Last updated: 2022-02-22

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File Version Author Date Message
Rmd 5418dcd toobiwankenobi 2022-02-22 add remaining pngs and new .htmls
html 5418dcd toobiwankenobi 2022-02-22 add remaining pngs and new .htmls
Rmd 64e5fde toobiwankenobi 2022-02-16 change order and naming of supp fig files
Rmd f9a3a83 toobiwankenobi 2022-02-08 clean repo for release
Rmd fa0f601 toobiwankenobi 2022-02-06 clean Supp Fig code
Rmd b20b6fb toobiwankenobi 2022-02-02 update code for Supp Figures
Rmd d6a945a toobiwankenobi 2021-12-06 updated figures
Rmd 3da15db toobiwankenobi 2021-11-24 changes for revision
Rmd 434eee4 toobiwankenobi 2021-09-23 Figure adaptions and new Supp Figure with gates
Rmd 545c207 toobiwankenobi 2020-12-22 clean up branch
Rmd 9442cb9 toobiwankenobi 2020-12-22 add all new files
Rmd d8c7699 Tobias Hoch 2020-10-23 adapt figure 1 and supp fig 6
Rmd 77466b7 Tobias Hoch 2020-10-22 work on subfigures

Introduction

This script generates plots for Supplementary Figure 6.

Preparations

knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

Load libraries

sapply(list.files("code/helper_functions", full.names = TRUE), source)
        code/helper_functions/calculateSummary.R
value   ?                                       
visible FALSE                                   
        code/helper_functions/censor_dat.R
value   ?                                 
visible FALSE                             
        code/helper_functions/detect_mRNA_expression.R
value   ?                                             
visible FALSE                                         
        code/helper_functions/DistanceToClusterCenter.R
value   ?                                              
visible FALSE                                          
        code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value   ?                                  ?                                
visible FALSE                              FALSE                            
        code/helper_functions/getInfoFromString.R
value   ?                                        
visible FALSE                                    
        code/helper_functions/getSpotnumber.R
value   ?                                    
visible FALSE                                
        code/helper_functions/plotCellCounts.R
value   ?                                     
visible FALSE                                 
        code/helper_functions/plotCellFractions.R
value   ?                                        
visible FALSE                                    
        code/helper_functions/plotDist.R code/helper_functions/read_Data.R
value   ?                                ?                                
visible FALSE                            FALSE                            
        code/helper_functions/scatter_function.R
value   ?                                       
visible FALSE                                   
        code/helper_functions/sceChecks.R
value   ?                                
visible FALSE                            
        code/helper_functions/validityChecks.R
value   ?                                     
visible FALSE                                 
library(cytomapper)
library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table) 
library(ggrastr)
library(ggplot2)
library(colorRamps)
library(RColorBrewer)
library(gridExtra)
library(ggpmisc)
library(ComplexHeatmap)
library(scater)
library(dittoSeq)
library(ggbeeswarm)
library(corrplot)
library(ggpubr)
library(cowplot)
library(circlize)
library(ggrepel)
library(rstatix)
library(ape)
library(biomaRt)

Load Data

# SCE object
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")

sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]

Analysis

Supp Fig 5A

# sample 300 cells per cell type
sce_prot_sub <- sce_prot[,sce_prot$layer_1_gated != "unlabelled"]

set.seed(2345)
# sub-sample 500 cells
sample <- data.frame(colData(sce_prot_sub)) %>%
  group_by(celltype) %>%
  slice_sample(n=300)

