Last updated: 2021-02-10
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Knit directory: melanoma_publication_old_data/
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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 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)sce_rna = readRDS(file = "data/sce_RNA.rds")
sce_prot = readRDS(file = "data/sce_protein.rds")
# meta data
dat_relation = fread(file = "data/protein/Object relationships.csv",stringsAsFactors = FALSE)
dat_relation_rna = fread(file = "data/RNA/Object relationships.csv",stringsAsFactors = FALSE)
# image
image_mat_rna <- read.csv("data/rna/Image.csv")
# surv_dat
dat_survival_prot <- fread(file = "data/protein/clinical_data_protein.csv")
time_data = fread(file = "data/survdat_for_modelling.csv",stringsAsFactors = FALSE)
time_data$BlockID <- time_data$Block.ID# 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$celltype_first == "Tcytotoxic",]
cur_dat_relation_subcluster <- cur_dat_relation_subcluster[cur_dat_relation_subcluster$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("Tcytotoxic"),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") %>%
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_jitter(alpha=1, position = position_jitterdodge(dodge.width = 0.75,jitter.width = 0.1), size = 4,
#aes(x = celltype, y = fraction, #col=ifelse(celltype %in% c("Bcell", "BnTcell"), bcell_patch_score, " "),
#group = dysfunction_score)) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=1) +
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="Bcell 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 == "Bcell") %>%
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 Bcell 885 0.134
2 33 TLS low dysfunction Bcell 4831 0.365
3 86 TLS low dysfunction Bcell 2400 0.252
4 95 TLS low dysfunction Bcell 1603 0.116
5 109 TLS low dysfunction Bcell 1333 0.254
6 114 TLS low dysfunction Bcell 2881 0.360
7 118 TLS low dysfunction Bcell 787 0.206
# … 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="Bcell 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("Tcytotoxic", "Tcell", "HLA-DR"))
#all_images$interaction <- interaction(all_images$celltype, all_images$bcell_patch_score)
ggplot(all_images,aes(x=bcell_patch_score, y = log10(as.numeric(density+1)), fill=celltype)) +
geom_boxplot(alpha=1, lwd=1, outlier.shape = NA) +
#scale_x_discrete(labels = rep(unique(all_images$bcell_patch_score), each = 3)) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=2) +
#geom_line(aes(group = Description), alpha = ifelse(all_images$celltype != "HLA-DR", 0.6, 0), colour = "black") +
#geom_point(aes(group= interaction), position = position_dodge(0.75)) +
ylab("CXCL13 Expressing Cells per mm2\n(log10 + 1)") +
xlab("") +
theme_bw() +
theme(text = element_text(size=18),
axis.text.x = element_text(angle=45, vjust=1, hjust=1)) +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype[c("Tcytotoxic", "Tcell", "HLA-DR")]),
breaks = names(metadata(sce_rna)$colour_vectors$celltype[c("Tcytotoxic", "Tcell", "HLA-DR")])) +
#scale_color_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype[c("Tcytotoxic", "Tcell", "HLA-DR")]),
#breaks = names(metadata(sce_rna)$colour_vectors$celltype[c("Tcytotoxic", "Tcell", "HLA-DR")]),
#guide = FALSE) +
guides(fill=guide_legend(title="Cell Type", override.aes = c(lwd=0.5)))example <- findClusters(sce_prot[,sce_prot$Description == "C9"], sce_prot[,colData(sce_prot)$celltype %in% c("Bcell", "BnTcell")]$cellID,
'cellID',
'Center_X', 'Center_Y',
'Description',
distance = 15,
min_clust_size = 20,
output_colname = "example_patch")Time difference of 2.19296 secs
[1] "clusters successfully added to sce object"example <- findCommunity(example,
'cellID',
'Center_X', 'Center_Y',
'Description',
'example_patch',
distance = 50,
output_colname = "example_milieu",
plot = TRUE)Time difference of 3.302347 secs
[1] "communities 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("Tcytotoxic", "Thelper"))
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") %>%
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="Bcell Score", override.aes = c(lwd=0.5))) +
xlab("") +
ylab("Fraction of Population") +
facet_wrap(~celltype, scales = "free") +
ylim(-0.1,1.1)# what fraction of each celltype is part of a milieu?
