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Last updated: 2022-05-09

Checks: 6 1

Knit directory: humanCardiacFibroblasts/

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File	Version	Author	Date	Message
Rmd	70e878c	mluetge	2022-05-02	update grand application plots
html	70e878c	mluetge	2022-05-02	update grand application plots

---

load packages

suppressPackageStartupMessages({
  library(SingleCellExperiment)
  library(tidyverse)
  library(Seurat)
  library(magrittr)
  library(dplyr)
  library(purrr)
  library(ggplot2)
  library(here)
  library(runSeurat3)
  library(ggsci)
  library(ggpubr)
  library(pheatmap)
  library(viridis)
  library(sctransform)
})

sign plot funct

## adapted from CellMixS
visGroup_adapt <- function (sce,group,dim_red = "TSNE",col_group=pal_nejm()(8)) 
{
    if (!is(sce, "SingleCellExperiment")) {
        stop("Error:'sce' must be a 'SingleCellExperiment' object.")
    }
    if (!group %in% names(colData(sce))) {
        stop("Error: 'group' variable must be in 'colData(sce)'")
    }
    cell_names <- colnames(sce)
    if (!dim_red %in% "TSNE") {
        if (!dim_red %in% reducedDimNames(sce)) {
            stop("Please provide a dim_red method listed in reducedDims of sce")
        }
        red_dim <- as.data.frame(reducedDim(sce, dim_red))
    }
    else {
        if (!"TSNE" %in% reducedDimNames(sce)) {
            if ("logcounts" %in% names(assays(sce))) {
                sce <- runTSNE(sce)
            }
            else {
                sce <- runTSNE(sce, exprs_values = "counts")
            }
        }
        red_dim <- as.data.frame(reducedDim(sce, "TSNE"))
    }
    colnames(red_dim) <- c("red_dim1", "red_dim2")
    df <- data.frame(sample_id = cell_names, group_var = colData(sce)[, 
        group], red_Dim1 = red_dim$red_dim1, red_Dim2 = red_dim$red_dim2)
    t <- ggplot(df, aes_string(x = "red_Dim1", y = "red_Dim2")) + 
        xlab(paste0(dim_red, "_1")) + ylab(paste0(dim_red, "_2")) + 
        theme_void() + theme(aspect.ratio = 1,
                             panel.grid.minor = element_blank(), 
        panel.grid.major = element_line(color = "grey", size = 0.3))
    t_group <- t + geom_point(size = 1.5, alpha = 0.8,
                              aes_string(color = "group_var")) + 
        guides(color = guide_legend(override.aes = list(size = 1), 
            title = group)) + ggtitle(group)
    if (is.numeric(df$group_var)) {
        t_group <- t_group + scale_color_viridis(option = "D")
    }
    else {
        t_group <- t_group + scale_color_manual(values = col_group)
    }
    t_group
}

set dir

basedir <- here()
seurat <- readRDS(file = paste0(basedir, 
                              "/data/humanHearts_merged_seurat.rds"))
seurat$ID[which(seurat$ID == "ID23_25")] <- "ID2325"

## subset on sel patients
selPat <- c("ID2325", "ID28", "ID30", "ID31", "ID21", "ID26", "ECMO4", "ID29")
seurat <- subset(seurat, ID %in% selPat)
#seurat <- rerunSeurat3(seurat)

seurat$grp <- "normal"
seurat$grp[which(seurat$ID %in% c("ID21", "ID26"))] <- "HF"
seurat$grp[which(seurat$ID %in% c("ID30", "ID31", "ID29"))] <- "Myocarditis"

#Idents(seurat) <- seurat$RNA_snn_res.0.25

## integrate data across patients
Idents(seurat) <- seurat$ID

seurat.list <- SplitObject(object = seurat, split.by = "ID")
for (i in 1:length(x = seurat.list)) {
    seurat.list[[i]] <- NormalizeData(object = seurat.list[[i]],
                                      verbose = FALSE)
    seurat.list[[i]] <- FindVariableFeatures(object = seurat.list[[i]], 
        selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}

seurat.anchors <- FindIntegrationAnchors(object.list = seurat.list, dims = 1:15)
seurat.int <- IntegrateData(anchorset = seurat.anchors, dims = 1:15)
DefaultAssay(object = seurat.int) <- "integrated"

