plasma cell 0.0027361167 0.0049250101 -0.8330300 -1.340330           26
7:       cMono 0.0001005632 0.0003016895 -0.8458496 -1.403523            0
8:  CD4 T cell 0.0001004823 0.0003016895 -0.8841618 -1.470774            0
9:      ncMono 0.0001005328 0.0003016895 -0.8851646 -1.470914            0
    size  leadingEdge
   <int>       <list>
1:    49 NKG7, GN....
2:    18 CCL5, GZ....
3:    47 NPC2, YP....
4:    47 RPS11, C....
5:    17 HLA-DRA,....
6:    24 SUB1, RP....
7:    47 S100A9, ....
8:    50 TPT1, RP....
9:    49 FTH1, CO....

$`2`
      pathway         pval         padj         ES       NES nMoreExtreme  size
       <char>        <num>        <num>      <num>     <num>        <num> <int>
1:     B cell 0.0008802817 0.0019806338  0.9759274  2.286607            0    47
2:        cDC 0.0017094017 0.0030769231  0.9364487  1.819993            2    17
3: CD4 T cell 0.0102803738 0.0132176235  0.6651708  1.567700           10    50
4: CD8 T cell 0.0047044632 0.0070566948 -0.8797950 -1.455393           38    18
5:      cMono 0.0001127904 0.0003383713 -0.8465424 -1.546511            0    47
6:     ncMono 0.0001121202 0.0003383713 -0.8885809 -1.631038            0    49
7:    NK cell 0.0001121202 0.0003383713 -0.9126109 -1.675146            0    49
    leadingEdge
         <list>
1: MS4A1, C....
2: HLA-DRA,....
3: RPS6, RP....
4: CCL5, IL....
5: S100A6, ....
6: S100A4, ....
7: HCST, NK....

$`3`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:       cMono 0.0002200220 0.0003960396  0.9539785  2.056921            0
2:      ncMono 0.0002193463 0.0003960396  0.8821112  1.915328            0
3: Plasma cell 0.0029955947 0.0038514789 -0.8344716 -1.600177           16
4:  CD8 T cell 0.0005242006 0.0007863009 -0.9104821 -1.656777            2
5:     NK cell 0.0001837222 0.0003960396 -0.8350365 -1.809856            0
6:      B cell 0.0001832509 0.0003960396 -0.8791693 -1.892953            0
7:  CD4 T cell 0.0001828822 0.0003960396 -0.9284623 -2.023550            0
    size  leadingEdge
   <int>       <list>
1:    47 S100A9, ....
2:    49 AIF1, S1....
3:    24 ISG20, P....
4:    18 CCL5, IL....
5:    49 NKG7, GN....
6:    47 CXCR4, R....
7:    50 RPL3, RP....

$`4`
      pathway         pval         padj         ES       NES nMoreExtreme  size
       <char>        <num>        <num>      <num>     <num>        <num> <int>
1: CD4 T cell 0.0012106538 0.0021791768  0.9835702  2.454592            0    50
2:        cDC 0.0018957346 0.0028436019 -0.8984204 -1.480202           15    17
3:    NK cell 0.0003279047 0.0007377856 -0.8152480 -1.492383            2    49
4:        pDC 0.0002192261 0.0006576784 -0.8331320 -1.519475            1    47
5:      cMono 0.0001096131 0.0004932588 -0.9043158 -1.649300            0    47
6:     ncMono 0.0001093016 0.0004932588 -0.9322838 -1.706628            0    49
    leadingEdge
         <list>
1: IL7R, LD....
2: HLA-DRA,....
3: NKG7, GN....
4: PLEK, PL....
5: S100A9, ....
6: FCER1G, ....

