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Rmd 8d9f708 jessievb 2021-10-07 quality check page and remove header title

Introduction

The single-cell multi-omics data contains single-cell transcriptomic and proteomic and phospho-proteomic data of in-vitro generated plasmacells. The code below shows Quality control plots, QC-filtering, normalization and scaling of the dataset.

Convert matrix to Seurat objects

Import all count matrixes (from data/raw folder), combine plates and create unfiltered Seurat objects.

source("code/Import_and_create_seuratObj.R")
seu_RNA <- readRDS("output/seu.RNA.rds")
seu.PROT_live <- readRDS("output/seu.PROT_live.rds")
seu.PROT_fix <- readRDS("output/seu.PROT_fix.rds")

QC plots

plot_RNA_nCount <- plot_QC_paper(seu_object = seu_RNA, 
                                 feature = "nCount_RNA", 
                                 ytext = "Total UMI counts per cell",
                                 xtext = "Plate number",
                                 paneltitle = "Fixed cells (1586 to 1589) show lower counts",
                                 colorviolin = "dodgerblue2" ) + 
                        geom_vline(xintercept =6.5, size = 0.3, color = "red") +
  annotate(geom = "text", x = 6.6, y=20000, label = "Fixed cells", hjust = 0, size = 2.5) +
                        theme(axis.title.x = element_blank()) 

plot_RNA_ngenes <- plot_QC_paper(seu_object = seu_RNA, 
                                 feature = "nFeature_RNA", 
                                 ytext = "Total genes per cell",
                                 xtext = "Plate number",
                                 paneltitle = "Keep cells >300 genes",
                                 colorviolin = "dodgerblue2" ) + 
                        geom_hline(yintercept = 300, size = 0.3, color = "red") +
                        theme(axis.title.x = element_blank())
  
plot_percent.mt <- plot_QC_paper(seu_object = seu_RNA, 
                                 feature = "percent.mt", 
                                 ytext = "% Mitochondrial counts",
                                 xtext = "Plate number",
                                 paneltitle = "Keep cells < 5 % mitochondrial genecounts",
                                 colorviolin = "dodgerblue2" ) +
                        geom_hline(yintercept = 5, color = "red", size = 0.3) +
                        theme(axis.title.x = element_blank())

plot_percent.rb <- plot_QC_paper(seu_object = seu_RNA, 
                                 feature = "percent.rb", 
                                 ytext = "% Ribosomal counts",
                                 xtext = "Plate number",
                                 paneltitle = "comparable % ribosomal counts in all plates",
                                 colorviolin = "dodgerblue2" ) +
                        theme(axis.title.x = element_blank())
  
  
plot_PROT_nCount.live <- plot_QC_paper(seu_object = seu.PROT_live, 
                                 feature = "nCount_PROT", 
                                 ytext =  "Total UMI counts per cell",
                                 xtext = "Plate number",
                                 paneltitle = "Keep cells > 1500 & < 9000 PROT counts",
                                 colorviolin = "deeppink3" ) +
                        geom_hline(yintercept = 1500, size = 0.3) +
                        geom_hline(yintercept = 9000, size = 0.3) +
                        theme(axis.title.x = element_blank())

plot_PROT_nCount.fix <- plot_QC_paper(seu_object = seu.PROT_fix, 
                                 feature = "nCount_PROT", 
                                 ytext =  "Total UMI counts per cell",
                                 xtext = "Plate number",
                                 paneltitle = "Keep cells > 2500 & < 20000 PROT counts",
                                 colorviolin = "deeppink3" ) +
                        geom_hline(yintercept = 2500, size = 0.3) +
                        geom_hline(yintercept = 20000, size = 0.3) +
                        theme(axis.title.x = element_blank())

plot_PROT_nproteins.live <- plot_QC_paper(seu_object = seu.PROT_live, 
                                 feature = "nFeature_PROT", 
                                 ytext =  "Total proteins per cell",
                                 xtext = "Plate number",
                                 paneltitle = "Keep cells >40 proteins",
                                 colorviolin = "deeppink3" ) +
                        geom_hline(yintercept = 40, size = 0.3, color = "red")+
                        theme(axis.title.x = element_blank())