# rerun seurat
seurat.int <- ScaleData(object = seurat.int, verbose = FALSE,
                        features = rownames(seurat.int))
seurat.int <- RunPCA(object = seurat.int, npcs = 20, verbose = FALSE)
seurat.int <- RunTSNE(object = seurat.int, reduction = "pca", dims = 1:20)
seurat.int <- RunUMAP(object = seurat.int, reduction = "pca", dims = 1:20)

seurat.int <- FindNeighbors(object = seurat.int, reduction = "pca", dims = 1:20)
res <- c(0.6,0.8,0.4,0.25)
for(i in 1:length(res)){
  seurat.int <- FindClusters(object = seurat.int, resolution = res[i],
                             random.seed = 1234)
}

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11281
Number of edges: 505870

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9194
Number of communities: 17
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11281
Number of edges: 505870

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9017
Number of communities: 19
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11281
Number of edges: 505870

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9402
Number of communities: 14
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11281
Number of edges: 505870

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9587
Number of communities: 12
Elapsed time: 1 seconds

DefaultAssay(object = seurat.int) <- "RNA"
seurat <- seurat.int
remove(seurat.int)

color vectors

colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colLoc <- pal_npg()(length(unique(seurat$location)))
colBatch <- c(pal_jco()(10), pal_npg()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_futurama()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colGrp <- c("#b6bcbb", "#a32d25", "#2544a3")



names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colLoc) <- unique(seurat$location)
names(colBatch) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)
names(colGrp) <- c("normal", "Myocarditis", "HF")

vis data

clusters

DimPlot(seurat, reduction = "umap", cols=colPal)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis cluster-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

DimPlot(seurat, reduction = "umap", cols=colPal,
        pt.size=0.6)+
  theme_void()

Past versions of “vis cluster-2.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

technique

DimPlot(seurat, reduction = "umap", group.by = "technique", cols=colTec)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis tecnique-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

Sample

DimPlot(seurat, reduction = "umap", group.by = "dataset", cols=colSmp)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis sample-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

ID

DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colBatch)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis ID-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colBatch,
        pt.size=0.6, shuffle = T)+
  theme_void()

Past versions of “vis ID-2.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

Origin

DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis origin-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

isolation

DimPlot(seurat, reduction = "umap", group.by = "isolation", cols=colIso)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis isolation-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

location

DimPlot(seurat, reduction = "umap", group.by = "location", cols=colLoc)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis location-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

grp

DimPlot(seurat, reduction = "umap", group.by = "grp", cols=colGrp)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of “vis grp-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

DimPlot(seurat, reduction = "umap", group.by = "grp", cols=colGrp,
        pt.size=0.6)+
  theme_void()

Past versions of “vis grp-2.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

grp without HF

seuratSub <- subset(seurat, grp == "HF", invert=T)


DimPlot(seuratSub, reduction = "umap", group.by = "grp", cols=colGrp,
        pt.size=0.6, order = "Myocarditis")+
  theme_void()

Past versions of “vis grp without HF-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

grp without M

seuratSub2 <- subset(seurat, grp == "Myocarditis", invert=T)


DimPlot(seuratSub2, reduction = "umap", group.by = "grp", cols=colGrp,
        pt.size=0.6, order = "HF")+
  theme_void()

Past versions of “vis grp without M-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

DE genes grp

Idents(seurat) <- seurat$grp
DEgenes <- FindAllMarkers(object = seurat, assay ="RNA",
                                     only.pos = TRUE, min.pct = 0.25,
                                     logfc.threshold = 0.25,
                                     test.use = "wilcox")

top 20 marker genes per grp

cluster <- levels(seurat)
selGenesAll <- DEgenes %>% group_by(cluster) %>% 
  top_n(-20, p_val_adj) %>% 
  top_n(20, avg_log2FC)
selGenesAll <- selGenesAll %>% mutate(geneIDval=gsub("^.*\\.", "", gene)) %>% filter(nchar(geneIDval)>1)