$`5`
      pathway         pval         padj         ES       NES nMoreExtreme  size
       <char>        <num>        <num>      <num>     <num>        <num> <int>
1:    NK cell 0.0007974482 0.0017942584  0.9427049  2.171396            0    49
2: CD8 T cell 0.0005299417 0.0015898251  0.9757739  1.934063            0    18
3: CD4 T cell 0.0160384924 0.0258044554  0.6841476  1.584800           19    50
4:        pDC 0.0828932262 0.1065770051 -0.6791309 -1.275989          721    47
5:        cDC 0.0172029703 0.0258044554 -0.8349892 -1.400588          138    17
6:     ncMono 0.0001143118 0.0005166475 -0.9090886 -1.714372            0    49
7:      cMono 0.0001148106 0.0005166475 -0.9257589 -1.739368            0    47
    leadingEdge
         <list>
1: NKG7, GN....
2: CCL5, GZ....
3: IL7R, RP....
4: NPC2, CT....
5: HLA-DRA,....
6: FCER1G, ....
7: S100A9, ....

$`6`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:      B cell 0.0001007557 0.0004534005  0.9362193  1.593408            0
2:  CD4 T cell 0.0001005935 0.0004534005  0.9245421  1.577215            0
3:         cDC 0.0005278159 0.0015834477  0.9262262  1.485196            4
4: Plasma cell 0.0065257924 0.0117464264  0.8261552  1.356850           62
5:         pDC 0.0129974811 0.0149253731  0.7484645  1.273857          128
6:      ncMono 0.0149253731 0.0149253731 -0.5308483 -1.572343            0
7:       cMono 0.0129870130 0.0149253731 -0.6492382 -1.903165            0
8:  CD8 T cell 0.0020449898 0.0046012270 -0.9208483 -2.178479            0
9:     NK cell 0.0149253731 0.0149253731 -0.7908323 -2.342401            0
    size  leadingEdge
   <int>       <list>
1:    47 CD37, MS....
2:    50 RPS6, RP....
3:    17 HLA-DRA,....
4:    24 ISG20, R....
5:    47 IRF8, BC....
6:    49 S100A4, ....
7:    47 TYROBP, ....
8:    18 CCL5, IL....
9:    49 NKG7, HC....

$`7`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1:     NK cell 0.0001021450 0.0004596527  0.9546415  1.672175            0
2:  CD4 T cell 0.0001020200 0.0004596527  0.8733240  1.531929            0
3: Plasma cell 0.0004268032 0.0012804097  0.8984626  1.506395            3
4:  CD8 T cell 0.0010966115 0.0024673758  0.9003202  1.473335            9
5:         cDC 0.0998937301 0.1284347958 -0.6073699 -1.379830           93
6:      ncMono 0.0141509434 0.0212264151 -0.5229824 -1.472616            2
7:       cMono 0.0045248869 0.0081447964 -0.7893952 -2.210234            0
    size  leadingEdge
   <int>       <list>
1:    49 NKG7, GN....
2:    50 RPL3, RP....
3:    24 PPIB, RP....
4:    18 CCL5, GZ....
5:    17 HLA-DRA,....
6:    49 COTL1, A....
7:    47 S100A9, ....

$`8`
       pathway         pval         padj         ES       NES nMoreExtreme
        <char>        <num>        <num>      <num>     <num>        <num>
1: Plasma cell 0.0185744657 0.0278616986 -0.7754252 -1.341161          165
2:     NK cell 0.0014034330 0.0031577243 -0.7580622 -1.409313           12
3:      B cell 0.0009742368 0.0029227105 -0.7613088 -1.409610            8
4:         cDC 0.0062471604 0.0112448887 -0.8528729 -1.423184           54
5:       cMono 0.0004329942 0.0019484737 -0.7894528 -1.461720            3
6:  CD4 T cell 0.0001079214 0.0009712929 -0.8908780 -1.658522            0
    size  leadingEdge
   <int>       <list>
1:    24 PPIB, RP....
2:    49 ITGB2, N....
3:    47 RPS23, R....
4:    17 HLA-DRB1....
5:    47 JUND, S1....
6:    50 RPL34, R....

