Last updated: 2022-02-22
Checks: 7 0
Knit directory: MelanomaIMC/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200728) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version d00a53f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rproj.user/
Ignored: Table_S4.csv
Ignored: code/.DS_Store
Ignored: code/._.DS_Store
Ignored: data/.DS_Store
Ignored: data/._.DS_Store
Ignored: data/data_for_analysis/
Ignored: data/full_data/
Unstaged changes:
Modified: .gitignore
Staged changes:
Deleted: analysis/.smbdeleteAAA16a00f1
Modified: analysis/_site.yml
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Supp-Figure_14.rmd) and HTML (docs/Supp-Figure_14.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 64e5fde | toobiwankenobi | 2022-02-16 | change order and naming of supp fig files |
This script generates plots for Supplementary Figure 14, which shows Figure 5 without any LN samples.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())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(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
library(ggplot2)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggpubr)
library(ggbeeswarm)
library(gridExtra)
library(tidyr)
library(ggpmisc)
library(circlize)
library(dittoSeq)
library(scater)
library(cowplot)
library(cytomapper)
library(corrplot)
library(ggridges)
library(rstatix)
library(sf)
library(concaveman)
library(RANN)sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
# remove all LN samples
sce_rna <- sce_rna[,sce_rna$MM_location_simplified != "LN"]
sce_prot <- sce_prot[,sce_prot$MM_location_simplified != "LN"]
sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]
# meta data
dat_relation = fread(file = "data/data_for_analysis/protein/Object relationships.csv",stringsAsFactors = FALSE)
dat_relation_rna = fread(file = "data/data_for_analysis/RNA/Object relationships.csv",stringsAsFactors = FALSE)
# image
image_mat_rna <- read.csv("data/data_for_analysis/rna/Image.csv")
# surv_dat
dat_survival_prot <- fread(file = "data/data_for_analysis/protein/clinical_data_protein.csv")# prepare data and add cellID
dat_relation$cellID_first <- paste("protein", paste(dat_relation$`First Image Number`, dat_relation$`First Object Number`, sep = "_"), sep = "_")
dat_relation$cellID_second <- paste("protein", paste(dat_relation$`Second Image Number`, dat_relation$`Second Object Number`, sep = "_"), sep = "_")
# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_prot))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_prot))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")
dat_relation <- left_join(dat_relation, celltype_first, by = "cellID_first")
dat_relation <- left_join(dat_relation, celltype_second, by = "cellID_second")
colnames(dat_relation)[5] <- "FirstImageNumber"# prepare data and add cellID
dat_relation_rna$cellID_first <- paste("RNA", paste(dat_relation_rna$`First Image Number`, dat_relation_rna$`First Object Number`, sep = "_"), sep = "_")
dat_relation_rna$cellID_second <- paste("RNA", paste(dat_relation_rna$`Second Image Number`, dat_relation_rna$`Second Object Number`, sep = "_"), sep = "_")
# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_rna))[,c("cellID", "celltype_rf", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_rna))[,c("cellID", "celltype_rf", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")
dat_relation_rna <- left_join(dat_relation_rna, celltype_first, by = "cellID_first")
dat_relation_rna <- left_join(dat_relation_rna, celltype_second, by = "cellID_second")
colnames(dat_relation_rna)[5] <- "FirstImageNumber"# subset sce for inflamed/exhausted in high samples