# unique cellIDs
sample <- sample[sample$cellID %in% unique(sample$cellID),]

cur_sce <- sce_prot[,sce_prot$cellID %in% sample$cellID]

good_markers <- rownames(sce_prot)[rowData(sce_prot)$good_marker]

colors <- metadata(sce_prot)$colour_vector$celltype
colors <- colors[c("B cell", "BnT cell", "CD4+ T cell", "CD8+ T cell", "FOXP3+ T cell", "Macrophage", "Neutrophil", "pDC", "Stroma", "Tumor", "unknown")]

dittoHeatmap(cur_sce,
             genes = good_markers, 
             assay = "asinh",
            annot.by = c("celltype"),
            show_colnames = FALSE,
            cluster_rows = TRUE,
            annot.colors = colors,
            heatmap.colors = colorRampPalette(c("dark blue", "white", "dark red"))(100),
            breaks = seq(-3,3, length.out = 101),
            use_raster=TRUE)

Version Author Date
235386f toobiwankenobi 2022-02-22

Supp Figure 6B

set.seed(2345)

sce_rna_sub <- sce_rna[,sce_rna$layer_1_gated != "unlabelled"]

# remove unkown - not enough cells
sce_rna_sub <- sce_rna_sub[,sce_rna_sub$celltype != "unknown"]

# sub-sample 500 cells
sample <- data.frame(colData(sce_rna_sub)) %>%
  group_by(celltype) %>%
  slice_sample(n=300)

# unique cellIDs
sample <- sample[sample$cellID %in% unique(sample$cellID),]

cur_sce <- sce_rna[,sce_rna$cellID %in% unique(sample$cellID)]

good_markers <- rownames(sce_rna)[rowData(sce_rna)$good_marker]
colors <- metadata(sce_rna)$colour_vector$celltype[]
colors <- colors[c("CD38", "CD8- T cell", "CD8+ T cell", "HLA-DR", "Macrophage", "Neutrophil", "Stroma", "Tumor", "Vasculature")]

dittoHeatmap(cur_sce, genes = good_markers, assay = "asinh",
            annot.by = c("celltype"),
            show_colnames = FALSE,
            cluster_rows = TRUE,
            annot.colors = colors,
            heatmap.colors = colorRampPalette(c("dark blue", "white", "dark red"))(100),
            breaks = seq(-3,3, length.out = 101),
            use_raster=TRUE)

Version Author Date
235386f toobiwankenobi 2022-02-22

Supp Figure 6C and 5D

These plots are generated after the randomForest classification. For this, see files 04_1_RNA_celltype_classification.rmd and 04_1_Protein_celltype_classification.rmd

Supp Figure 6E

# rna data
cur_rna <- data.frame(colData(sce_rna))

# protein data
cur_prot <- data.frame(colData(sce_prot))

# rna data set - cell type fractions
rna_sum <- cur_rna %>%
  group_by(Description, celltype) %>%
  summarise(n = n()) %>%
  group_by(Description) %>%
  mutate(fraction = n/sum(n)) %>%
  reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)

# protein data set - cell type fractions
prot_sum <- cur_prot %>%
  group_by(Description, celltype) %>%
  summarise(n = n()) %>%
  group_by(Description) %>%
  mutate(fraction = n/sum(n)) %>%
  reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)

# equal images
all(rna_sum$Description == prot_sum$Description)
[1] TRUE
# correlation
cor <- cor(rna_sum[,-1], prot_sum[,-1], method = "pearson")

# reorder cor matrix
cor <- cor[c("CD38", "HLA-DR", "Stroma", "Vasculature", "unknown", "CD8- T cell", "CD8+ T cell", "Macrophage", "Neutrophil", "Tumor"),
           c("B cell", "BnT cell", "pDC", "Stroma", "unknown", "FOXP3+ T cell", "CD4+ T cell", "CD8+ T cell", "Macrophage", "Neutrophil", "Tumor") ]

corrplot(cor, 
         addCoef.col = "black",
         method = "circle",
         tl.col="black",
         tl.cex = 1.5)

Version Author Date
235386f toobiwankenobi 2022-02-22
# correlation and p-value
common_cells <- c("Macrophage", "Neutrophil", "Tumor", "CD8+ T cell")