plot_PROT_nproteins.fix <- plot_QC_paper(seu_object = seu.PROT_fix, 
                                 feature = "nFeature_PROT", 
                                 ytext =  "Total proteins per cell",
                                 xtext = "Plate number",
                                 paneltitle = "Keep cells >65 proteins",
                                 colorviolin = "deeppink3" ) +
                        geom_hline(yintercept = 65, size = 0.3, color = "red")+
                        theme(axis.title.x = element_blank())



plot.QC <- plot_grid(plot_RNA_nCount, 
                     plot_RNA_ngenes,  
                     plot_percent.mt, 
                     plot_percent.rb,
                     plot_PROT_nCount.live,
                     plot_PROT_nCount.fix,
                     plot_PROT_nproteins.live,
                     plot_PROT_nproteins.fix,  
                     labels = c('a', 'b', 'c','d' , 'e', 'f', 'g', 'h'), label_size = 10, ncol = 2)

ggsave(plot.QC, filename = "output/paper_figures/Suppl_QC_filters.pdf", width = 183, height = 200, units = "mm",  dpi = 300, useDingbats = FALSE)
ggsave(plot.QC, filename = "output/paper_figures/Suppl_QC_filters.png", width = 183, height = 200, units = "mm",  dpi = 300)
plot.QC

Supplementary Figure Thresholds for selection of high-quality samples and cells from the QuRIE-seq datasets.

  1. Based on the indicated cut-offs, high-quality cells are filtered for further analysis:
## Filter fixed protein dataset
seu.PROT.fix.subset <- subset(seu.PROT_fix, subset = nCount_PROT >= 2500 & nCount_PROT < 20000)

## Filter live-cell protein dataset
seu.PROT.live.subset <- subset(seu.PROT_live, subset = nCount_PROT >= 1500 & nCount_PROT <= 9000)


## RNA quality of fixed dataset is too low (very low gene numbers and counts). Therefore continue only with live-cell dataset.
seu.RNA_live <- subset(seu_RNA, idents = c(1586:1589), invert = TRUE)
seu.RNA_fix <- subset(seu_RNA, idents = c(1586:1589))

## Filter RNA live dataset
seu.RNA_live.subset <- subset(seu.RNA_live, subset = percent.mt <=5 & nFeature_RNA >= 300)
seu.RNA_fix.subset <- subset(seu.RNA_fix) #, subset = percent.mt <= 5 & nFeature_RNA >= 300 # Nofilter because RNA not taken along.
  1. Filter low-detected genes

Keep genes that are >1% cells detected.

## Additional filter features (genes) detected in 1% of cells
seu.RNA_live.subset <- CreateSeuratObject(seu.RNA_live.subset[["RNA"]]@counts, min.cells = round(ncol(seu.RNA_live.subset)/100)) ## keep features detected in 1% of cells
seu.RNA_fix.subset <- CreateSeuratObject(seu.RNA_fix.subset[["RNA"]]@counts, min.cells = round(ncol(seu.RNA_fix.subset)/100)) ## keep features detected in min 1% cells
  1. Merge Seurat objects from Protein & RNA modalities
## Merge Seurat objects live dataset
intersect <- colnames(seu.RNA_live.subset)[colnames(seu.RNA_live.subset) %in% colnames(seu.PROT.live.subset)]
intersect <- colnames(seu.PROT.live.subset)[colnames(seu.PROT.live.subset) %in% intersect]

seu.RNA_combined.live <- subset(seu.RNA_live.subset, cells = intersect )
Prot.live.intersect <- seu.PROT.live.subset@assays$PROT@counts[,colnames(seu.PROT.live.subset) %in% intersect]

seu.RNA_combined.live[["PROT"]] <- CreateAssayObject(counts = Prot.live.intersect)
seu.RNA_combined.live
An object of class Seurat 
10211 features across 1433 samples within 2 assays 
Active assay: RNA (10158 features, 0 variable features)
 1 other assay present: PROT
## fix dataset
intersect <- colnames(seu.RNA_fix.subset)[colnames(seu.RNA_fix.subset) %in% colnames(seu.PROT.fix.subset)]
intersect <- colnames(seu.PROT.fix.subset)[colnames(seu.PROT.fix.subset) %in% intersect]

seu.RNA_combined.fix <- subset(seu.RNA_fix.subset, cells = intersect )
Prot.fix.intersect <- seu.PROT.fix.subset@assays$PROT@counts[,colnames(seu.PROT.fix.subset) %in% intersect]

