SS2 (for AG Hefendehl): Integration
Desirée Brösamle
Dec 2021
Last updated: 2023-02-16
Checks: 6 1
Knit directory: scSeq_Hefendehl/
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Preprocessing
QC metrics from AG Hefendehl (Stroke)
Combined datasets and filter cells that have unique feature counts (gene number) over 7000 or less than 200 and cells with >5% mitochondrial counts.
# Visualize QC metrics as a violin plot
Idents(plates_hefendehl) <- "Plate"
VlnPlot(plates_hefendehl, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rp"),
group.by = "Plate", pt.size = 1, ncol = 4)
SampleMeta=plates_hefendehl@meta.data
cormt<- cor(SampleMeta$nCount_RNA, SampleMeta$percent.mt, method="spearman")
corfeat<- cor(SampleMeta$nCount_RNA, SampleMeta$nFeature_RNA, method="spearman")
plot1 <- ggplot(plates_hefendehl@meta.data,aes(x=nCount_RNA, y=percent.mt, col=Plate))+
geom_point()+theme_classic()+geom_abline(slope = 0, intercept = 5)+labs(title=cormt)
plot2 <- ggplot(plates_hefendehl@meta.data,aes(x=nCount_RNA, y=nFeature_RNA, col=Plate))+geom_point()+theme_classic()+geom_abline(slope = 0, intercept = 7000)+
ggtitle(corfeat)
plot1 | plot2
Perform integrated analysis
PCA, tSNE and UMAP from the first 30 dimensions and with a resolution of 0.8
# Visualization
Idents(samples.integrated) <- "seurat_clusters"
DimPlot(samples.integrated, reduction = "umap", split.by = "Treatment")
p1<- DimPlot(samples.integrated, reduction = "umap", group.by = "Plate")
p1a<- DimPlot(samples.integrated, reduction = "umap", group.by = "Mouse_ID")
p1b<- DimPlot(samples.integrated, reduction = "umap", group.by = "Genotype_Treatment")
p1 + p1a + p1b
DimPlot(samples.integrated, group.by = "seurat_clusters", pt.size =1, label = T) + NoLegend()
DimPlot(samples.integrated, reduction = "umap", split.by = "Plate", pt.size= 1)
DimPlot(samples.integrated, reduction = "umap", split.by = "Genotype_Treatment", pt.size =1)
Cell cycle scoring
First, a score is assigned to each cell (Tirosh et al. 2016), based on its expression of G2/M and S phase markers. These markers should be anticorrelated in their expression levels and cells expression neither are likely not cycling and in G1 phase. Note: For downstream cell cylce regression the quantitative scores for G2/M and S phase are used, not the dicrete classification.
Cluster distribution
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Age",label = F) +
ggtitle("Cluster distribution according to Age") | DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Genotype_Treatment",label = F) +
ggtitle("Cluster distribution according to Genotype + Treatment")
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Plate",label = F) +
ggtitle("Cluster distribution according to Plate") | DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by = "seurat_clusters", label = T) +
ggtitle("Clustering")
Idents(samples.integrated) <- "seurat_clusters"
# use same colors for clusters as in plots
require(scales)
samples.integrated$seurat_clusters = factor(samples.integrated$seurat_clusters, levels=sort(as.character(unique(samples.integrated$seurat_clusters))))
identities <- levels(samples.integrated$seurat_clusters) # Create vector with levels of object@ident
cluster_colors <- hue_pal()(length(identities)) # Create vector of default ggplot2 colors
# number of cells in each cluster
cluster_nCell <- as.data.frame.matrix(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment))
cluster_nCell["Total" ,] = colSums(cluster_nCell)
cluster_nCell APPPS1_Ctrl APPPS1_Stroke WT_Ctrl WT_Stroke
0 220 56 117 83
1 54 41 42 23
2 21 61 49 12
3 0 18 5 4
4 3 28 10 4
5 8 1 5 5
6 5 12 8 10
Total 311 217 236 141# % of cells in each cluster , grouped by genotype_Age
cluster_per_Cell <- data.frame(table(samples.integrated$seurat_clusters,
samples.integrated$Age))
colnames(cluster_per_Cell) <- c("Cluster", "Age", "Frequency")
cluster_per_Cell Cluster Age Frequency
1 0 37 weeks 295
2 1 37 weeks 100
3 2 37 weeks 83