# mean correlation for celltype specific correlation
round(mean(cor(rna_sum[,"Macrophage"], prot_sum[,"Macrophage"]),
     cor(rna_sum[,"Neutrophil"], prot_sum[,"Neutrophil"]),
     cor(rna_sum[,"Tumor"], prot_sum[,"Tumor"]),
     cor(rna_sum[,"CD8+ T cell"], prot_sum[,"CD8+ T cell"])),2)
[1] 0.93
# p-values
cor.test(rna_sum[,"Macrophage"], prot_sum[,"Macrophage"])

    Pearson's product-moment correlation

data:  rna_sum[, "Macrophage"] and prot_sum[, "Macrophage"]
t = 31.629, df = 157, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9050165 0.9481476
sample estimates:
      cor 
0.9297027 
cor.test(rna_sum[,"Neutrophil"], prot_sum[,"Neutrophil"])

    Pearson's product-moment correlation

data:  rna_sum[, "Neutrophil"] and prot_sum[, "Neutrophil"]
t = 61.685, df = 157, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9727088 0.9853381
sample estimates:
      cor 
0.9799866 
cor.test(rna_sum[,"Tumor"], prot_sum[,"Tumor"])

    Pearson's product-moment correlation

data:  rna_sum[, "Tumor"] and prot_sum[, "Tumor"]
t = 47.778, df = 157, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9554987 0.9759952
sample estimates:
      cor 
0.9672899 
cor.test(rna_sum[,"CD8+ T cell"], prot_sum[,"CD8+ T cell"])

    Pearson's product-moment correlation

data:  rna_sum[, "CD8+ T cell"] and prot_sum[, "CD8+ T cell"]
t = 26.889, df = 157, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.8740854 0.9307515
sample estimates:
      cor 
0.9064157 

Supp Figure 6F

Detection of chemokine expressing cells

for the detection of chemokine expressing cells we make use of the fact that we also measured a negative control (DapB).

# get the names of the chemokine channels without the negative control channel
chemokine_channels = rownames(sce_rna[which(grepl("T\\d+_",rownames(sce_rna)) & ! grepl("DapB",rownames(sce_rna))),])
chemokine_channels_sub <- c("T2_CCL22")

# run function to define chemokine expressing cells 
output_list <- compute_difference(sce_rna, 
                          cellID = "cellID", 
                          assay_name = "asinh", 
                          threshold = 0.01, 
                          mRNA_channels = chemokine_channels_sub, 
                          negative_control = "T6_DapB", 
                          return_calc_metrics = TRUE)

Plot Results from Chemokine Detection

# check difference between DapB and signal (histogram)
plot_list = list()
for(i in chemokine_channels_sub){
  
  # subset whole data set for visualization purposes
  diff_chemo <- output_list[[i]]
  diff_chemo_sub <- diff_chemo[sample(nrow(diff_chemo), nrow(diff_chemo)*0.5), ]

  a <- ggplot(data = diff_chemo_sub, aes(x=scaled_diff)) + 
  geom_histogram(binwidth = 0.05, aes(fill = 
                                       ifelse(padj <= 0.01 & scaled_diff > 0, 'p<0.01', 'n.s.'))) + 
  xlab(paste(paste("Scaled Difference ", i, sep = " "), " - DapB", sep = "")) + 
    xlim(-5,7) +
    theme_minimal() + 
    theme(text = element_text(size=20),
          legend.position = "none") + 
    scale_fill_manual(values = c("black", "deepskyblue1"))
  
  # significant cells defined by subtraction
  b <- ggplot(data=diff_chemo_sub, aes(x=mean_negative_control, y=mean_chemokine)) + 
    geom_point_rast(alpha=0.2, aes(col = 
                                ifelse(padj <= 0.01 & scaled_diff > 0, 'p<0.01', 'n.s.'))) + 
    scale_color_manual(values = c("black", "deepskyblue1"), 
                       name = "Legend") +
    guides(color = guide_legend(override.aes = list(alpha=1, size=3))) +
    xlim(0,5.5) + ylim(0,5.5) +
    ylab(paste("Mean expression of", i, sep=" ")) +
    xlab("Mean DapB mRNA expression") +
    theme_minimal() + 
    theme(text = element_text(size=20)) 

  
  grid.arrange(a,b, nrow = 1, ncol=2)
}
Warning: Removed 327 rows containing non-finite values (stat_bin).
Warning: Removed 4 rows containing missing values (geom_bar).
Warning: Removed 2 rows containing missing values (geom_point).