seu.RNA_combined.fix[["PROT"]] <- CreateAssayObject(counts = Prot.fix.intersect)
seu.RNA_combined.fix
An object of class Seurat 
5095 features across 1038 samples within 2 assays 
Active assay: RNA (5019 features, 0 variable features)
 1 other assay present: PROT
  1. Antibody quality (Non-detected proteins) filter
scale_x_reordered <- function(..., sep = "___") {
  reg <- paste0(sep, ".+$")
  ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
}

reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
  new_x <- paste(x, within, sep = sep)
  stats::reorder(new_x, by, FUN = fun)
}
PROT_tbl_subset.fix <- as.data.frame(seu.PROT.fix.subset@assays$PROT@counts) %>%
  mutate(protein = rownames(seu.PROT.fix.subset)) %>%
  dplyr::select(protein, everything()) %>%
  gather("cell", "count", 2:c(ncol(seu.PROT.fix.subset)+1)) %>%
  mutate(sample = gsub('.{5}$', '', cell) )
ggplot(PROT_tbl_subset.fix, aes(reorder_within(protein, count, sample), count)) +
     geom_boxplot(outlier.colour="black", outlier.shape=16,
             outlier.size=0.5, notch=FALSE)  +
     scale_x_reordered() +
     facet_wrap(.~ sample, ncol=4,scales = "free_y") + 
  theme_few()+
    theme(axis.text.x=element_text(angle=90,hjust=1,vjust=.5,colour='gray50')) +
  scale_y_log10() +
  add.textsize +
  coord_flip() +
  labs(y="count (log10)", x = "Antibody (ordered per plate)")

Supplementary Figure Fixed dataset antibody counts per plate (ordered from high to low per plate (1586-1589))

prot.median.fix <- aggregate(PROT_tbl_subset.fix[, 3], list(protein =PROT_tbl_subset.fix$protein), mean)

prot.fix.toremove <- prot.median.fix$protein[prot.median.fix$x <=0.2]

filtered.prot.counts <- seu.PROT.fix.subset[["PROT"]]@counts[!c(rownames(seu.PROT.fix.subset[["PROT"]]@counts) %chin% prot.fix.toremove),]

seu.PROT.fix.subset <- CreateSeuratObject(filtered.prot.counts, assay = "PROT")
PROT_tbl_subset.live <- as.data.frame(seu.RNA_combined.live@assays$PROT@counts) %>%
  mutate(protein = rownames(seu.RNA_combined.live[["PROT"]])) %>%
  dplyr::select(protein, everything()) %>%
  gather("cell", "count", 2:c(ncol(seu.RNA_combined.live[["PROT"]])+1)) %>%
  mutate(sample = gsub('.{5}$', '', cell) )
ggplot(PROT_tbl_subset.live, aes(reorder_within(protein, count, sample), count)) +
     geom_boxplot(outlier.colour="black", outlier.shape=16,
             outlier.size=0.5, notch=FALSE)  +
     scale_x_reordered() +
     facet_wrap(.~ sample, ncol=4,scales = "free_y") + 
  theme_few()+
    theme(axis.text.x=element_text(angle=90,hjust=1,vjust=.5,colour='gray50')) +
  scale_y_log10() +
  add.textsize +
  coord_flip() +
  labs(y="count (log10)", x = "Antibody (ordered per plate)")

Supplementary Figure Live dataset antibody counts per plate (ordered from high to low per plate (1586-1589))

prot.median.live <- aggregate(PROT_tbl_subset.live[, 3], list(protein =PROT_tbl_subset.live$protein), mean)

prot.live.toremove <- prot.median.live$protein[prot.median.live$x <1]

filtered.prot.counts.live <- seu.RNA_combined.live[["PROT"]]@counts[!c(rownames(seu.RNA_combined.live[["PROT"]]@counts) %chin% prot.live.toremove),]

seu.RNA_combined.live[["PROT"]] <- CreateAssayObject(counts = filtered.prot.counts.live)
  1. Add metadata to object:
## metadata import
metadata <- read_delim("data/metadata.txt", "\t", escape_double = FALSE, trim_ws = TRUE)
metadata$sample <- as.factor(metadata$sample)