Idents(seurat) <- seurat$grp
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
                  colVecIdent = colGrp, 
                  ordVec=levels(seurat),
                  gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                  cr=T, condCol=F)

Past versions of “top DE genes EMB29-1.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

Idents(seurat) <- seurat$ID
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
                  colVecIdent = colBatch, 
                  ordVec=levels(seurat),
                  gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                  cr=T, condCol=F)

Past versions of “top DE genes EMB29-2.png”

Version	Author	Date
70e878c	mluetge	2022-05-02

cnt Tab

table(seurat$grp)

         HF Myocarditis      normal 
       1064        2670        7547 

table(seurat$ID)

 ECMO4   ID21 ID2325   ID26   ID28   ID29   ID30   ID31 
  3162    826   3131    238   1254   1242    236   1192 

table(seurat$ID, seurat$grp)

        
           HF Myocarditis normal
  ECMO4     0           0   3162
  ID21    826           0      0
  ID2325    0           0   3131
  ID26    238           0      0
  ID28      0           0   1254
  ID29      0        1242      0
  ID30      0         236      0
  ID31      0        1192      0

signature cut 1.5

split by grp

signDat <- read_delim(file = paste0(basedir,
                    "/data/SelSignaturesTreat.txt"),
                    delim = "\t")
genes <- data.frame(geneID=rownames(seurat)) %>% 
  mutate(gene=gsub("^.*\\.", "", geneID))
signDat <- signDat %>% left_join(.,genes, by="gene")
allSign <- unique(signDat$signature)

DefaultAssay(object = seurat) <- "integrated"
sce2 <- as.SingleCellExperiment(seurat)

DefaultAssay(object = seurat) <- "RNA"
sce <- as.SingleCellExperiment(seurat)
reducedDims(sce) <- list(PCA=reducedDim(sce2, "PCA"),
                         TSNE=reducedDim(sce2, "TSNE"),
                         UMAP=reducedDim(sce2, "UMAP"))

treatGrps <- unique(sce$grp)

cutOff <- 1.5
pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))

lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$grp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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across all

#sce <- as.SingleCellExperiment(seurat)

cutOff <- 1.5
pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))

lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
    p <- visGroup_adapt(sceSub, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - across all')) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p

})

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save objects

Idents(seurat) <- Idents(seurat) <- seurat$integrated_snn_res.0.25
saveRDS(seurat, file = paste0(basedir, 
                              "/data/humanHearts_intAcrossPat_Normal_HF_Myocarditis.rds"))

saveRDS(seuratSub, file = paste0(basedir, 
                              "/data/humanHearts_intAcrossPat_Normal_Myocarditis.rds"))

saveRDS(seuratSub2, file = paste0(basedir, 
                              "/data/humanHearts_intAcrossPat_Normal_HF.rds"))

write.table(DEgenes,
            file=paste0(basedir,
                        "/data/humanHearts_intAcrossPat_NORMALvsHFvsMYO_overallDEGenes.txt"),
            row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")