4 3 37 weeks 15
5 4 37 weeks 14
6 5 37 weeks 8
7 6 37 weeks 18
8 0 38 weeks 49
9 1 38 weeks 11
10 2 38 weeks 16
11 3 38 weeks 3
12 4 38 weeks 3
13 5 38 weeks 4
14 6 38 weeks 4
15 0 40 weeks 132
16 1 40 weeks 49
17 2 40 weeks 44
18 3 40 weeks 9
19 4 40 weeks 28
20 5 40 weeks 7
21 6 40 weeks 13cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Age), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Cluster", "Age", "Frequency")
cluster_percent_Cell Cluster Age Frequency
1 0 37 weeks 55.35
2 1 37 weeks 18.76
3 2 37 weeks 15.57
4 3 37 weeks 2.81
5 4 37 weeks 2.63
6 5 37 weeks 1.50
7 6 37 weeks 3.38
8 0 38 weeks 54.44
9 1 38 weeks 12.22
10 2 38 weeks 17.78
11 3 38 weeks 3.33
12 4 38 weeks 3.33
13 5 38 weeks 4.44
14 6 38 weeks 4.44
15 0 40 weeks 46.81
16 1 40 weeks 17.38
17 2 40 weeks 15.60
18 3 40 weeks 3.19
19 4 40 weeks 9.93
20 5 40 weeks 2.48
21 6 40 weeks 4.61cluster_per_Treatment <- data.frame(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment))
colnames(cluster_per_Treatment) <- c("Cluster", "Genotype_Treatment", "Frequency")
cluster_per_Treatment Cluster Genotype_Treatment Frequency
1 0 APPPS1_Ctrl 220
2 1 APPPS1_Ctrl 54
3 2 APPPS1_Ctrl 21
4 3 APPPS1_Ctrl 0
5 4 APPPS1_Ctrl 3
6 5 APPPS1_Ctrl 8
7 6 APPPS1_Ctrl 5
8 0 APPPS1_Stroke 56
9 1 APPPS1_Stroke 41
10 2 APPPS1_Stroke 61
11 3 APPPS1_Stroke 18
12 4 APPPS1_Stroke 28
13 5 APPPS1_Stroke 1
14 6 APPPS1_Stroke 12
15 0 WT_Ctrl 117
16 1 WT_Ctrl 42
17 2 WT_Ctrl 49
18 3 WT_Ctrl 5
19 4 WT_Ctrl 10
20 5 WT_Ctrl 5
21 6 WT_Ctrl 8
22 0 WT_Stroke 83
23 1 WT_Stroke 23
24 2 WT_Stroke 12
25 3 WT_Stroke 4
26 4 WT_Stroke 4
27 5 WT_Stroke 5
28 6 WT_Stroke 10cluster_percent_Treatment <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Treatment) <- c("Cluster", "Genotype_Treatment", "Frequency")
cluster_percent_Treatment Cluster Genotype_Treatment Frequency
1 0 APPPS1_Ctrl 70.74
2 1 APPPS1_Ctrl 17.36
3 2 APPPS1_Ctrl 6.75
4 3 APPPS1_Ctrl 0.00
5 4 APPPS1_Ctrl 0.96
6 5 APPPS1_Ctrl 2.57
7 6 APPPS1_Ctrl 1.61
8 0 APPPS1_Stroke 25.81
9 1 APPPS1_Stroke 18.89
10 2 APPPS1_Stroke 28.11
11 3 APPPS1_Stroke 8.29
12 4 APPPS1_Stroke 12.90
13 5 APPPS1_Stroke 0.46
14 6 APPPS1_Stroke 5.53
15 0 WT_Ctrl 49.58
16 1 WT_Ctrl 17.80
17 2 WT_Ctrl 20.76
18 3 WT_Ctrl 2.12
19 4 WT_Ctrl 4.24
20 5 WT_Ctrl 2.12
21 6 WT_Ctrl 3.39
22 0 WT_Stroke 58.87
23 1 WT_Stroke 16.31
24 2 WT_Stroke 8.51
25 3 WT_Stroke 2.84
26 4 WT_Stroke 2.84
27 5 WT_Stroke 3.55
28 6 WT_Stroke 7.09# Grouped barchart of cell proportions
a1<- ggplot(cluster_percent_Cell, aes(fill=Age,
y=Frequency,
x=Cluster)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Age [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1)) +
xlab("")
# Grouped barchart of cell proportions
b1 <- ggplot(cluster_percent_Cell, aes(fill=Cluster, y=Frequency, x=Age)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Age [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1))
c1 <-ggplot(cluster_percent_Cell, aes(fill=Cluster, y=Frequency, x=Age)) +
geom_bar(position="stack", stat="identity")
a1 | b1 | c1
# Grouped barchart of cell proportions
a1<- ggplot(cluster_percent_Treatment, aes(fill=Genotype_Treatment,
y=Frequency,
x=Cluster)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1)) +
xlab("")
# Grouped barchart of cell proportions
b1 <- ggplot(cluster_percent_Treatment, aes(fill=Cluster, y=Frequency, x=Genotype_Treatment)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1))
c1 <-ggplot(cluster_percent_Treatment, aes(fill=Cluster, y=Frequency, x=Genotype_Treatment)) +
geom_bar(position="stack", stat="identity")
a1 | b1 | c1
Cell type identification
Finding differentially expressed features (cluster biomarkers)
Idents(samples.integrated) <- samples.integrated$seurat_clusters
markers <- FindAllMarkers(object = samples.integrated,
only.pos = TRUE,
logfc.threshold = 0.25)
DefaultAssay(samples.integrated) = "SCT"
markers %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC) -> top20