## add metadata to fix dataset
meta.fix <- data.frame(seu.RNA_combined.fix@meta.data)  %>%
  mutate(sample = orig.ident ) %>%
  left_join(metadata) %>%
  mutate(group = sample) 
meta.fix<-as.data.frame(meta.fix)
rownames(meta.fix) <- rownames(data.frame(seu.RNA_combined.fix@meta.data) )
seu.RNA_combined.fix <- AddMetaData(object = seu.RNA_combined.fix, metadata = meta.fix)

#meta.fix <- data.frame(seu.RNA_combined.fix@meta.data) %>%
#  mutate(sample = rownames(seu.RNA_combined.fix@meta.data))


## add metadata to live dataset
meta.live <- data.frame(seu.RNA_combined.live@meta.data)  %>%
  mutate(sample = orig.ident ) %>%
  left_join(metadata) %>%
  mutate(group = sample) 
meta.live<-as.data.frame(meta.live)
rownames(meta.live) <- rownames(data.frame(seu.RNA_combined.live@meta.data) )
seu.RNA_combined.live <- AddMetaData(object = seu.RNA_combined.live, metadata = meta.live)

#meta.live <- data.frame(seu.RNA_combined.live@meta.data) %>%
#  mutate(sample = rownames(seu.RNA_combined.live@meta.data)) 

seu.RNA_combined.live[["percent.mt"]] <- PercentageFeatureSet(seu.RNA_combined.live, pattern = "^MT")

seu.RNA_combined.fix[["percent.mt"]] <- PercentageFeatureSet(seu.RNA_combined.fix, pattern = "^MT")

Normalize and Scale

## fix data normalize RNA
DefaultAssay(seu.RNA_combined.fix) <- 'RNA'
seu.RNA_combined.fix <- SCTransform(seu.RNA_combined.fix, assay = "RNA", new.assay.name = "SCT", vars.to.regress = c("nCount_RNA", "percent.mt", "plate"), return.only.var.genes = FALSE, verbose = FALSE)

# Add some metadata to normalized data (ncounts & percent mt)
seu.RNA_combined.fix <- AddMetaData(seu.RNA_combined.fix, as.data.frame(seu.RNA_combined.fix@assays$SCT@counts) %>% summarise_all(funs(sum)) %>% unlist(), col.name = "nCount_RNA_SCT")

seu.RNA_combined.fix <- PercentageFeatureSet(seu.RNA_combined.fix, pattern = "^MT\\.|^MTRN", col.name = "percent.mt.aftersct", assay = "SCT")

## Fixed dataset normalize protein
DefaultAssay(seu.RNA_combined.fix) <- 'PROT'
VariableFeatures(seu.RNA_combined.fix) <- rownames(seu.RNA_combined.fix[["PROT"]])
seu.RNA_combined.fix <- NormalizeData(seu.RNA_combined.fix, normalization.method = 'CLR', margin = 2, assay = "PROT") %>% 
  ScaleData(vars.to.regress = c("nCount_PROT", "plate"))

## live data normalize RNA
DefaultAssay(seu.RNA_combined.live) <- 'RNA'
seu.RNA_combined.live <- SCTransform(seu.RNA_combined.live, assay = "RNA", new.assay.name = "SCT", vars.to.regress = c("nCount_RNA", "percent.mt", "plate"), return.only.var.genes = FALSE, verbose = FALSE)

# Add some metadata to normalized data (ncounts & percent mt)
seu.RNA_combined.live <- AddMetaData(seu.RNA_combined.live, as.data.frame(seu.RNA_combined.live@assays$SCT@counts) %>% summarise_all(funs(sum)) %>% unlist(), col.name = "nCount_RNA_SCT")

seu.RNA_combined.live <- PercentageFeatureSet(seu.RNA_combined.live, pattern = "^MT\\.|^MTRN", col.name = "percent.mt.aftersct", assay = "SCT")

## live normalize & scale protein data 
DefaultAssay(seu.RNA_combined.live) <- 'PROT'
VariableFeatures(seu.RNA_combined.live) <- rownames(seu.RNA_combined.live[["PROT"]])
seu.RNA_combined.live <- NormalizeData(seu.RNA_combined.live, normalization.method = 'CLR', margin = 2, assay = "PROT") %>% 
  ScaleData(vars.to.regress = c("nCount_PROT", "plate"))

Filtered dataset properties

Overview of the number of cells and data properties of all plates.

