Last updated: 2021-06-22
Checks: 6 1
Knit directory: melanoma_publication_old_data/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0f72ef1 | toobiwankenobi | 2021-05-11 | figure adaptations |
| html | 0f72ef1 | toobiwankenobi | 2021-05-11 | figure adaptations |
| Rmd | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| html | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| Rmd | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| html | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| Rmd | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
| Rmd | afa7957 | toobiwankenobi | 2021-02-08 | minor changes on figures and figure order |
| Rmd | 20a1458 | toobiwankenobi | 2021-02-04 | adapt figure order |
| Rmd | f9bb33a | toobiwankenobi | 2021-02-04 | new Figure 5 and minor changes in figure order |
| Rmd | 9442cb9 | toobiwankenobi | 2020-12-22 | add all new files |
| Rmd | 77466b7 | Tobias Hoch | 2020-10-22 | work on subfigures |
| Rmd | f643fb2 | toobiwankenobi | 2020-10-19 | add tumor analysis |
| Rmd | 58c40e5 | toobiwankenobi | 2020-10-19 | correct files that don’t work |
| Rmd | 1af3353 | toobiwankenobi | 2020-10-16 | add stuff |
This script generates plots for Figure 3.
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
value ?
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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visible FALSE
code/helper_functions/validityChecks.R
value ?
visible FALSE library(cytomapper)
library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
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)sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
# remove LN margin samples and control samples
sce_rna$PunchLocation <- paste(sce_rna$MM_location, sce_rna$Location, sep = "_")
sce_prot$PunchLocation <- paste(sce_prot$MM_location, sce_prot$Location, sep = "_")
sce_rna <- sce_rna[,sce_rna$PunchLocation != "LN_M" & sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$PunchLocation != "LN_M" & sce_prot$Location != "CTRL"]
# image
image_mat_prot <- read.csv("data/data_for_analysis/protein/Image.csv")
# surv_dat
dat_survival_prot <- fread(file = "data/data_for_analysis/protein/clinical_data_protein.csv")im_size <- as.data.frame(cbind(image_mat_prot$Metadata_Description, (image_mat_prot$Height_cellmask * image_mat_prot$Width_cellmask)/1000000))
names(im_size) <- c("Description", "mm2")
im_size$mm2 <- as.numeric(im_size$mm2)
im_size[im_size$Description %in% c("G1", "G1 - split"), ]$mm2 <- mean(im_size[im_size$Description %in% c("G1", "G1 - split"), ]$mm2)
im_size <- im_size[im_size != "G1 - split",]# loop through all patches
for(i in c("cxcl13only_clust")){
# subset sce object to only contain community cells
sce_sub <- sce_rna[,colData(sce_rna)[,i] > 0]
assay(sce_sub, "scaled_asinh") <- t(scale(t(assay(sce_sub, "asinh"))))
# create UAMP
set.seed(12345)
sce_sub <- runDiffusionMap(sce_sub,
exprs_values = "asinh",
subset_row = rowData(sce_sub)$good_marker,
ncomponents = 2)
# add patch size to sce
cur_df <- data.frame(colData(sce_sub))
clust_size <- cur_df %>%
group_by(cur_df[,i]) %>%
summarise(clust_size = n())
names(clust_size)[1] <- i
cur_df <- left_join(cur_df, clust_size)
sce_sub$clust_size = as.numeric(log10(cur_df$clust_size))
# col by clust size
a <- dittoDimPlot(sce_sub,
reduction.use = "DiffusionMap",
var = "clust_size",
size = 1,
legend.show = TRUE,
opacity = 1,
max.color = "red", min.color = "blue",
main = NULL,
legend.title = "Patch Size (log10)") +
xlim(quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.95)[[1]]) +
ylim(quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.95)[[1]]) +
theme_bw() +
theme(text = element_text(size=18))
# col by celltype
b <- dittoDimPlot(sce_sub,
reduction.use = "DiffusionMap",
var = "celltype",
opacity = 1,
color.panel = metadata(sce_sub)$colour_vector$celltype,
size = 1,
legend.show = TRUE,
main = NULL,
legend.title = "Cell Type") +
theme_bw() +
xlim(quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.95)[[1]]) +
ylim(quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.95)[[1]]) +
theme(text = element_text(size=18)) +
guides(colour = guide_legend(override.aes = list(alpha = 1, size=3)))
leg_a <- cowplot::get_legend(a)
leg_b <- cowplot::get_legend(b)
}Warning: Removed 1063 rows containing missing values (geom_point).