DoHeatmap(samples.integrated, features = top20$gene) + NoLegend() + ggtitle("Top20 cluster marker genes")
SingleR
SingleR is an automatic annotation method for (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 set. Here we use the built-in references “Immgen” (830 microarray samples of sorted hematopoetic and immune cell populations) and “Mouse RNA-Seq” (358 non-specific mouse RNA-seq samples).
Immgen reference
MouseRNA-Seq reference Section Skipped as pred.mouseRNA object is not available
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$MouseRNASeq_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c( "MouseRNASeq_sc_labels", "Genotype_Treatment","Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$MouseRNASeq_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("MouseRNASeq_sc_labels", "Genotype_Treatment", "Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to MouseRNASeq reference (absolut values)") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to MouseRNASeq reference [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$Immgen_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c( "Immgen_sc_labels", "Genotype_Treatment","Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$Immgen_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c( "Immgen_sc_labels", "Genotype_Treatment","Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to Immgen reference (absolut values)") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Immgen reference [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
Renaming Clusters
DimPlot(samples.integrated, reduction = "umap", label=T) + NoLegend()
Zoom into Microglia clusters
A second Seurat cluster is generated, where all non-microglial immune cells are removed according to the mouseRNASeq_sc_labels.
samples.microglia <- samples.integrated[,grepl("Microgli.*", samples.integrated$MouseRNASeq_cluster_labels)]
p3 <- DimPlot(samples.microglia, reduction = "umap", label=T, group.by = "MouseRNASeq_cluster_labels") + NoLegend()
p3a <- DimPlot(samples.microglia, reduction = "umap", label=T, group.by = "seurat_clusters") + NoLegend()
p3 + p3a
DAMs (Cell. 2017 Jun 15;169(7):1276-1290.e17. doi: 10.1016/j.cell.2017.05.018)
DAM plotting
DAM2_marker_gene_list <- list(Stage2_DAM_up)
samples.microglia <- AddModuleScore(object = samples.microglia,
features = DAM2_marker_gene_list, name = "DAM2_score")
p5 <- FeaturePlot(object = samples.microglia, features = "DAM2_score1")+scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.p5a <- DimPlot(object = samples.microglia, reduction = "umap")
p5 | p5a
DAM 2 Markers
DAM2 upreagulated markers
DAM2 dowreagulated markers
Renaming clusters
Idents(samples.microglia) <- "Celltype"
DimPlot(samples.microglia, reduction = "umap", label = TRUE, pt.size = 1)+ NoLegend()
Signature enrichment
All enrichment tests were done with gprofiler 2
Tested Ontologies and Signatures GO:MF = Molecular Function GO:BP = Biological Processes GO:CC = Cellular Compartment), KEGG = pathways from KEGG Reactome, TF = regulatory motif matches from TRANSFAC HPA = tissue specificity from Human Protein Atlas; CORUM = protein complexes from CORUM HP = human disease phenotypes from Human Phenotype Ontology.
For more dteails see Website: https://biit.cs.ut.ee/gprofiler/gost”
DEGs and GSEA across microglia of different genotypes
log fold-change of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the target group (e.g. the APPPS1+ group)
WT vs App
WT vs MX04+ log fold-chage of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the Methoxy positive group
MX04+ vs MX04- in APPPS1 cohort only
DEGs and GSEA of microglia of different genotype and treatment
WT_Ctrl vs WT_Stroke
APP_Ctrl vs APP_Stroke
WT_Ctrl vs APP_Ctrl
WT_Stroke vs APP_Stroke
DEGs and GSEA of each cluster
log fold-chage of the average expression between the two groups: Positive values indicate that the gene is more highly expressed in the target group (e.g. Microglia 0) versus all others.