Live-cells RNA & surface protein dataset

seu.RNA_combined.live
An object of class Seurat 
20366 features across 1433 samples within 3 assays 
Active assay: PROT (50 features, 50 variable features)
 2 other assays present: RNA, SCT

Table Overview of per plate properties after filtering.

kable(seu.RNA_combined.live@meta.data %>% 
        group_by(donor,plate) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
donor plate Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Median counts PROT Number proteins
D32 P_1578 216 1624 556 1.14 3842 46
D32 P_1579 293 2333 861 1.21 3601 45
D33 P_1580 274 2888 1040 1.18 3908 47
D33 P_1584 184 3688 1220 1.21 4383 48
D40 P_1582 231 1150 524 1.11 3575 47
D40 P_1585 235 3706 1133 1.24 3831 47

Table Overview of per donor properties after filtering.

kable(seu.RNA_combined.live@meta.data %>% 
        group_by(donor) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
donor Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Median counts PROT Number proteins
D32 509 2008 732 1.18 3693 46
D33 458 3168 1122 1.18 4098 47
D40 466 1817 731 1.17 3694 47

Fixed cells intracellular proteins dataset

seu.RNA_combined.fix
An object of class Seurat 
10114 features across 1038 samples within 3 assays 
Active assay: PROT (76 features, 76 variable features)
 2 other assays present: RNA, SCT

Table Overview of per plate properties after filtering.

kable(seu.RNA_combined.fix@meta.data %>% 
        group_by(donor,plate) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
donor plate Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Median counts PROT Number proteins
D33 P_1586 290 280 106 0.52 7664 72
D33 P_1587 232 266 120 0.99 8492 72
D40 P_1588 254 322 140 0.57 7250 72
D40 P_1589 262 272 116 0.81 8704 72

Table Overview of per donor properties after filtering.

kable(seu.RNA_combined.fix@meta.data %>% 
        group_by(donor) %>% 
        summarise(`Number of cells` = round(n(),0),
                  `Median counts RNA` = round(median(nCount_RNA),0),
                  `Median Number genes` = round(median(nFeature_RNA),0),
                  `Median Mitochondrial counts (Median %)` = round(median(percent.mt),2), 
                  `Median counts PROT` = round(median(nCount_PROT),0),
                  `Number proteins` = round(median(nFeature_PROT),0)
                  )) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
donor Number of cells Median counts RNA Median Number genes Median Mitochondrial counts (Median %) Median counts PROT Number proteins
D33 522 272 112 0.73 7924 72
D40 516 292 126 0.64 7942 72

Save dataset

Seurat object with filtered cells and normalized counts is stored in “output/seu.fix_norm.rds” (intracellular protein modality) and “output/seu.live_norm.rds”(RNA and surface protein modalities).

## Save Seurat objects
saveRDS(seu.RNA_combined.fix, file = "output/seu.fix_norm.rds")
saveRDS(seu.RNA_combined.live, file = "output/seu.live_norm.rds")

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_Netherlands.1252  LC_CTYPE=English_Netherlands.1252   
[3] LC_MONETARY=English_Netherlands.1252 LC_NUMERIC=C                        
[5] LC_TIME=English_Netherlands.1252    

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

other attached packages:
 [1] ggupset_0.3.0          RColorBrewer_1.1-2     clusterProfiler_3.18.1
 [4] enrichplot_1.10.2      UCell_1.0.0            data.table_1.13.6     
 [7] scales_1.1.1           cowplot_1.1.1          ggthemes_4.2.4        
[10] kableExtra_1.3.1       knitr_1.31             org.Hs.eg.db_3.12.0   
[13] AnnotationDbi_1.52.0   IRanges_2.24.1         S4Vectors_0.28.1      
[16] Biobase_2.50.0         BiocGenerics_0.36.0    forcats_0.5.1         
[19] stringr_1.4.0          dplyr_1.0.3            purrr_0.3.4           
[22] readr_1.4.0            tidyr_1.1.2            tibble_3.0.5          
[25] ggplot2_3.3.3          tidyverse_1.3.0        Matrix_1.2-18         
[28] SeuratObject_4.0.0     Seurat_4.0.0           workflowr_1.6.2       