Version Author Date
235386f toobiwankenobi 2022-02-22

Supp Figure 6G

This script reproduces the homology analysis between the different chemokines. We downloaded the data from www.ncbi.nlm.nih.gov and saved the transcript sequences. https://www.ebi.ac.uk/Tools/msa/clustalo/ was used to align all-vs-all transcripts using the following call:

$APPBIN/clustal-omega-1.2.4/bin/clustalo --infile clustalo-E20210914-122047-0397-8283578-p2m.sequence --threads 8 --MAC-RAM 8000 --verbose --guidetree-out clustalo-E20210914-122047-0397-8283578-p2m.dnd --outfmt clustal --resno --outfile clustalo-E20210914-122047-0397-8283578-p2m.clustal_num --output-order tree-order --seqtype dna

We will first look at the identity matrix, inidcating the percentage of sequence similarity.

seq_similarity <- read.table("data/data_for_analysis/ClustalW_results/identity_matrix.txt")
rownames(seq_similarity) <- seq_similarity$V2
seq_similarity <- seq_similarity[,-c(1,2)]

# Remove non coding transcript variants
seq_similarity <- seq_similarity[!grepl("XR_|NR_", rownames(seq_similarity)),
                                 !grepl("XR_|NR_", rownames(seq_similarity))]
rownames(seq_similarity) <- sub("\\.[0-9]*", "", rownames(seq_similarity))


phylogenetic_tree <- read.tree("data/data_for_analysis/ClustalW_results/phylogenetic_tree.txt")

Now, we will map between RefSeq transcript ids, ensemble transcript ids and gene names.

# workaround: useMart error: SSL certificate problem: unable to get local issuer certificate
httr::set_config(httr::config(ssl_verifypeer = FALSE), override = FALSE)

ensembl <- useMart("ensembl", dataset="hsapiens_gene_ensembl")
cur_tab <- getBM(attributes=c("refseq_mrna", "ensembl_gene_id", "hgnc_symbol"), 
      filters = "refseq_mrna", values = rownames(seq_similarity), 
      mart = ensembl, uniqueRows = FALSE)
cur_tab <- cur_tab[match(rownames(seq_similarity), cur_tab$refseq_mrna),]

rownames(seq_similarity)  <- colnames(seq_similarity) <- 
    paste0(cur_tab$refseq_mrna, "_", cur_tab$hgnc_symbol)

And we compare it to correlation in expression across all cells.

final_sce <- sce_rna
# select only the cells that express chemokines
for_analysis <- final_sce[,final_sce$expressor != "NA"]

cur_cor <- cor(t(assay(for_analysis, "asinh")[c("T5_CCL4", "T7_CCL18", "T1_CXCL8",
                                             "T4_CXCL10", "T3_CXCL12", "T8_CXCL13",
                                             "T12_CCL2", "T2_CCL22",
                                             "T9_CXCL9", "T11_CCL8", "T10_CCL19"),]), 
                                           method = "spearman")

pheatmap(cur_cor, color = colorRampPalette(c("dark blue", "white", "dark red"))(100), 
         breaks = seq(-1, 1, length.out = 100))