sce_protein_sub <- sce_prot[, sce_prot$dysfunction_density %in% c("high - High Dysfunction", "high - Low Dysfunction")]
# sample 9 images each
images <- data.frame(colData(sce_protein_sub)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(dysfunction_density) %>%
#filter(ImageNumber %in% sample(ImageNumber, 9)) %>%
select(ImageNumber, dysfunction_density)
return <- list()
for (i in c("high - High Dysfunction", "high - Low Dysfunction")){
# title name
title_name <- i
# count interactions in 20 sample images
cur_dat_relation <- data.frame(dat_relation) %>%
filter(FirstImageNumber %in% images[images$dysfunction_density == i,]$ImageNumber) %>%
select("celltype_first" ,"celltype_second") %>%
dplyr::count(celltype_first,celltype_second) %>%
data.frame()
# remove tumor-tumor interactions
cur_dat_relation <- cur_dat_relation[cur_dat_relation$celltype_first != "Tumor",]
cur_dat_relation <- cur_dat_relation[cur_dat_relation$celltype_first != "unknown",]
cur_dat_relation <- cur_dat_relation[cur_dat_relation$celltype_second != "unknown",]
# count interactions
cur_dat_relation_subcluster <- data.frame(dat_relation) %>%
filter(FirstImageNumber %in% images[images$dysfunction_density == i,]$ImageNumber) %>%
select("celltype_first" , "celltype_clust_second") %>%
dplyr::count(celltype_first, celltype_clust_second) %>%
data.frame()
# make coord diagram
chordDiagramFromDataFrame(cur_dat_relation,
grid.col = metadata(sce_prot)$colour_vectors$celltype,
reduce = 0.05,
transparency = ifelse(cur_dat_relation[[1]] %in% c("CD8+ T cell"),0,0.6),
annotationTrack = c("grid"))
}# create legend for tumor subclusters
lgd2 = Legend(labels = names(metadata(sce_prot)$colour_vector$celltype), title = "Cell Type", legend_gp = gpar(fill = unname(metadata(sce_prot)$colour_vector$celltype)))
draw(packLegend(lgd2,
#lgd1,
gap = unit(2, "cm")))celltypes <- data.frame(colData(sce_prot)) %>%
filter(celltype != "Tumor") %>%
group_by(ImageNumber, bcell_patch_score, dysfunction_score, celltype) %>%
summarise(n=n()) %>%
mutate(fraction = n/sum(n)) %>%
filter(is.na(dysfunction_score) == FALSE)
celltypes$bcell_patch_score <- as.character(celltypes$bcell_patch_score)
celltypes$bcell_patch_score <- factor(celltypes$bcell_patch_score, levels = c("No B cells", "No B cell Patches", "Small B cell Patches", "B cell Follicles"))
stat.test <- celltypes %>%
group_by(celltype) %>%
wilcox_test(data = ., fraction ~ dysfunction_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_x_position(x = "dysfunction_score", dodge = 0.8)
ggplot(celltypes, aes(x=celltype, y=fraction)) +
geom_boxplot(alpha=.5, lwd = 1, outlier.shape = NA, aes(fill=dysfunction_score)) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=1, aes(group=dysfunction_score)) +
stat_pvalue_manual(stat.test, x = "celltype", label = "p.adj.signif", size = 7, y.position = -0.05) +
theme_bw() +
theme(text = element_text(size = 20),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="Dysfunction Score", override.aes = c(lwd=0.5, alpha=1)), col=guide_legend(title="B cell Score")) +
xlab("") +
ylab("Cell Type Fractions") +
scale_color_manual(values = c("black", "lightblue", "darkblue", "red", "red4"),
labels = c(" ", "no Bcells", "few Bcells", "small patches", "large patches"),
guide = TRUE)# check images above median in Low Dysfunction group for B cells (all have large patches)
celltypes %>%
filter(dysfunction_score == "Low Dysfunction") %>%
filter(celltype == "B cell") %>%
group_by(dysfunction_score) %>%
mutate(median_frac = median(fraction)) %>%