loaded via a namespace (and not attached):
  [1] reticulate_1.18      tidyselect_1.1.0     RSQLite_2.2.3       
  [4] htmlwidgets_1.5.3    grid_4.0.3           BiocParallel_1.24.1 
  [7] Rtsne_0.15           scatterpie_0.1.5     munsell_0.5.0       
 [10] codetools_0.2-16     ica_1.0-2            future_1.21.0       
 [13] miniUI_0.1.1.1       withr_2.4.1          colorspace_2.0-0    
 [16] GOSemSim_2.16.1      highr_0.8            rstudioapi_0.13     
 [19] ROCR_1.0-11          tensor_1.5           DOSE_3.16.0         
 [22] listenv_0.8.0        labeling_0.4.2       git2r_0.28.0        
 [25] polyclip_1.10-0      bit64_4.0.5          farver_2.0.3        
 [28] downloader_0.4       rprojroot_2.0.2      parallelly_1.23.0   
 [31] vctrs_0.3.6          generics_0.1.0       xfun_0.23           
 [34] R6_2.5.0             graphlayouts_0.7.1   spatstat.utils_2.1-0
 [37] cachem_1.0.1         fgsea_1.16.0         assertthat_0.2.1    
 [40] promises_1.1.1       ggraph_2.0.5         gtable_0.3.0        
 [43] globals_0.14.0       goftest_1.2-2        tidygraph_1.2.0     
 [46] rlang_0.4.10         splines_4.0.3        lazyeval_0.2.2      
 [49] broom_0.7.3          BiocManager_1.30.10  yaml_2.2.1          
 [52] reshape2_1.4.4       abind_1.4-5          modelr_0.1.8        
 [55] backports_1.2.1      httpuv_1.5.5         qvalue_2.22.0       
 [58] tools_4.0.3          ellipsis_0.3.1       ggridges_0.5.3      
 [61] Rcpp_1.0.6           plyr_1.8.6           ps_1.5.0            
 [64] rpart_4.1-15         deldir_0.2-10        pbapply_1.4-3       
 [67] viridis_0.5.1        zoo_1.8-8            haven_2.3.1         
 [70] ggrepel_0.9.1        cluster_2.1.0        fs_1.5.0            
 [73] magrittr_2.0.1       scattermore_0.7      DO.db_2.9           
 [76] lmtest_0.9-38        reprex_1.0.0         RANN_2.6.1          
 [79] whisker_0.4          fitdistrplus_1.1-3   matrixStats_0.57.0  
 [82] hms_1.0.0            patchwork_1.1.1      mime_0.9            
 [85] evaluate_0.14        xtable_1.8-4         readxl_1.3.1        
 [88] gridExtra_2.3        compiler_4.0.3       shadowtext_0.0.7    
 [91] KernSmooth_2.23-17   crayon_1.3.4         htmltools_0.5.1.1   
 [94] mgcv_1.8-33          later_1.1.0.1        lubridate_1.7.9.2   
 [97] DBI_1.1.1            tweenr_1.0.1         dbplyr_2.0.0        
[100] MASS_7.3-53          cli_2.2.0            igraph_1.2.6        
[103] pkgconfig_2.0.3      rvcheck_0.1.8        plotly_4.9.3        
[106] xml2_1.3.2           webshot_0.5.2        rvest_0.3.6         
[109] digest_0.6.27        sctransform_0.3.2    RcppAnnoy_0.0.18    
[112] spatstat.data_2.1-0  rmarkdown_2.6        cellranger_1.1.0    
[115] leiden_0.3.7         fastmatch_1.1-0      uwot_0.1.10         
[118] shiny_1.6.0          lifecycle_0.2.0      nlme_3.1-149        
[121] jsonlite_1.7.2       viridisLite_0.3.0    fansi_0.4.2         
[124] pillar_1.4.7         lattice_0.20-41      fastmap_1.1.0       
[127] httr_1.4.2           survival_3.2-7       GO.db_3.12.1        
[130] glue_1.4.2           spatstat_1.64-1      png_0.1-7           
[133] bit_4.0.4            ggforce_0.3.2        stringi_1.5.3       
[136] blob_1.2.1           memoise_2.0.0        irlba_2.3.3         
[139] future.apply_1.7.0