Microglia_0
Microglia_1
Microglia_2
Microglia_3
Microglia_4
Microglia_5
Genotype + Treatment comparison in each cluster
Idents(samples.microglia) <- "Genotype_Treatment"
mg1 <- samples.microglia[,samples.microglia$Celltype == "Microglia_1"]
mg1 <- PrepSCTFindMarkers(mg1)Found 4 SCT models. Recorrecting SCT counts using minimum median counts: 86149.6040823294mg1.markers.Genotype_Treatment <- FindAllMarkers(mg1)Calculating cluster APPPS1_StrokeCalculating cluster APPPS1_CtrlCalculating cluster WT_StrokeCalculating cluster WT_Ctrlmg1_top20 = mg1.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg2 <- samples.microglia[,samples.microglia$Celltype == "Microglia_2"]
mg2 <- PrepSCTFindMarkers(mg2)Found 4 SCT models. Recorrecting SCT counts using minimum median counts: 130791.604296882mg2.markers.Genotype_Treatment <- FindAllMarkers(mg2)Calculating cluster APPPS1_StrokeCalculating cluster APPPS1_CtrlCalculating cluster WT_StrokeCalculating cluster WT_Ctrlmg2_top20 = mg2.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg3 <- samples.microglia[,samples.microglia$Celltype == "Microglia_3"]
mg3 <- PrepSCTFindMarkers(mg3)Found 4 SCT models. Recorrecting SCT counts using minimum median counts: 33475.3117257001mg3.markers.Genotype_Treatment <- FindAllMarkers(mg3)Calculating cluster APPPS1_StrokeCalculating cluster APPPS1_CtrlCalculating cluster WT_StrokeCalculating cluster WT_Ctrlmg3_top20 = mg3.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg4 <- samples.microglia[,samples.microglia$Celltype == "Microglia_4"]
mg4 <- PrepSCTFindMarkers(mg4)Found 4 SCT models. Recorrecting SCT counts using minimum median counts: 1490mg4.markers.Genotype_Treatment <- FindAllMarkers(mg4)Calculating cluster APPPS1_StrokeCalculating cluster APPPS1_CtrlCalculating cluster WT_StrokeCalculating cluster WT_Ctrlmg4_top20 = mg4.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))
mg5 <- samples.microglia[,samples.microglia$Celltype == "Microglia_5"]
mg5 <- PrepSCTFindMarkers(mg5)Found 4 SCT models. Recorrecting SCT counts using minimum median counts: 101880.479534596mg5.markers.Genotype_Treatment <- FindAllMarkers(mg5)Calculating cluster APPPS1_StrokeCalculating cluster APPPS1_CtrlCalculating cluster WT_StrokeCalculating cluster WT_CtrlWarning: The following tests were not performed:Warning: When testing APPPS1_Stroke versus all:
Cell group 1 has fewer than 3 cellsmg5_top20 = mg5.markers.Genotype_Treatment %>% group_by(cluster) %>% top_n(n=20, wt=abs(avg_log2FC))Microglia_1
Microglia_2
Microglia_3
Microglia_4
Microglia_5
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 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
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
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scales_1.2.1 gprofiler2_0.2.1
[3] forcats_1.0.0 stringr_1.5.0
[5] dplyr_1.1.0 purrr_1.0.1
[7] readr_2.1.4 tidyr_1.3.0
[9] tibble_3.1.8 tidyverse_1.3.2
[11] pheatmap_1.0.12 EnhancedVolcano_1.16.0