Version Author Date
235386f toobiwankenobi 2022-02-22
cor_tibbble <- cur_cor %>%
    as_tibble() %>%
    mutate(probe = rownames(cur_cor)) %>% 
    pivot_longer(cols = 1:ncol(cur_cor)) %>%
    mutate(probe = str_split(probe, pattern = "_", simplify = TRUE)[,2],
           name = str_split(name, pattern = "_", simplify = TRUE)[,2]) %>%
    arrange(probe, name) %>%
    filter(probe != name)

sim_tibble <- seq_similarity %>%
    as_tibble() %>% 
    mutate(probe = rownames(seq_similarity)) %>% 
    pivot_longer(cols = 1:ncol(seq_similarity)) %>%
    mutate(from_chemo = str_split(probe, pattern = "_", simplify = TRUE)[,3],
           to_chemo = str_split(name, pattern = "_", simplify = TRUE)[,3]) %>%
    group_by(from_chemo, to_chemo) %>%
    dplyr::summarize(mean_similarity = mean(value, na.rm=TRUE)) %>%
    arrange(from_chemo, to_chemo) %>%
    filter(from_chemo != to_chemo)

all.equal(paste(cor_tibbble$probe, cor_tibbble$name),
          paste(sim_tibble$from_chemo, sim_tibble$to_chemo))
[1] TRUE
ggplot(data.frame(similarity = sim_tibble$mean_similarity,
                  correlation = cor_tibbble$value)) +
    geom_point(aes(similarity, correlation))
Warning: Removed 20 rows containing missing values (geom_point).

Version Author Date
235386f toobiwankenobi 2022-02-22
cor.test(sim_tibble$mean_similarity,
         cor_tibbble$value)

    Pearson's product-moment correlation

data:  sim_tibble$mean_similarity and cor_tibbble$value
t = 4.9613, df = 88, p-value = 3.387e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2883482 0.6150626
sample estimates:
      cor 
0.4675216 

Number of co-expressors

Finally, we will compare the sequence similarity to the jaccard index of chemokine expressors.

# Define the jaccard similarity
jaccard <- function(x,y){
    intersection <- length(intersect(x,y))
    union = length(x) + length(y) - intersection
    return (intersection/union)
}

# We will pass the unique cell ids into this function
cur_out <- lapply(seq_len(nrow(sim_tibble)),
       function(x){
           from_chemo_cells <- colnames(final_sce)[colData(final_sce)[[sim_tibble$from_chemo[x]]] != 0]
           to_chemo_cells <- colnames(final_sce)[colData(final_sce)[[sim_tibble$to_chemo[x]]] != 0]
           return(jaccard(from_chemo_cells, to_chemo_cells))
       })
sim_tibble$jaccard_sim <- unlist(cur_out)

ggplot(data.frame(similarity = sim_tibble$mean_similarity,
                  jaccard_sim = sim_tibble$jaccard_sim)) +
    geom_point(aes(similarity, jaccard_sim)) +
    geom_smooth(method = "lm", aes(similarity, jaccard_sim)) +  stat_cor(method = "pearson",
           aes(x = similarity, y = jaccard_sim, label = paste0("atop(", ..r.label..,  ",", ..p.label.. ,")")),
           size = 7, cor.coef.name = "R", label.sep="\n", label.y.npc = "top") + 
    theme_bw() + theme(text=element_text(size=15)) +
    xlab("Sequence similarity") + ylab("Chemokine co-expression [Jaccard index]")
Warning: Removed 20 rows containing non-finite values (stat_smooth).
Warning: Removed 20 rows containing non-finite values (stat_cor).
Warning: Removed 20 rows containing missing values (geom_point).

Version Author Date
235386f toobiwankenobi 2022-02-22
cor.test(sim_tibble$mean_similarity,
         sim_tibble$jaccard_sim)

    Pearson's product-moment correlation

data:  sim_tibble$mean_similarity and sim_tibble$jaccard_sim
t = 1.6191, df = 88, p-value = 0.109
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.03836368  0.36433718
sample estimates:
      cor 
0.1700787 

Supp Figure 6H

Fraction of Tumor Cells that express chemokines

cur_dat <- data.frame(colData(sce_rna))
cur_dat <- cur_dat %>%
  filter(celltype == "Tumor") %>%
  filter(Location != "CTRL")

cur_dat <- cur_dat[,c("ImageNumber", "Mutation", colnames(cur_dat)[grepl(glob2rx("C*L*"),names(cur_dat))])]

