Last updated: 2021-02-19
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Knit directory: melanoma_publication_old_data/
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| Rmd | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
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| Rmd | c3e8a47 | toobiwankenobi | 2020-10-12 | clean branch |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
This script generates plots for Figure 5.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())First, we will load the libraries needed for this part of the analysis.
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
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visible FALSE
code/helper_functions/censor_dat.R
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visible FALSE
code/helper_functions/detect_mRNA_expression.R
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visible FALSE
code/helper_functions/DistanceToClusterCenter.R
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visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
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visible FALSE
code/helper_functions/getSpotnumber.R
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visible FALSE
code/helper_functions/plotCellCounts.R
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visible FALSE
code/helper_functions/plotCellFractions.R
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visible FALSE
code/helper_functions/plotDist.R
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visible FALSE
code/helper_functions/scatter_function.R
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visible FALSE
code/helper_functions/sceChecks.R
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code/helper_functions/validityChecks.R
value ?
visible FALSE library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
library(ggplot2)
library(cba)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggpubr)
library(ggbeeswarm)
library(gridExtra)
library(tidyr)
library(ggpmisc)
library(circlize)
library(coxme)
library(dittoSeq)
library(scater)
library(cowplot)
library(survminer)
library(ggalluvial)
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")
# 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()
# remove tumor-tumor interactions
cur_dat_relation_subcluster <- cur_dat_relation_subcluster[cur_dat_relation_subcluster$celltype_first == "CD8+ T cell",]
cur_dat_relation_subcluster <- cur_dat_relation_subcluster[cur_dat_relation_subcluster$celltype_clust_second %in%
unique(sce_prot[,sce_prot$celltype == "Tumor"]$celltype_clustered),]
# return subcluster adj df
return[[i]] <- cur_dat_relation_subcluster
# 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"))
# add percentages
circos.track(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
xplot = get.cell.meta.data("xplot")
circos.lines(xlim, c(mean(ylim), mean(ylim)), lty = 3) # dotted line
by = ifelse(abs(xplot[2] - xplot[1]) > 30, 0.2, 0.5)
for(p in seq(by, 1, by = by)) {
circos.text(p*(xlim[2] - xlim[1]) + xlim[1], mean(ylim) + 0.1,
paste0(p*100, "%"), cex = 0.5, adj = c(0.5, 0), niceFacing = TRUE)
}
}, bg.border = NA)
}# create legend for tumor subclusters
#lgd1 = Legend(labels = names(col_vec[grep("Tumor", names(col_vec))]), title = "Tumor\nSubclusters", legend_gp = gpar(fill = unname(col_vec[grep("Tumor", names(col_vec))])))
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", "TLS"))
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 = 16),
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: 7 x 7
# Groups: dysfunction_score [1]
ImageNumber bcell_patch_sco… dysfunction_sco… celltype n fraction
<int> <fct> <chr> <chr> <int> <dbl>
1 29 TLS low dysfunction B cell 883 0.134
2 33 TLS low dysfunction B cell 4829 0.365
3 86 TLS low dysfunction B cell 2416 0.253
4 95 TLS low dysfunction B cell 1602 0.116
5 109 TLS low dysfunction B cell 1337 0.255
6 114 TLS low dysfunction B cell 2895 0.362
7 118 TLS low dysfunction B cell 782 0.204
# … with 1 more variable: median_frac <dbl>c <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = T) %>%
filter(dysfunction_score %in% c("low dysfunction", "high dysfunction")) %>%