filter(fraction > median_frac)# A tibble: 4 × 7
# Groups: dysfunction_score [1]
ImageNumber bcell_patch_score dysfunction_score celltype n fraction
<int> <fct> <chr> <chr> <int> <dbl>
1 13 No B cell Patches Low Dysfunction B cell 24 0.0146
2 19 Small B cell Patches Low Dysfunction B cell 131 0.0761
3 29 B cell Follicles Low Dysfunction B cell 883 0.134
4 118 B cell Follicles Low Dysfunction B cell 782 0.204
# … with 1 more variable: median_frac <dbl>a <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = T) %>%
group_by(bcell_patch_score, dysfunction_score) %>%
summarise(n=n()) %>%
filter(is.na(dysfunction_score) == F) %>%
group_by(dysfunction_score) %>%
mutate(fraction = n / sum(n)) %>%
ungroup()
ggplot(a) +
geom_col(aes(y=dysfunction_score, x=fraction, fill=bcell_patch_score)) +
theme_minimal() +
theme(text = element_text(size = 22)) +
xlab("Fraction of Samples") +
ylab("") +
guides(fill=guide_legend(title="B cell Score"))# fraction of all CXCL13 production
cxcl13_fraction <- data.frame(colData(sce_rna)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin samples
filter(CXCL13 == 1) %>%
group_by(Description, celltype, .drop = FALSE) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ celltype, fill = 0, value.var = "n") %>%
reshape2::melt(id.vars=c("Description"), variable.name = "celltype", value.name = "n") %>%
group_by(Description, .drop = FALSE) %>%
mutate(fraction = n / sum(n)) %>%
filter(celltype %in% c("CD8+ T cell", "CD8- T cell", "HLA-DR"))
all_images <- data.frame(colData(sce_rna)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>%
distinct(Description, .keep_all = T) %>%
dplyr::select(Description)
# left_join to have all images
all_images <- left_join(all_images, cxcl13_fraction)
# add 0 to images that do not containt CXCL13 producing cells
all_images <- all_images %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0) %>%
dplyr::select(-`NA`) %>%
reshape2::melt(id.vars=c("Description"), variable.name = "celltype", value.name = "fraction")
# get Bcell score for each image
Bcell <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = T) %>%
group_by(Description) %>%
dplyr::select(Description, bcell_patch_score)
# add Bcell score
all_images <- left_join(all_images, Bcell[,c("Description","bcell_patch_score")])
# stats - is there a difference between celltype fractions in the groups?
all_images %>%
group_by(bcell_patch_score) %>%
wilcox_test(fraction ~ celltype) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1))# A tibble: 12 × 10
bcell_patch_score .y. group1 group2 n1 n2 statistic p p.adj
* <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
1 No B cells frac… CD8- … CD8+ … 47 47 1093 9.24e-1 9.24e-1
2 No B cells frac… CD8- … HLA-DR 47 47 1444. 4.47e-4 8.94e-4
3 No B cells frac… CD8+ … HLA-DR 47 47 1492. 1.07e-4 2.57e-4
4 No B cell Patches frac… CD8- … CD8+ … 38 38 778. 5.54e-1 6.04e-1
5 No B cell Patches frac… CD8- … HLA-DR 38 38 1234 3.23e-9 3.88e-8
6 No B cell Patches frac… CD8+ … HLA-DR 38 38 1150 2.82e-7 1.69e-6
7 Small B cell Patch… frac… CD8- … CD8+ … 13 13 23.5 2 e-3 3.43e-3
8 Small B cell Patch… frac… CD8- … HLA-DR 13 13 162. 3.61e-5 1.08e-4
9 Small B cell Patch… frac… CD8+ … HLA-DR 13 13 162. 3.61e-5 1.08e-4
10 B cell Follicles frac… CD8- … CD8+ … 7 7 31 4.56e-1 6.04e-1
11 B cell Follicles frac… CD8- … HLA-DR 7 7 36.5 1.4 e-1 2.1 e-1
12 B cell Follicles frac… CD8+ … HLA-DR 7 7 30 5.21e-1 6.04e-1
# … with 1 more variable: p.adj.signif <chr>ggplot(all_images,aes(x=bcell_patch_score, y = as.numeric(fraction), fill=celltype)) +
geom_boxplot(alpha=1, lwd=1, outlier.shape = NA, aes(fill=celltype)) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=2, aes(fill=celltype)) +