session info

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] sctransform_0.3.3           viridis_0.6.2              
 [3] viridisLite_0.4.0           pheatmap_1.0.12            
 [5] ggpubr_0.4.0                ggsci_2.9                  
 [7] runSeurat3_0.1.0            here_1.0.1                 
 [9] magrittr_2.0.3              sp_1.4-7                   
[11] SeuratObject_4.1.0          Seurat_4.1.1               
[13] forcats_0.5.1               stringr_1.4.0              
[15] dplyr_1.0.9                 purrr_0.3.4                
[17] readr_2.1.2                 tidyr_1.2.0                
[19] tibble_3.1.7                ggplot2_3.3.6              
[21] tidyverse_1.3.1             SingleCellExperiment_1.14.1
[23] SummarizedExperiment_1.22.0 Biobase_2.52.0             
[25] GenomicRanges_1.44.0        GenomeInfoDb_1.28.4        
[27] IRanges_2.26.0              S4Vectors_0.30.2           
[29] BiocGenerics_0.38.0         MatrixGenerics_1.4.3       
[31] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.24        tidyselect_1.1.2      
  [4] htmlwidgets_1.5.4      grid_4.1.0             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-2             
 [10] future_1.25.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.10.0      
 [16] highr_0.9              knitr_1.39             rstudioapi_0.13       
 [19] ROCR_1.0-11            ggsignif_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.6 polyclip_1.10-0        bit64_4.0.5           
 [28] farver_2.1.0           rprojroot_2.0.3        parallelly_1.31.1     
 [31] vctrs_0.4.1            generics_0.1.2         xfun_0.30             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] DelayedArray_0.18.0    assertthat_0.2.1       vroom_1.5.7           
 [40] promises_1.2.0.1       scales_1.2.0           rgeos_0.5-9           
 [43] gtable_0.3.0           globals_0.15.0         goftest_1.2-3         
 [46] workflowr_1.7.0        rlang_1.0.2            splines_4.1.0         
 [49] rstatix_0.7.0          lazyeval_0.2.2         spatstat.geom_2.4-0   
 [52] broom_0.8.0            yaml_2.3.5             reshape2_1.4.4        
 [55] abind_1.4-5            modelr_0.1.8           backports_1.4.1       
 [58] httpuv_1.6.5           tools_4.1.0            ellipsis_0.3.2        
 [61] spatstat.core_2.4-2    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [64] ggridges_0.5.3         Rcpp_1.0.8.3           plyr_1.8.7            
 [67] zlibbioc_1.38.0        RCurl_1.98-1.6         rpart_4.1.16          
 [70] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [73] zoo_1.8-10             haven_2.5.0            ggrepel_0.9.1         
 [76] cluster_2.1.3          fs_1.5.2               data.table_1.14.2     
 [79] RSpectra_0.16-1        scattermore_0.8        lmtest_0.9-40         
 [82] reprex_2.0.1           RANN_2.6.1             whisker_0.4           
 [85] fitdistrplus_1.1-8     hms_1.1.1              patchwork_1.1.1       
 [88] mime_0.12              evaluate_0.15          xtable_1.8-4          
 [91] readxl_1.4.0           gridExtra_2.3          compiler_4.1.0        
 [94] KernSmooth_2.23-20     crayon_1.5.1           htmltools_0.5.2       
 [97] mgcv_1.8-40            later_1.3.0            tzdb_0.3.0            
[100] lubridate_1.8.0        DBI_1.1.2              dbplyr_2.1.1          
[103] MASS_7.3-57            Matrix_1.4-1           car_3.0-13            
[106] cli_3.3.0              igraph_1.3.1           pkgconfig_2.0.3       
[109] plotly_4.10.0          spatstat.sparse_2.1-1  xml2_1.3.3            
[112] bslib_0.3.1            XVector_0.32.0         rvest_1.0.2           
[115] digest_0.6.29          RcppAnnoy_0.0.19       spatstat.data_2.2-0   
[118] rmarkdown_2.14         cellranger_1.1.0       leiden_0.3.10         
[121] uwot_0.1.11            shiny_1.7.1            lifecycle_1.0.1       
[124] nlme_3.1-157           jsonlite_1.8.0         carData_3.0-5         
[127] limma_3.48.3           fansi_1.0.3            pillar_1.7.0          
[130] lattice_0.20-45        fastmap_1.1.0          httr_1.4.3            
[133] survival_3.3-1         glue_1.6.2             png_0.1-7             
[136] bit_4.0.4              stringi_1.7.6          sass_0.4.1            
[139] irlba_2.3.5            future.apply_1.9.0    

date()

[1] "Mon May  9 15:30:27 2022"