# colSums of Chemokines in Tumor Cells (Multiple Producer count more than once)
cur_dat <- cur_dat %>%
  group_by(ImageNumber, Mutation) %>%
  mutate(cells = n()) %>%
  group_by(ImageNumber, cells, Mutation) %>%
  summarise_each(funs(sum))
Warning: `summarise_each_()` was deprecated in dplyr 0.7.0.
Please use `across()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
# compute fractions
cur_dat[,4:14] <- cur_dat[,4:14] / t(cur_dat$cells)

cur_dat <- cur_dat %>%
  filter(cells > 200) %>%
  reshape2::melt(id.vars=c("ImageNumber", "cells", "Mutation"), variable.name="chemokine", value.name="fraction")

ggplot(cur_dat,aes(x=fct_reorder(chemokine, fraction, .fun = median, .desc = TRUE), y=fraction+0.001)) + 
  geom_boxplot(alpha=.5) +
  geom_quasirandom(alpha=.2) +
  theme_bw() + 
  theme(text=element_text(size=16)) +
  ylab("Fraction of Expressing Tumor Cells\n(fraction + 0.001)") +
  xlab("") +
  scale_y_log10() +
  annotation_logticks(sides = "l") +
  geom_hline(yintercept = median(cur_dat$fraction+0.001), linetype = 2) 

Version Author Date
235386f toobiwankenobi 2022-02-22

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

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] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] biomaRt_2.50.3              ape_5.6-1                  
 [3] rstatix_0.7.0               ggrepel_0.9.1              
 [5] circlize_0.4.13             cowplot_1.1.1              
 [7] ggpubr_0.4.0                corrplot_0.92              
 [9] ggbeeswarm_0.6.0            dittoSeq_1.6.0             
[11] scater_1.22.0               scuttle_1.4.0              
[13] ComplexHeatmap_2.10.0       ggpmisc_0.4.5              
[15] ggpp_0.4.3                  gridExtra_2.3              
[17] RColorBrewer_1.1-2          colorRamps_2.3             
[19] ggrastr_1.0.1               data.table_1.14.2          
[21] forcats_0.5.1               stringr_1.4.0              
[23] purrr_0.3.4                 readr_2.1.2                
[25] tidyr_1.2.0                 tibble_3.1.6               
[27] ggplot2_3.3.5               tidyverse_1.3.1            
[29] reshape2_1.4.4              cytomapper_1.6.0           
[31] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[33] Biobase_2.54.0              GenomicRanges_1.46.1       
[35] GenomeInfoDb_1.30.1         IRanges_2.28.0             
[37] S4Vectors_0.32.3            BiocGenerics_0.40.0        
[39] MatrixGenerics_1.6.0        matrixStats_0.61.0         
[41] EBImage_4.36.0              dplyr_1.0.7                
[43] workflowr_1.7.0            