ylab("Fraction of Total CXCL13 Expression") +
#stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
theme_bw() +
theme(text = element_text(size=19),
axis.text.x = element_text(angle=45, vjust=1, hjust=1)) +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype[c("CD8+ T cell", "CD8- T cell", "HLA-DR")]),
breaks = names(metadata(sce_rna)$colour_vectors$celltype[c("CD8+ T cell", "CD8- T cell", "HLA-DR")])) +
guides(fill=guide_legend(title="Cell Type", override.aes = c(lwd=0.5))) +
scale_color_discrete(guide = FALSE)Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
use `guide = "none"` instead.example <- findPatch(sce_prot[,sce_prot$Description == "P5"], sce_prot[,colData(sce_prot)$celltype %in% c("B cell", "BnT cell")]$cellID,
'cellID',
'Center_X', 'Center_Y',
'Description',
distance = 15,
min_clust_size = 10,
output_colname = "example_patch")Time difference of 0.7922647 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'Description',
'example_patch',
distance = 30,
output_colname = "example_milieu",
plot = TRUE)Time difference of 1.375145 secs
[1] "milieus successfully added to sce object"celltypes <- data.frame(colData(sce_prot)) %>%
mutate(TCF7_PD1 = paste(PD1, TCF7, sep = "_")) %>%
group_by(PatientID,BlockID, Description, bcell_patch_score, celltype, TCF7_PD1) %>%
summarise(n=n()) %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score + celltype ~ TCF7_PD1, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score", "celltype"),
variable.name = "TCF7_PD1", value.name = "n") %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score + TCF7_PD1 ~ celltype, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score", "TCF7_PD1"),
variable.name = "celltype", value.name = "n") %>%
group_by(Description, celltype) %>%
mutate(fraction = n/sum(n)) %>%
mutate(total_cells = sum(n)) %>%
ungroup() %>%
filter(celltype %in% c("CD8+ T cell", "CD4+ T cell"))
celltypes$bcell_patch_score <- as.character(celltypes$bcell_patch_score)
celltypes$bcell_patch_score <- factor(celltypes$bcell_patch_score, levels = c("No B cells", "No B cell Patches", "Small B cell Patches", "B cell Follicles"))
celltypes$celltype <- factor(celltypes$celltype, levels = c("CD8+ T cell", "CD4+ T cell"))
stat.test <- celltypes %>%
group_by(celltype, TCF7_PD1) %>%
kruskal_test(data = ., fraction ~ bcell_patch_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
mutate(group1 = celltype, group2 = TCF7_PD1) %>%
add_xy_position()
ggplot(celltypes, aes(x=TCF7_PD1, y=fraction)) +
geom_boxplot(alpha=1, lwd = 0.5, outlier.size = 0.5, aes(fill=bcell_patch_score)) +
stat_pvalue_manual(x = "group2", stat.test, y.position = -0.1, size = 6) +
theme_bw() +
theme(text = element_text(size = 14),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="B cell Score", override.aes = c(lwd=0.5))) +
xlab("") +
ylab("Fraction of Population") +
facet_wrap(~celltype, scales = "free") +
ylim(-0.1,1.1)Warning: Removed 36 rows containing non-finite values (stat_boxplot).# magnify PD1+TCF7+ population
ggplot(celltypes[celltypes$TCF7_PD1 == "PD1+_TCF7+",], aes(x=TCF7_PD1, y=fraction)) +
geom_boxplot(alpha=1, lwd = 0.5, outlier.size = 0.5, aes(fill=bcell_patch_score)) +
theme_bw() +
theme(text = element_text(size = 14),
axis.text.x = element_blank(),
legend.position = "none") +
xlab("") +
ylab("") +
facet_wrap(~celltype, scales = "free") +
coord_cartesian(ylim = c(0,0.05))Warning: Removed 9 rows containing non-finite values (stat_boxplot).# what fraction of each celltype is part of a milieu?