Session information

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] sctransform_0.3.3           viridis_0.6.2              
 [3] viridisLite_0.4.0           pheatmap_1.0.12            
 [5] ggpubr_0.4.0                ggsci_2.9                  
 [7] runSeurat3_0.1.0            here_1.0.1                 
 [9] magrittr_2.0.3              sp_1.4-7                   
[11] SeuratObject_4.1.0          Seurat_4.1.1               
[13] forcats_0.5.1               stringr_1.4.0              
[15] dplyr_1.0.9                 purrr_0.3.4                
[17] readr_2.1.2                 tidyr_1.2.0                
[19] tibble_3.1.7                ggplot2_3.3.6              
[21] tidyverse_1.3.1             SingleCellExperiment_1.14.1
[23] SummarizedExperiment_1.22.0 Biobase_2.52.0             
[25] GenomicRanges_1.44.0        GenomeInfoDb_1.28.4        
[27] IRanges_2.26.0              S4Vectors_0.30.2           
[29] BiocGenerics_0.38.0         MatrixGenerics_1.4.3       
[31] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.24        tidyselect_1.1.2      
  [4] htmlwidgets_1.5.4      grid_4.1.0             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-2             
 [10] future_1.25.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.10.0      
 [16] highr_0.9              knitr_1.39             rstudioapi_0.13       
 [19] ROCR_1.0-11            ggsignif_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.6 polyclip_1.10-0        bit64_4.0.5           
 [28] farver_2.1.0           rprojroot_2.0.3        parallelly_1.31.1     
 [31] vctrs_0.4.1            generics_0.1.2         xfun_0.30             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] DelayedArray_0.18.0    assertthat_0.2.1       vroom_1.5.7           
 [40] promises_1.2.0.1       scales_1.2.0           rgeos_0.5-9           
 [43] gtable_0.3.0           globals_0.15.0         goftest_1.2-3         
 [46] workflowr_1.7.0        rlang_1.0.2            splines_4.1.0         
 [49] rstatix_0.7.0          lazyeval_0.2.2         spatstat.geom_2.4-0   
 [52] broom_0.8.0            yaml_2.3.5             reshape2_1.4.4        
 [55] abind_1.4-5            modelr_0.1.8           backports_1.4.1       
 [58] httpuv_1.6.5           tools_4.1.0            ellipsis_0.3.2        
 [61] spatstat.core_2.4-2    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [64] ggridges_0.5.3         Rcpp_1.0.8.3           plyr_1.8.7            
 [67] zlibbioc_1.38.0        RCurl_1.98-1.6         rpart_4.1.16          
 [70] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [73] zoo_1.8-10             haven_2.5.0            ggrepel_0.9.1         
 [76] cluster_2.1.3          fs_1.5.2               data.table_1.14.2     
 [79] RSpectra_0.16-1        scattermore_0.8        lmtest_0.9-40         
 [82] reprex_2.0.1           RANN_2.6.1             whisker_0.4           
 [85] fitdistrplus_1.1-8     hms_1.1.1              patchwork_1.1.1       
 [88] mime_0.12              evaluate_0.15          xtable_1.8-4          
 [91] readxl_1.4.0           gridExtra_2.3          compiler_4.1.0        
 [94] KernSmooth_2.23-20     crayon_1.5.1           htmltools_0.5.2       
 [97] mgcv_1.8-40            later_1.3.0            tzdb_0.3.0            
[100] lubridate_1.8.0        DBI_1.1.2              dbplyr_2.1.1          
[103] MASS_7.3-57            Matrix_1.4-1           car_3.0-13            
[106] cli_3.3.0              igraph_1.3.1           pkgconfig_2.0.3       
[109] plotly_4.10.0          spatstat.sparse_2.1-1  xml2_1.3.3            
[112] bslib_0.3.1            XVector_0.32.0         rvest_1.0.2           
[115] digest_0.6.29          RcppAnnoy_0.0.19       spatstat.data_2.2-0   
[118] rmarkdown_2.14         cellranger_1.1.0       leiden_0.3.10         
[121] uwot_0.1.11            shiny_1.7.1            lifecycle_1.0.1       
[124] nlme_3.1-157           jsonlite_1.8.0         carData_3.0-5         
[127] limma_3.48.3           fansi_1.0.3            pillar_1.7.0          
[130] lattice_0.20-45        fastmap_1.1.0          httr_1.4.3            
[133] survival_3.3-1         glue_1.6.2             png_0.1-7             
[136] bit_4.0.4              stringi_1.7.6          sass_0.4.1            
[139] irlba_2.3.5            future.apply_1.9.0