$`9`
      pathway         pval         padj         ES       NES nMoreExtreme  size
       <char>        <num>        <num>      <num>     <num>        <num> <int>
1:    NK cell 0.0010504202 0.0018907563  0.9859981  2.203758            0    49
2: CD8 T cell 0.0232052212 0.0298352844  0.8506292  1.698669           31    18
3:        cDC 0.0058119261 0.0087178891 -0.8722269 -1.428073           49    17
4:     ncMono 0.0004419890 0.0009944751 -0.8169229 -1.466723            3    49
5:     B cell 0.0001107420 0.0003322259 -0.8439880 -1.509311            0    47
6:      cMono 0.0001107420 0.0003322259 -0.8862629 -1.584912            0    47
7: CD4 T cell 0.0001103266 0.0003322259 -0.8893830 -1.599761            0    50
    leadingEdge
         <list>
1: NKG7, GN....
2: CCL5, GZ....
3: HLA-DRA,....
4: COTL1, F....
5: RPL18A, ....
6: S100A9, ....
7: RPS13, R....

Selecting top significant overlap per cluster, we can now rename the clusters according to the predicted labels. OBS! Be aware that if you have some clusters that have non-significant p-values for all the gene sets, the cluster label will not be very reliable. Also, the gene sets you are using may not cover all the celltypes you have in your dataset and hence predictions may just be the most similar celltype. Also, some of the clusters have very similar p-values to multiple celltypes, for instance the ncMono and cMono celltypes are equally good for some clusters.

new.cluster.ids <- unlist(lapply(res, function(x) {
    as.data.frame(x)[1, 1]
}))

alldata$ref_gsea <- new.cluster.ids[as.character(alldata$leiden_k20)]

wrap_plots(
    plotReducedDim(alldata, dimred = "UMAP", colour_by = "leiden_k20"),
    plotReducedDim(alldata, dimred = "UMAP", colour_by = "ref_gsea"),
    ncol = 2
)

Compare the results with the other celltype prediction methods in the ctrl_13 sample.

ctrl.sce$ref_gsea <- alldata$ref_gsea[alldata$sample == "ctrl.13"]

wrap_plots(
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "ref_gsea"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "scmap_cell"),
    plotReducedDim(ctrl.sce, dimred = "UMAP", colour_by = "singler.hpca"),
    ncol = 3
)

7.2 With annotated gene sets

We have downloaded the celltype gene lists from http://bio-bigdata.hrbmu.edu.cn/CellMarker/CellMarker_download.html and converted the excel file to a csv for you. Read in the gene lists and do some filtering.

path_file <- file.path("data/human_cell_markers.txt")
if (!file.exists(path_file)) download.file(file.path(path_data, "misc/cell_marker_human.csv"), destfile = path_file, method = "curl", extra = curl_upass)
markers <- read.delim("data/human_cell_markers.txt")
markers <- markers[markers$speciesType == "Human", ]
markers <- markers[markers$cancerType == "Normal", ]

# Filter by tissue (to reduce computational time and have tissue-specific classification)
# sort(unique(markers$tissueType))
# grep("blood",unique(markers$tissueType),value = T)
# markers <- markers [ markers$tissueType %in% c("Blood","Venous blood",
#                                                "Serum","Plasma",
#                                                "Spleen","Bone marrow","Lymph node"), ]


# remove strange characters etc.
celltype_list <- lapply(unique(markers$cellName), function(x) {
    x <- paste(markers$geneSymbol[markers$cellName == x], sep = ",")
    x <- gsub("[[]|[]]| |-", ",", x)
    x <- unlist(strsplit(x, split = ","))
    x <- unique(x[!x %in% c("", "NA", "family")])
    x <- casefold(x, upper = T)
})
names(celltype_list) <- unique(markers$cellName)
# celltype_list <- lapply(celltype_list , function(x) {x[1:min(length(x),50)]} )
celltype_list <- celltype_list[unlist(lapply(celltype_list, length)) < 100]
celltype_list <- celltype_list[unlist(lapply(celltype_list, length)) > 5]
# run fgsea for each of the clusters in the list
res <- lapply(DGE_list, function(x) {
    x$logFC <- rowSums(as.matrix(x[, grep("logFC", colnames(x))]))
    gene_rank <- setNames(x$logFC, rownames(x))
    fgseaRes <- fgsea(pathways = celltype_list, stats = gene_rank, nperm = 10000)
    return(fgseaRes)
})
names(res) <- names(DGE_list)

# You can filter and resort the table based on ES, NES or pvalue
res <- lapply(res, function(x) {
    x[x$pval < 0.01, ]
})
res <- lapply(res, function(x) {
    x[x$size > 5, ]
})
res <- lapply(res, function(x) {
    x[order(x$NES, decreasing = T), ]
})