cur_dat <- data.frame(colData(sce_prot)) %>%
mutate(MMLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(MMLocationPunch != "LN_M") %>%
filter(Location != "CTRL") %>%
filter(celltype %in% c("CD8+ T cell", "CD4+ T cell")) %>%
mutate(status = paste(TCF7, PD1, sep = "_")) %>%
mutate(milieu_binary = ifelse(bcell_milieu > 0, 1, 0)) %>%
group_by(Description, milieu_binary, celltype, status) %>%
summarise(n=n()) %>%
group_by(Description, celltype, status) %>%
mutate(fraction_in_milieu = n / sum(n)) %>%
filter(milieu_binary == 1)
# what fraction of the total cell area is made up by cells that are part of a milieu
milieu_area <- data.frame(colData(sce_prot)) %>%
mutate(MMLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(MMLocationPunch != "LN_M") %>%
filter(Location != "CTRL") %>%
mutate(milieu_binary = ifelse(bcell_milieu > 0, 1, 0)) %>%
group_by(Description, milieu_binary) %>%
summarise(area = sum(Area)) %>%
group_by(Description) %>%
mutate(fraction_of_area = area / sum(area)) %>%
filter(row_number() == n())
sum <- left_join(cur_dat, milieu_area, by="Description")
# calculate metric - milieu_fraction divided by milieu-area-fraction (this normalizes the first metric)
sum$metric <- sum$fraction_in_milieu / sum$fraction_of_area
sum$celltype <- factor(sum$celltype, levels = c("CD8+ T cell", "CD4+ T cell"))
# one-sample t test
stat.test <- sum %>%
group_by(celltype, status) %>%
mutate(log10_metric = log10(metric)) %>%
t_test(log10_metric ~ 1, mu = 0, alternative = "greater", detailed = TRUE) %>%
adjust_pvalue() %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1))
ggplot(sum, aes(x=sum$status, y=log10(metric))) +
geom_boxplot(outlier.shape = NA) +
geom_quasirandom(size=0.75) +
geom_hline(yintercept = 0) +
facet_wrap(~celltype) +
theme_bw() +
theme(text=element_text(size=14),
axis.text.x = element_text(angle=45, vjust=1, hjust=1)) +
stat_pvalue_manual(
stat.test, x = "status", y.position = 1.5,
label = "p.adj.signif",
position = position_dodge(0.8),
size=6) +
ylab("Enrichment in B cell milieus (log10)") +
xlab("")
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] RANN_2.6.1 concaveman_1.1.0
[3] sf_1.0-5 rstatix_0.7.0
[5] ggridges_0.5.3 corrplot_0.92
[7] cytomapper_1.6.0 EBImage_4.36.0
[9] cowplot_1.1.1 scater_1.22.0
[11] scuttle_1.4.0 dittoSeq_1.6.0
[13] ggpmisc_0.4.5 ggpp_0.4.3
[15] gridExtra_2.3 ggbeeswarm_0.6.0
[17] ggpubr_0.4.0 RColorBrewer_1.1-2
[19] circlize_0.4.13 colorRamps_2.3
[21] ComplexHeatmap_2.10.0 data.table_1.14.2
[23] forcats_0.5.1 stringr_1.4.0
[25] purrr_0.3.4 readr_2.1.2
[27] tidyr_1.2.0 tibble_3.1.6