[13] ggrepel_0.9.3 SingleR_2.0.0
[15] SummarizedExperiment_1.28.0 Biobase_2.58.0
[17] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[19] IRanges_2.32.0 S4Vectors_0.36.1
[21] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
[23] matrixStats_0.63.0 ggplot2_3.4.1
[25] SeuratObject_4.1.3 Seurat_4.3.0
[27] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.3 spatstat.explore_3.0-6
[3] reticulate_1.28 tidyselect_1.2.0
[5] htmlwidgets_1.6.1 grid_4.2.2
[7] BiocParallel_1.32.5 Rtsne_0.16
[9] munsell_0.5.0 ScaledMatrix_1.6.0
[11] codetools_0.2-19 ica_1.0-3
[13] DT_0.27 future_1.31.0
[15] miniUI_0.1.1.1 withr_2.5.0
[17] spatstat.random_3.1-3 colorspace_2.1-0
[19] progressr_0.13.0 highr_0.10
[21] knitr_1.42 rstudioapi_0.14
[23] ROCR_1.0-11 tensor_1.5
[25] listenv_0.9.0 labeling_0.4.2
[27] git2r_0.31.0 GenomeInfoDbData_1.2.9
[29] polyclip_1.10-4 farver_2.1.1
[31] rprojroot_2.0.3 parallelly_1.34.0
[33] vctrs_0.5.2 generics_0.1.3
[35] xfun_0.37 timechange_0.2.0
[37] R6_2.5.1 ggbeeswarm_0.7.1
[39] rsvd_1.0.5 bitops_1.0-7
[41] spatstat.utils_3.0-1 cachem_1.0.6
[43] DelayedArray_0.24.0 assertthat_0.2.1
[45] promises_1.2.0.1 googlesheets4_1.0.1
[47] beeswarm_0.4.0 gtable_0.3.1
[49] beachmat_2.14.0 globals_0.16.2
[51] processx_3.8.0 goftest_1.2-3
[53] rlang_1.0.6 splines_4.2.2
[55] lazyeval_0.2.2 gargle_1.3.0
[57] spatstat.geom_3.0-6 broom_1.0.3
[59] yaml_2.3.7 reshape2_1.4.4
[61] abind_1.4-5 modelr_0.1.10
[63] crosstalk_1.2.0 backports_1.4.1
[65] httpuv_1.6.9 tools_4.2.2
[67] ellipsis_0.3.2 jquerylib_0.1.4
[69] RColorBrewer_1.1-3 ggridges_0.5.4
[71] Rcpp_1.0.10 plyr_1.8.8
[73] sparseMatrixStats_1.10.0 zlibbioc_1.44.0
[75] RCurl_1.98-1.10 ps_1.7.2
[77] deldir_1.0-6 pbapply_1.7-0
[79] cowplot_1.1.1 zoo_1.8-11
[81] haven_2.5.1 cluster_2.1.4
[83] fs_1.6.1 magrittr_2.0.3
[85] data.table_1.14.6 scattermore_0.8
[87] reprex_2.0.2 lmtest_0.9-40
[89] RANN_2.6.1 googledrive_2.0.0
[91] whisker_0.4.1 fitdistrplus_1.1-8
[93] hms_1.1.2 patchwork_1.1.2
[95] mime_0.12 evaluate_0.20
[97] xtable_1.8-4 readxl_1.4.2
[99] gridExtra_2.3 compiler_4.2.2
[101] crayon_1.5.2 KernSmooth_2.23-20
[103] htmltools_0.5.4 tzdb_0.3.0
[105] later_1.3.0 lubridate_1.9.2
[107] DBI_1.1.3 dbplyr_2.3.0
[109] MASS_7.3-58.2 Matrix_1.5-3
[111] cli_3.6.0 parallel_4.2.2
[113] igraph_1.4.0 pkgconfig_2.0.3
[115] getPass_0.2-2 sp_1.6-0
[117] plotly_4.10.1 spatstat.sparse_3.0-0
[119] xml2_1.3.3 vipor_0.4.5
[121] bslib_0.4.2 XVector_0.38.0
[123] rvest_1.0.3 callr_3.7.3
[125] digest_0.6.31 sctransform_0.3.5
[127] RcppAnnoy_0.0.20 spatstat.data_3.0-0
[129] rmarkdown_2.20 cellranger_1.1.0
[131] leiden_0.4.3 uwot_0.1.14
[133] DelayedMatrixStats_1.20.0 shiny_1.7.4
[135] lifecycle_1.0.3 nlme_3.1-162
[137] jsonlite_1.8.4 limma_3.54.1
[139] viridisLite_0.4.1 fansi_1.0.4
[141] pillar_1.8.1 lattice_0.20-45
[143] ggrastr_1.0.1 fastmap_1.1.0
[145] httr_1.4.4 survival_3.5-3
[147] glue_1.6.2 png_0.1-8
[149] stringi_1.7.12 sass_0.4.5
[151] BiocSingular_1.14.0 irlba_2.3.5.1
[153] future.apply_1.10.0