cur_dat <- data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
filter(celltype %in% c("Tcytotoxic", "Thelper")) %>%
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$fraction_of_area
# one-sample t test
stat.test <- sum %>%
group_by(celltype, status) %>%
mutate(log10_metric = log10(metric)) %>%
t_test(log10_metric ~ 1, mu = 0, alternative = "greater", detailed = TRUE) %>%
adjust_pvalue() %>%
add_significance("p.adj")
ggplot(sum, aes(x=sum$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=7) +
ylab("Area-Normalized Log10 Fold-Change") +
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] rstatix_0.6.0 ggridges_0.5.3
[3] corrplot_0.84 cytomapper_1.3.1
[5] EBImage_4.32.0 ggalluvial_0.12.3
[7] survminer_0.4.8 cowplot_1.1.1
[9] scater_1.16.2 dittoSeq_1.0.2
[11] coxme_2.2-16 bdsmatrix_1.3-4
[13] survival_3.2-7 ggpmisc_0.3.7
[15] gridExtra_2.3 ggbeeswarm_0.6.0
[17] ggpubr_0.4.0 RColorBrewer_1.1-2
[19] circlize_0.4.12 colorRamps_2.3
[21] ComplexHeatmap_2.4.3 cba_0.2-21
[23] proxy_0.4-24 data.table_1.13.6
[25] forcats_0.5.0 stringr_1.4.0
[27] dplyr_1.0.2 purrr_0.3.4
[29] readr_1.4.0 tidyr_1.1.2
[31] tibble_3.0.4 ggplot2_3.3.3
[33] tidyverse_1.3.0 reshape2_1.4.4
[35] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[37] Biobase_2.50.0 GenomicRanges_1.42.0
[39] GenomeInfoDb_1.26.2 IRanges_2.24.1
[41] S4Vectors_0.28.1 BiocGenerics_0.36.0
[43] MatrixGenerics_1.2.0 matrixStats_0.57.0
[45] 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 concaveman_1.1.0
[29] bitops_1.0-6 DelayedArray_0.16.0
[31] assertthat_0.2.1 promises_1.1.1
[33] scales_1.1.1 beeswarm_0.2.3
[35] gtable_0.3.0 rlang_0.4.10
[37] systemfonts_0.3.2 GlobalOptions_0.1.2
[39] splines_4.0.3 broom_0.7.3
[41] yaml_2.2.1 abind_1.4-5
[43] modelr_0.1.8 backports_1.2.1
[45] httpuv_1.5.4 tools_4.0.3
[47] ellipsis_0.3.1 raster_3.4-5
[49] Rcpp_1.0.5 plyr_1.8.6
[51] zlibbioc_1.36.0 classInt_0.4-3
[53] RCurl_1.98-1.2 GetoptLong_1.0.5
[55] viridis_0.5.1 zoo_1.8-8
[57] haven_2.3.1 ggrepel_0.9.0
[59] cluster_2.1.0 fs_1.5.0
[61] magrittr_2.0.1 openxlsx_4.2.3
[63] RANN_2.6.1 reprex_0.3.0
[65] whisker_0.4 hms_0.5.3
[67] mime_0.9 evaluate_0.14
[69] fftwtools_0.9-9 xtable_1.8-4
[71] rio_0.5.16 jpeg_0.1-8.1
[73] readxl_1.3.1 shape_1.4.5
[75] compiler_4.0.3 V8_3.4.0
[77] KernSmooth_2.23-18 crayon_1.3.4
[79] htmltools_0.5.0 later_1.1.0.1
[81] tiff_0.1-6 lubridate_1.7.9.2
[83] DBI_1.1.0 dbplyr_2.0.0
[85] sf_0.9-7 Matrix_1.3-2
[87] car_3.0-10 cli_2.2.0
[89] pkgconfig_2.0.3 km.ci_0.5-2
[91] foreign_0.8-81 sp_1.4-5
[93] xml2_1.3.2 svglite_1.2.3.2
[95] vipor_0.4.5 XVector_0.30.0
[97] rvest_0.3.6 digest_0.6.27
[99] rmarkdown_2.6 cellranger_1.1.0
[101] survMisc_0.5.5 edgeR_3.30.3
[103] DelayedMatrixStats_1.10.1 gdtools_0.2.3
[105] curl_4.3 shiny_1.5.0
[107] rjson_0.2.20 lifecycle_0.2.0
[109] nlme_3.1-151 jsonlite_1.7.2
[111] carData_3.0-4 BiocNeighbors_1.6.0
[113] viridisLite_0.3.0 limma_3.44.3
[115] fansi_0.4.1 pillar_1.4.7
[117] lattice_0.20-41 fastmap_1.0.1
[119] httr_1.4.2 glue_1.4.2
[121] zip_2.1.1 png_0.1-7
[123] svgPanZoom_0.3.4 class_7.3-17
[125] stringi_1.5.3 BiocSingular_1.4.0
[127] e1071_1.7-4 irlba_2.3.3