# show top 3 for each cluster.
lapply(res, head, 3)
$`1`
                           pathway        pval       padj        ES      NES
                            <char>       <num>      <num>     <num>    <num>
1:          CD1C+_B dendritic cell 0.005714286 0.09215686 0.9240174 2.278437
2:                      Neutrophil 0.009803922 0.10112794 0.8330121 2.225166
3: Monocyte derived dendritic cell 0.002100840 0.07405411 0.9258710 1.966663
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:            0    54 S100A8, ....
2:            0    82 S100A8, ....
3:            0    18 S100A8, ....

$`10`
                        pathway       pval       padj         ES       NES
                         <char>      <num>      <num>      <num>     <num>
1: Megakaryocyte erythroid cell 0.00779922 0.08625020 -0.7071694 -1.187934
2:          Natural killer cell 0.00380000 0.07937778 -0.7167473 -1.204219
3:                   Neutrophil 0.00219978 0.05169483 -0.7256130 -1.218451
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:           77    83 PTPRC, C....
2:           37    84 PTPRC, N....
3:           21    82 PTPRC, I....

$`11`
            pathway        pval       padj        ES      NES nMoreExtreme
             <char>       <num>      <num>     <num>    <num>        <num>
1:     Stromal cell 0.000646134 0.04049106 0.8411401 1.728948            2
2: Mesenchymal cell 0.003513123 0.09435243 0.7389426 1.637908           16
3:       Neutrophil 0.001625356 0.06111337 0.7063061 1.630526            7
    size  leadingEdge
   <int>       <list>
1:    38 PECAM1, ....
2:    61 COTL1, S....
3:    82 LST1, FC....

$`12`
                pathway        pval       padj        ES      NES nMoreExtreme
                 <char>       <num>      <num>     <num>    <num>        <num>
1: T helper9 (Th9) cell 0.002138351 0.03382502 0.9414120 1.428838           19
2:         Naive T cell 0.002313354 0.03382502 0.9275264 1.428112           21
3:          CD8+ T cell 0.001742160 0.03382502 0.8930096 1.415301           16
    size  leadingEdge
   <int>       <list>
1:    10 CD3E, CD....
2:    12 IL7R, CD....
3:    19 IL7R, CD....

$`13`
                  pathway         pval       padj        ES      NES
                   <char>        <num>      <num>     <num>    <num>
1:             Neutrophil 0.0002906977 0.01481015 0.8834234 2.234207
2: CD1C+_B dendritic cell 0.0002794857 0.01481015 0.9185908 2.172957
3:          Megakaryocyte 0.0002732240 0.01481015 0.8960407 1.880966
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:            0    82 S100A8, ....
2:            0    54 S100A8, ....
3:            0    26 PPBP, PF....

$`14`
                           pathway        pval        padj        ES      NES
                            <char>       <num>       <num>     <num>    <num>
1: Monocyte derived dendritic cell 0.000303859 0.010767623 0.9303951 1.420317
2:                      Neutrophil 0.000099990 0.009403762 0.8820943 1.416893
3:                   Hemangioblast 0.001068262 0.015273941 0.9721348 1.412983
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:            2    18 S100A9, ....
2:            0    82 S100A9, ....
3:            9     8 PECAM1, CDH1

$`15`
                              pathway        pval       padj         ES
                               <char>       <num>      <num>      <num>
1:                  Naive CD8+ T cell 0.006802721 0.07856247 -0.7007477
2:                          Stem cell 0.005329847 0.07157223 -0.7492465
3: Specialist antigen presenting cell 0.006542526 0.07856247 -0.7565157
         NES nMoreExtreme  size  leadingEdge
       <num>        <num> <int>       <list>
1: -1.189770           67    91 RPS8, AI....
2: -1.249298           52    52 VIM, CD4....
3: -1.255061           64    46 CD48, CD....