[29] ggplot2_3.3.5 tidyverse_1.3.1
[31] reshape2_1.4.4 SingleCellExperiment_1.16.0
[33] SummarizedExperiment_1.24.0 Biobase_2.54.0
[35] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[37] IRanges_2.28.0 S4Vectors_0.32.3
[39] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[41] matrixStats_0.61.0 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 htmlwidgets_1.5.4
[5] BiocParallel_1.28.3 munsell_0.5.0
[7] ScaledMatrix_1.2.0 units_0.7-2
[9] codetools_0.2-18 withr_2.4.3
[11] colorspace_2.0-2 highr_0.9
[13] knitr_1.37 rstudioapi_0.13
[15] ggsignif_0.6.3 labeling_0.4.2
[17] git2r_0.29.0 GenomeInfoDbData_1.2.7
[19] farver_2.1.0 pheatmap_1.0.12
[21] rhdf5_2.38.0 rprojroot_2.0.2
[23] vctrs_0.3.8 generics_0.1.2
[25] xfun_0.29 R6_2.5.1
[27] doParallel_1.0.16 clue_0.3-60
[29] rsvd_1.0.5 locfit_1.5-9.4
[31] bitops_1.0-7 rhdf5filters_1.6.0
[33] DelayedArray_0.20.0 assertthat_0.2.1
[35] promises_1.2.0.1 scales_1.1.1
[37] beeswarm_0.4.0 gtable_0.3.0
[39] beachmat_2.10.0 processx_3.5.2
[41] rlang_1.0.0 MatrixModels_0.5-0
[43] systemfonts_1.0.3 GlobalOptions_0.1.2
[45] broom_0.7.12 yaml_2.2.2
[47] abind_1.4-5 modelr_0.1.8
[49] backports_1.4.1 httpuv_1.6.5
[51] tools_4.1.2 ellipsis_0.3.2
[53] raster_3.5-15 jquerylib_0.1.4
[55] proxy_0.4-26 Rcpp_1.0.8
[57] plyr_1.8.6 sparseMatrixStats_1.6.0
[59] zlibbioc_1.40.0 classInt_0.4-3
[61] RCurl_1.98-1.5 ps_1.6.0
[63] GetoptLong_1.0.5 viridis_0.6.2
[65] haven_2.4.3 ggrepel_0.9.1
[67] cluster_2.1.2 fs_1.5.2
[69] magrittr_2.0.2 SparseM_1.81
[71] reprex_2.0.1 whisker_0.4
[73] hms_1.1.1 mime_0.12
[75] evaluate_0.14 fftwtools_0.9-11
[77] xtable_1.8-4 jpeg_0.1-9
[79] readxl_1.3.1 shape_1.4.6
[81] compiler_4.1.2 V8_4.0.0
[83] KernSmooth_2.23-20 crayon_1.4.2
[85] htmltools_0.5.2 later_1.3.0
[87] tzdb_0.2.0 tiff_0.1-11
[89] lubridate_1.8.0 DBI_1.1.2
[91] dbplyr_2.1.1 Matrix_1.4-0
[93] car_3.0-12 cli_3.1.1
[95] parallel_4.1.2 pkgconfig_2.0.3
[97] getPass_0.2-2 sp_1.4-6
[99] terra_1.5-17 xml2_1.3.3
[101] foreach_1.5.2 svglite_2.0.0
[103] vipor_0.4.5 bslib_0.3.1
[105] XVector_0.34.0 rvest_1.0.2
[107] callr_3.7.0 digest_0.6.29
[109] rmarkdown_2.11 cellranger_1.1.0
[111] DelayedMatrixStats_1.16.0 curl_4.3.2
[113] shiny_1.7.1 quantreg_5.87
[115] rjson_0.2.21 lifecycle_1.0.1
[117] jsonlite_1.7.3 Rhdf5lib_1.16.0
[119] carData_3.0-5 BiocNeighbors_1.12.0
[121] viridisLite_0.4.0 fansi_1.0.2
[123] pillar_1.7.0 lattice_0.20-45
[125] fastmap_1.1.0 httr_1.4.2
[127] glue_1.6.1 png_0.1-7
[129] iterators_1.0.13 svgPanZoom_0.3.4
[131] class_7.3-20 stringi_1.7.6
[133] sass_0.4.0 HDF5Array_1.22.1
[135] BiocSingular_1.10.0 e1071_1.7-9
[137] irlba_2.3.5