dplyr::count(MM_location, Location, dysfunction_score) %>%
ggplot(aes(axis1 = MM_location, axis2 = Location, axis3 = dysfunction_score,y=n))+
geom_alluvium(aes(fill=as.factor(dysfunction_score)))+
geom_stratum()+
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
theme_minimal()+
scale_x_discrete(limits = c("Met Location", "Punch Location", "Dysfunction Score"), expand = c(.2, .05)) +
theme(text = element_text(size=20)) +
guides(fill=guide_legend(title="Dysfunction Score")) +
ylab("Number of Samples")
plot(c)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"))# im_size
im_size <- as.data.frame(cbind(image_mat_rna$Metadata_Description, (image_mat_rna$Height_cellmask * image_mat_rna$Width_cellmask)/1000000))
names(im_size) <- c("Description", "mm2")
im_size$mm2 <- as.numeric(im_size$mm2)
cxcl13 <- data.frame(colData(sce_rna)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin samples
filter(CXCL13 == 1) %>%
group_by(Description, MM_location, celltype) %>%
summarise(n=n()) %>%
reshape2::dcast(Description + MM_location ~ celltype, value.var = "n", fill = 0)
all_images <- data.frame(colData(sce_rna)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>%
distinct(Description, .keep_all = T) %>%
select(Description)
# left_join to have all images
all_images <- left_join(all_images, cxcl13)
# replace NA
all_images[is.na(all_images)] <- 0
all_images <- all_images %>%
reshape2::melt(id.vars=c("Description", "MM_location"), variable.name = "celltype", value.name = "n")
# add mm2 and calculate density
all_images <- left_join(all_images, im_size)
all_images$density <- all_images$n / all_images$mm2
#
Bcell <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = T) %>%
group_by(Description) %>%
select(Description, bcell_patch_score)
# add grouping
all_images <- left_join(all_images, Bcell[,c("Description","bcell_patch_score")]) %>%
filter(celltype %in% c("CD8+ T cell", "CD8- T cell", "HLA-DR"))
#all_images$interaction <- interaction(all_images$celltype, all_images$bcell_patch_score)
ggplot(all_images,aes(x=bcell_patch_score, y = log10(as.numeric(density+1)), fill=celltype)) +
geom_boxplot(alpha=1, lwd=1, outlier.shape = NA) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=2) +
ylab("CXCL13 Expressing Cells per mm2\n(log10 + 1)") +
xlab("") +
theme_bw() +
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, vjust=1, hjust=1)) +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype[c("CD8+ T cell", "CD8- T cell", "HLA-DR")]),
breaks = names(metadata(sce_rna)$colour_vectors$celltype[c("CD8+ T cell", "CD8- T cell", "HLA-DR")])) +
guides(fill=guide_legend(title="Cell Type", override.aes = c(lwd=0.5)))example <- findPatch(sce_prot[,sce_prot$Description == "C9"], sce_prot[,colData(sce_prot)$celltype %in% c("B cell", "BnT cell")]$cellID,
'cellID',
'Center_X', 'Center_Y',
'Description',
distance = 15,
min_clust_size = 20,
output_colname = "example_patch")Time difference of 2.187578 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'Description',
'example_patch',
distance = 50,
output_colname = "example_milieu",
plot = TRUE)Time difference of 3.264488 secs
[1] "milieus successfully added to sce object"celltypes <- data.frame(colData(sce_prot)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin images
#filter(celltype != "Tumor") %>%
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") %>%
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", "TLS"))
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)# magnify PD1+TCF7+ population
ggplot(celltypes[celltypes$TCF7_PD1 == "PD1+_TCF7+",], aes(x=TCF7_PD1, y=fraction)) +
geom_boxplot(alpha=1, lwd = 0.5, outlier.size = 0.5, aes(fill=bcell_patch_score)) +
theme_bw() +
theme(text = element_text(size = 14),
axis.text.x = element_blank(),
legend.position = "none") +
xlab("") +
ylab("") +
facet_wrap(~celltype, scales = "free") +
coord_cartesian(ylim = c(0,0.05))# what fraction of each celltype is part of a milieu?
cur_dat <- data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
filter(celltype %in% c("CD8+ T cell", "CD4+ T cell")) %>%
mutate(status = paste(TCF7, PD1, sep = "_")) %>%
#filter(status == "TCF7+_PD1+") %>%