loaded via a namespace (and not attached):
  [1] utf8_1.2.2                shinydashboard_0.7.2     
  [3] tidyselect_1.1.1          RSQLite_2.2.9            
  [5] AnnotationDbi_1.56.2      htmlwidgets_1.5.4        
  [7] BiocParallel_1.28.3       munsell_0.5.0            
  [9] ScaledMatrix_1.2.0        codetools_0.2-18         
 [11] withr_2.4.3               colorspace_2.0-2         
 [13] filelock_1.0.2            highr_0.9                
 [15] knitr_1.37                rstudioapi_0.13          
 [17] ggsignif_0.6.3            labeling_0.4.2           
 [19] git2r_0.29.0              GenomeInfoDbData_1.2.7   
 [21] farver_2.1.0              bit64_4.0.5              
 [23] pheatmap_1.0.12           rhdf5_2.38.0             
 [25] rprojroot_2.0.2           vctrs_0.3.8              
 [27] generics_0.1.2            xfun_0.29                
 [29] BiocFileCache_2.2.1       R6_2.5.1                 
 [31] doParallel_1.0.16         clue_0.3-60              
 [33] rsvd_1.0.5                locfit_1.5-9.4           
 [35] cachem_1.0.6              bitops_1.0-7             
 [37] rhdf5filters_1.6.0        DelayedArray_0.20.0      
 [39] assertthat_0.2.1          promises_1.2.0.1         
 [41] scales_1.1.1              beeswarm_0.4.0           
 [43] gtable_0.3.0              beachmat_2.10.0          
 [45] Cairo_1.5-14              processx_3.5.2           
 [47] rlang_1.0.0               MatrixModels_0.5-0       
 [49] systemfonts_1.0.3         splines_4.1.2            
 [51] GlobalOptions_0.1.2       broom_0.7.12             
 [53] yaml_2.2.2                abind_1.4-5              
 [55] modelr_0.1.8              backports_1.4.1          
 [57] httpuv_1.6.5              tools_4.1.2              
 [59] ellipsis_0.3.2            raster_3.5-15            
 [61] jquerylib_0.1.4           ggridges_0.5.3           
 [63] Rcpp_1.0.8                plyr_1.8.6               
 [65] progress_1.2.2            sparseMatrixStats_1.6.0  
 [67] zlibbioc_1.40.0           RCurl_1.98-1.5           
 [69] prettyunits_1.1.1         ps_1.6.0                 
 [71] GetoptLong_1.0.5          viridis_0.6.2            
 [73] haven_2.4.3               cluster_2.1.2            
 [75] fs_1.5.2                  magrittr_2.0.2           
 [77] magick_2.7.3              SparseM_1.81             
 [79] reprex_2.0.1              whisker_0.4              
 [81] hms_1.1.1                 mime_0.12                
 [83] fftwtools_0.9-11          evaluate_0.14            
 [85] xtable_1.8-4              XML_3.99-0.8             
 [87] jpeg_0.1-9                readxl_1.3.1             
 [89] shape_1.4.6               compiler_4.1.2           
 [91] crayon_1.4.2              htmltools_0.5.2          
 [93] mgcv_1.8-38               later_1.3.0              
 [95] tzdb_0.2.0                tiff_0.1-11              
 [97] lubridate_1.8.0           DBI_1.1.2                
 [99] dbplyr_2.1.1              rappdirs_0.3.3           
[101] Matrix_1.4-0              car_3.0-12               
[103] cli_3.1.1                 parallel_4.1.2           
[105] pkgconfig_2.0.3           getPass_0.2-2            
[107] sp_1.4-6                  terra_1.5-17             
[109] xml2_1.3.3                foreach_1.5.2            
[111] svglite_2.0.0             vipor_0.4.5              
[113] bslib_0.3.1               XVector_0.34.0           
[115] rvest_1.0.2               callr_3.7.0              
[117] digest_0.6.29             Biostrings_2.62.0        
[119] rmarkdown_2.11            cellranger_1.1.0         
[121] DelayedMatrixStats_1.16.0 curl_4.3.2               
[123] shiny_1.7.1               quantreg_5.87            
[125] rjson_0.2.21              nlme_3.1-155             
[127] lifecycle_1.0.1           jsonlite_1.7.3           
[129] Rhdf5lib_1.16.0           carData_3.0-5            
[131] BiocNeighbors_1.12.0      viridisLite_0.4.0        
[133] fansi_1.0.2               pillar_1.7.0             
[135] lattice_0.20-45           KEGGREST_1.34.0          
[137] fastmap_1.1.0             httr_1.4.2               
[139] glue_1.6.1                png_0.1-7                
[141] iterators_1.0.13          svgPanZoom_0.3.4         
[143] bit_4.0.4                 stringi_1.7.6            
[145] sass_0.4.0                HDF5Array_1.22.1         
[147] blob_1.2.2                BiocSingular_1.10.0      
[149] memoise_2.0.1             irlba_2.3.5