$`2`
                       pathway        pval       padj         ES       NES
                        <char>       <num>      <num>      <num>     <num>
1: CD4+CD25+ regulatory T cell 0.007720492 0.05805810 -0.9655285 -1.415802
2:      T helper17 (Th17) cell 0.008414164 0.05858751 -0.8162433 -1.418422
3:                 Granulocyte 0.009432821 0.06333466 -0.8610190 -1.425316
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:           58     6 CD3E, PT....
2:           71    28 CD3E, CD....
3:           77    18 CD63, PT....

$`3`
                  pathway         pval       padj        ES      NES
                   <char>        <num>      <num>     <num>    <num>
1:             Neutrophil 0.0002157032 0.02053298 0.9126227 2.148260
2: CD1C+_B dendritic cell 0.0002184360 0.02053298 0.9239943 2.048965
3:           Stromal cell 0.0024763620 0.08186371 0.8242241 1.722839
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:            0    82 S100A9, ....
2:            0    54 S100A9, ....
3:           10    38 VIM, TIM....

$`4`
             pathway         pval       padj        ES      NES nMoreExtreme
              <char>        <num>      <num>     <num>    <num>        <num>
1: Naive CD8+ T cell 0.0028328612 0.03672018 0.8335019 2.296178            0
2: Naive CD4+ T cell 0.0009813543 0.02049940 0.9020382 2.098437            0
3:       CD4+ T cell 0.0007936508 0.02049940 0.9168569 2.016731            0
    size  leadingEdge
   <int>       <list>
1:    91 LDHB, PI....
2:    34 IL7R, TC....
3:    25 LTB, IL7....

$`5`
                 pathway         pval       padj        ES      NES
                  <char>        <num>      <num>     <num>    <num>
1: CD4+ cytotoxic T cell 0.0010660981 0.02863235 0.8810115 2.234672
2:   Natural killer cell 0.0010559662 0.02863235 0.7810959 1.971997
3:           CD8+ T cell 0.0005668934 0.02664399 0.9445287 1.914053
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:            0    86 NKG7, CC....
2:            0    84 NKG7, GN....
3:            0    19 NKG7, CD....

$`6`
                  pathway        pval       padj        ES      NES
                   <char>       <num>      <num>     <num>    <num>
1:      Follicular B cell 0.001248959 0.03772828 0.8849481 1.448385
2: Lake et al.Science.Ex5 0.005774216 0.06308120 0.9631247 1.413750
3: Germinal center B cell 0.009589652 0.07511894 0.9175945 1.395307
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:           11    22 MS4A1, C....
2:           48     6 RCSD1, A....
3:           85     9 IRF8, PA....

$`7`
                             pathway         pval        padj        ES
                              <char>        <num>       <num>     <num>
1:             CD4+ cytotoxic T cell 0.0001004218 0.006301324 0.9001302
2: Effector CD8+ memory T (Tem) cell 0.0001005530 0.006301324 0.8819448
3:                  Cytotoxic T cell 0.0003219921 0.015133627 0.8840783
        NES nMoreExtreme  size  leadingEdge
      <num>        <num> <int>       <list>
1: 1.613471            0    86 NKG7, GN....
2: 1.577777            0    79 GNLY, FG....
3: 1.478784            2    23 GZMB, GZ....

$`8`
               pathway        pval      padj         ES       NES nMoreExtreme
                <char>       <num>     <num>      <num>     <num>        <num>
1:       Megakaryocyte 0.001955034 0.1115692  0.8280536  1.809771            1
2:            Platelet 0.005188067 0.1464878  0.6629861  1.640075            3
3: Natural killer cell 0.009988555 0.1877848 -0.6753616 -1.313413           95
    size  leadingEdge
   <int>       <list>
1:    26 PPBP, PF....
2:    45 GP9, ITG....
3:    84 PTPRC, N....

$`9`
                             pathway        pval       padj        ES      NES
                              <char>       <num>      <num>     <num>    <num>
1:             CD4+ cytotoxic T cell 0.001464129 0.03937997 0.9473821 2.311910
2: Effector CD8+ memory T (Tem) cell 0.001388889 0.03937997 0.8677227 2.094062
3:               Natural killer cell 0.001466276 0.03937997 0.7976773 1.935329
   nMoreExtreme  size  leadingEdge
          <num> <int>       <list>
1:            0    86 NKG7, GN....
2:            0    79 GNLY, GZ....
3:            0    84 NKG7, GN....