mutate(milieu_binary = ifelse(bcell_milieu > 0, 1, 0)) %>%
group_by(Description, milieu_binary, celltype, status) %>%
summarise(n=n()) %>%
group_by(Description, celltype, status) %>%
mutate(fraction_in_milieu = n / sum(n)) %>%
filter(milieu_binary == 1)
# what fraction of the total cell area is made up by cells that are part of a milieu
milieu_area <- data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
mutate(milieu_binary = ifelse(bcell_milieu > 0, 1, 0)) %>%
group_by(Description, milieu_binary) %>%
summarise(area = sum(Area)) %>%
group_by(Description) %>%
mutate(fraction_of_area = area / sum(area)) %>%
filter(row_number() == n())
sum <- left_join(cur_dat, milieu_area, by="Description")
# calculate metric - milieu_fraction divided by milieu-area-fraction (this normalizes the first metric)
sum$metric <- sum$fraction_in_milieu / sum$fraction_of_area
# 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("Area-Normalized Log10 FC") +
xlab("") #+ #coord_cartesian(ylim=c(-1,1.75))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 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=C
[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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] RANN_2.6.1 concaveman_1.1.0
[3] sf_0.9-7 rstatix_0.6.0
[5] ggridges_0.5.3 corrplot_0.84
[7] cytomapper_1.3.1 EBImage_4.32.0
[9] ggalluvial_0.12.3 survminer_0.4.8
[11] cowplot_1.1.1 scater_1.16.2
[13] dittoSeq_1.0.2 coxme_2.2-16
[15] bdsmatrix_1.3-4 survival_3.2-7
[17] ggpmisc_0.3.7 gridExtra_2.3
[19] ggbeeswarm_0.6.0 ggpubr_0.4.0
[21] RColorBrewer_1.1-2 circlize_0.4.12
[23] colorRamps_2.3 ComplexHeatmap_2.4.3
[25] cba_0.2-21 proxy_0.4-24
[27] data.table_1.13.6 forcats_0.5.0
[29] stringr_1.4.0 dplyr_1.0.2
[31] purrr_0.3.4 readr_1.4.0
[33] tidyr_1.1.2 tibble_3.0.4
[35] ggplot2_3.3.3 tidyverse_1.3.0
[37] reshape2_1.4.4 SingleCellExperiment_1.12.0
[39] SummarizedExperiment_1.20.0 Biobase_2.50.0
[41] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[43] IRanges_2.24.1 S4Vectors_0.28.1
[45] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[47] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.1.4 shinydashboard_0.7.1
[3] tidyselect_1.1.0 htmlwidgets_1.5.3
[5] BiocParallel_1.22.0 munsell_0.5.0
[7] units_0.6-7 codetools_0.2-18
[9] withr_2.3.0 colorspace_2.0-0
[11] knitr_1.30 rstudioapi_0.13
[13] ggsignif_0.6.0 labeling_0.4.2
[15] git2r_0.28.0 GenomeInfoDbData_1.2.4
[17] KMsurv_0.1-5 farver_2.0.3
[19] pheatmap_1.0.12 rprojroot_2.0.2
[21] vctrs_0.3.6 generics_0.1.0
[23] xfun_0.20 R6_2.5.0
[25] clue_0.3-58 rsvd_1.0.3
[27] locfit_1.5-9.4 bitops_1.0-6
[29] DelayedArray_0.16.0 assertthat_0.2.1
[31] promises_1.1.1 scales_1.1.1
[33] beeswarm_0.2.3 gtable_0.3.0
[35] rlang_0.4.10 systemfonts_0.3.2
[37] GlobalOptions_0.1.2 splines_4.0.3
[39] broom_0.7.3 yaml_2.2.1
[41] abind_1.4-5 modelr_0.1.8
[43] backports_1.2.1 httpuv_1.5.4
[45] tools_4.0.3 ellipsis_0.3.1
[47] raster_3.4-5 Rcpp_1.0.5
[49] plyr_1.8.6 zlibbioc_1.36.0
[51] classInt_0.4-3 RCurl_1.98-1.2
[53] GetoptLong_1.0.5 viridis_0.5.1
[55] zoo_1.8-8 haven_2.3.1
[57] ggrepel_0.9.0 cluster_2.1.0
[59] fs_1.5.0 magrittr_2.0.1
[61] openxlsx_4.2.3 reprex_0.3.0
[63] whisker_0.4 hms_0.5.3
[65] mime_0.9 evaluate_0.14
[67] fftwtools_0.9-9 xtable_1.8-4
[69] rio_0.5.16 jpeg_0.1-8.1
[71] readxl_1.3.1 shape_1.4.5
[73] compiler_4.0.3 V8_3.4.0
[75] KernSmooth_2.23-18 crayon_1.3.4
[77] htmltools_0.5.0 later_1.1.0.1
[79] tiff_0.1-6 lubridate_1.7.9.2
[81] DBI_1.1.0 dbplyr_2.0.0
[83] Matrix_1.3-2 car_3.0-10
[85] cli_2.2.0 pkgconfig_2.0.3
[87] km.ci_0.5-2 foreign_0.8-81
[89] sp_1.4-5 xml2_1.3.2
[91] svglite_1.2.3.2 vipor_0.4.5
[93] XVector_0.30.0 rvest_0.3.6
[95] digest_0.6.27 rmarkdown_2.6
[97] cellranger_1.1.0 survMisc_0.5.5
[99] edgeR_3.30.3 DelayedMatrixStats_1.10.1
[101] gdtools_0.2.3 curl_4.3
[103] shiny_1.5.0 rjson_0.2.20
[105] lifecycle_0.2.0 nlme_3.1-151
[107] jsonlite_1.7.2 carData_3.0-4
[109] BiocNeighbors_1.6.0 viridisLite_0.3.0
[111] limma_3.44.3 fansi_0.4.1
[113] pillar_1.4.7 lattice_0.20-41
[115] fastmap_1.0.1 httr_1.4.2
[117] glue_1.4.2 zip_2.1.1
[119] png_0.1-7 svgPanZoom_0.3.4
[121] class_7.3-17 stringi_1.5.3
[123] BiocSingular_1.4.0 e1071_1.7-4
[125] irlba_2.3.3