#CT_GSEA8:

new.cluster.ids <- unlist(lapply(res, function(x) {
    as.data.frame(x)[1, 1]
}))
alldata$cellmarker_gsea <- new.cluster.ids[as.character(alldata$leiden_k20)]

wrap_plots(
    plotReducedDim(alldata, dimred = "UMAP", colour_by = "cellmarker_gsea"),
    plotReducedDim(alldata, dimred = "UMAP", colour_by = "ref_gsea"),
    ncol = 2
)

Discuss

Do you think that the methods overlap well? Where do you see the most inconsistencies?

In this case we do not have any ground truth, and we cannot say which method performs best. You should keep in mind, that any celltype classification method is just a prediction, and you still need to use your common sense and knowledge of the biological system to judge if the results make sense.

Finally, lets save the data with predictions.

saveRDS(ctrl.sce, "data/covid/results/bioc_covid_qc_dr_int_cl_ct-ctrl13.rds")

8 Session info

Click here 
sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS/LAPACK: /Users/asabjor/miniconda3/envs/seurat5_u/lib/libopenblasp-r0.3.28.dylib;  LAPACK version 3.12.0

locale:
[1] sv_SE.UTF-8/sv_SE.UTF-8/sv_SE.UTF-8/C/sv_SE.UTF-8/sv_SE.UTF-8

time zone: Europe/Stockholm
tzcode source: system (macOS)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] fgsea_1.28.0                celldex_1.12.0             
 [3] Seurat_5.1.0                SeuratObject_5.0.2         
 [5] sp_2.1-4                    SingleR_2.4.0              
 [7] scmap_1.24.0                scPred_1.9.2               
 [9] pheatmap_1.0.12             patchwork_1.3.0            
[11] dplyr_1.1.4                 scran_1.30.0               
[13] scater_1.30.1               ggplot2_3.5.1              
[15] scuttle_1.12.0              SingleCellExperiment_1.24.0
[17] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[19] GenomicRanges_1.54.1        GenomeInfoDb_1.38.1        
[21] IRanges_2.36.0              S4Vectors_0.40.2           
[23] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[25] matrixStats_1.5.0          

loaded via a namespace (and not attached):
  [1] spatstat.sparse_3.1-0         bitops_1.0-9                 
  [3] lubridate_1.9.4               httr_1.4.7                   
  [5] RColorBrewer_1.1-3            tools_4.3.3                  
  [7] sctransform_0.4.1             R6_2.5.1                     
  [9] lazyeval_0.2.2                uwot_0.1.16                  
 [11] withr_3.0.2                   gridExtra_2.3                
 [13] progressr_0.15.1              cli_3.6.3                    
 [15] spatstat.explore_3.3-4        fastDummies_1.7.4            
 [17] labeling_0.4.3                spatstat.data_3.1-4          
 [19] randomForest_4.7-1.2          proxy_0.4-27                 
 [21] ggridges_0.5.6                pbapply_1.7-2                
 [23] harmony_1.2.3                 parallelly_1.41.0            
 [25] limma_3.58.1                  RSQLite_2.3.9                
 [27] FNN_1.1.4.1                   generics_0.1.3               
 [29] ica_1.0-3                     spatstat.random_3.3-2        
 [31] Matrix_1.6-5                  ggbeeswarm_0.7.2             
 [33] abind_1.4-5                   lifecycle_1.0.4              
 [35] yaml_2.3.10                   edgeR_4.0.16                 
 [37] BiocFileCache_2.10.1          recipes_1.1.0                
 [39] SparseArray_1.2.2             Rtsne_0.17                   
 [41] blob_1.2.4                    grid_4.3.3                   
 [43] promises_1.3.2                dqrng_0.3.2                  
 [45] ExperimentHub_2.10.0          crayon_1.5.3                 
 [47] miniUI_0.1.1.1                lattice_0.22-6               
 [49] beachmat_2.18.0               cowplot_1.1.3                
 [51] KEGGREST_1.42.0               pillar_1.10.1                
 [53] knitr_1.49                    metapod_1.10.0               
 [55] future.apply_1.11.2           codetools_0.2-20             
 [57] fastmatch_1.1-6               leiden_0.4.3.1               
 [59] googleVis_0.7.3               glue_1.8.0                   
 [61] spatstat.univar_3.1-1         data.table_1.15.4            
 [63] vctrs_0.6.5                   png_0.1-8                    
 [65] spam_2.11-0                   gtable_0.3.6                 
 [67] cachem_1.1.0                  gower_1.0.1                  
 [69] xfun_0.50                     S4Arrays_1.2.0               
 [71] mime_0.12                     prodlim_2024.06.25           
 [73] survival_3.8-3                timeDate_4041.110            
 [75] iterators_1.0.14              hardhat_1.4.0                
 [77] lava_1.8.0                    statmod_1.5.0                
 [79] bluster_1.12.0                interactiveDisplayBase_1.40.0
 [81] fitdistrplus_1.2-2            ROCR_1.0-11                  
 [83] ipred_0.9-15                  nlme_3.1-165                 
 [85] bit64_4.5.2                   filelock_1.0.3               
 [87] RcppAnnoy_0.0.22              irlba_2.3.5.1                
 [89] vipor_0.4.7                   KernSmooth_2.23-26           
 [91] rpart_4.1.24                  DBI_1.2.3                    
 [93] colorspace_2.1-1              nnet_7.3-20                  
 [95] tidyselect_1.2.1              curl_6.0.1                   
 [97] bit_4.5.0.1                   compiler_4.3.3               
 [99] BiocNeighbors_1.20.0          DelayedArray_0.28.0          
[101] plotly_4.10.4                 scales_1.3.0                 
[103] lmtest_0.9-40                 rappdirs_0.3.3               
[105] stringr_1.5.1                 digest_0.6.37                
[107] goftest_1.2-3                 spatstat.utils_3.1-2         
[109] rmarkdown_2.29                XVector_0.42.0               
[111] htmltools_0.5.8.1             pkgconfig_2.0.3              
[113] sparseMatrixStats_1.14.0      dbplyr_2.5.0                 
[115] fastmap_1.2.0                 rlang_1.1.4                  
[117] htmlwidgets_1.6.4             shiny_1.10.0                 
[119] DelayedMatrixStats_1.24.0     farver_2.1.2                 
[121] zoo_1.8-12                    jsonlite_1.8.9               
[123] BiocParallel_1.36.0           ModelMetrics_1.2.2.2         
[125] BiocSingular_1.18.0           RCurl_1.98-1.16              
[127] magrittr_2.0.3                GenomeInfoDbData_1.2.11      
[129] dotCall64_1.2                 munsell_0.5.1                
[131] Rcpp_1.0.13-1                 viridis_0.6.5                
[133] reticulate_1.40.0             stringi_1.8.4                
[135] pROC_1.18.5                   zlibbioc_1.48.0              
[137] MASS_7.3-60.0.1               AnnotationHub_3.10.0         
[139] plyr_1.8.9                    parallel_4.3.3               
[141] listenv_0.9.1                 ggrepel_0.9.6                
[143] deldir_2.0-4                  Biostrings_2.70.1            
[145] splines_4.3.3                 tensor_1.5                   
[147] locfit_1.5-9.10               igraph_2.0.3                 
[149] spatstat.geom_3.3-4           RcppHNSW_0.6.0               
[151] reshape2_1.4.4                ScaledMatrix_1.10.0          
[153] BiocVersion_3.18.1            evaluate_1.0.1               
[155] BiocManager_1.30.25           foreach_1.5.2                
[157] httpuv_1.6.15                 RANN_2.6.2                   
[159] tidyr_1.3.1                   purrr_1.0.2                  
[161] polyclip_1.10-7               future_1.34.0                
[163] scattermore_1.2               rsvd_1.0.5                   
[165] xtable_1.8-4                  e1071_1.7-16                 
[167] RSpectra_0.16-2               later_1.4.1                  
[169] viridisLite_0.4.2             class_7.3-23                 
[171] tibble_3.2.1                  memoise_2.0.1                
[173] AnnotationDbi_1.64.1          beeswarm_0.4.0               
[175] cluster_2.1.8                 timechange_0.3.0             
[177] globals_0.16.3                caret_6.0-94