Warning: Removed 1063 rows containing missing values (geom_point).sce_sub <- sce_rna[,colData(sce_rna)[,"cxcl13only_clust"] > 0]
# add patch size to sce
cur_df <- data.frame(colData(sce_sub))
clust_size <- cur_df %>%
group_by(cxcl13only_clust) %>%
summarise(clust_size = n())
names(clust_size)[1] <- "cxcl13only_clust"
cur_df <- left_join(cur_df, clust_size)
sce_sub$clust_size = as.numeric(log10(cur_df$clust_size))
# add clust size correlation plot
clust_size <- data.frame(colData(sce_sub)) %>%
distinct(Description, cxcl13only_clust, .keep_all = T) %>%
group_by(Description) %>%
summarise(maxClustSize = max(clust_size))
Bcell_patch <- data.frame(colData(sce_prot)) %>%
group_by(Description, bcell_patch) %>%
summarise(n=n()) %>%
mutate(n = ifelse(bcell_patch == 0, 0, n)) %>%
mutate(maxPatchSize = log10(max(n+1))) %>%
distinct(Description, .keep_all = T) %>%
select(Description, maxPatchSize)
Bcell_patch <- left_join(Bcell_patch, clust_size)
Bcell_patch[is.na(Bcell_patch$maxClustSize), ]$maxClustSize <- 0
Bcell <- data.frame(colData(sce_prot)) %>%
group_by(Description, celltype) %>%
summarise(n=n()) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0) %>%
select(Description, `B cell`)
Bcell <- left_join(Bcell, clust_size)
Bcell[is.na(Bcell$maxClustSize), ]$maxClustSize <- 0
# only when not 0 - CHANGE?
c <- ggplot(Bcell_patch[rowSums(Bcell_patch[,-1]) > 0,], aes(x = maxClustSize, y = maxPatchSize, label=Description)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..rr.label.. ,")")),
size = 6, cor.coef.name = "R", label.sep="\n", label.y = 0.5, label.x = 2) +
ylab("Max Size of B cell\nPatches (log10)") +
xlab("Max Size of CXCL13 Patches (log10)") +
theme_bw() +
theme(text = element_text(size=18))
d <- ggplot(Bcell, aes(x = maxClustSize, y = `B cell`, label=Description)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..rr.label.. ,")")),
size = 6, cor.coef.name = "R", label.sep="\n", label.y = 0.05, label.x = 2) +
ylab("B Cell Fraction") +
xlab("Max Size of CXCL13 Patches (log10)") +
theme_bw() +
theme(text = element_text(size=18))
plot_grid(grid.arrange(a + theme(legend.position = "none"),
b + theme(legend.position = "none"),
ncol = 2),
grid.arrange(d,c, ncol = 2),
ncol = 1,
rel_heights = c(0.65,0.35))Warning: Removed 1063 rows containing missing values (geom_point).
Warning: Removed 1063 rows containing missing values (geom_point).grid.arrange(leg_a)grid.arrange(leg_b)cur_dat <- data.frame(colData(sce_prot)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin images
filter(Location != "CTRL") %>%
mutate(TCF7_PD1 = paste(TCF7, PD1, sep = "_")) %>%
group_by(Description, bcell_patch_score, celltype, TCF7_PD1) %>%
summarise(n=n()) %>%
reshape2::dcast(Description + bcell_patch_score + celltype ~ TCF7_PD1, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("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"))
cur_dat <- left_join(cur_dat, im_size)
cur_dat$density <- cur_dat$n / cur_dat$mm2
cur_dat <- cur_dat %>%
filter(TCF7_PD1 %in% c("TCF7+_PD1+", "TCF7+_PD1-")) %>%
reshape2::dcast(Description + bcell_patch_score + celltype ~ TCF7_PD1, value.var = "density", fill=0)
ggplot(cur_dat, aes(x=log10(`TCF7+_PD1+`+1), y=log10(`TCF7+_PD1-`+1))) +
geom_point(size=3) +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..rr.label.. ,")")),
size = 7, cor.coef.name = "R", label.sep="\n", label.y.npc = "top") +
theme_bw() +
theme(text = element_text(size=16)) +
xlab("TCF7+PD1+ Cells per mm2 (log10+1)") +
ylab("TCF7+PD1- Cells per mm2 (log10+1)") +
facet_wrap(~celltype)# select blocks in ICI subgroup that are treatment-naive and non-adjuvant
blocks <- unique(sce_prot[,sce_prot$treatment_status_before_surgery == "naive" &
sce_prot$treatment_group_after_surgery == "ICI" &
sce_prot$Adjuvant == "n"]$BlockID)
# subset survival data (only data points that contain info for 3m response)
dat_survival_prot_sub <- dat_survival_prot[dat_survival_prot$BlockID %in% blocks &
is.na(dat_survival_prot$Status_at_3m) == FALSE, ]
# remove LN margin samples
dat_survival_prot_sub <- dat_survival_prot_sub %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M")
# group sizes
dat_survival_prot_sub %>%
distinct(PatientID, .keep_all = T) %>%
group_by(Status_at_3m) %>%
summarise(n=n())# A tibble: 2 x 2
Status_at_3m n
<chr> <int>
1 NR 7
2 R 4cur_dat <- data.frame(colData(sce_prot[,sce_prot$BlockID %in% unique(dat_survival_prot_sub$BlockID)])) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin images
#filter(celltype != "Tumor") %>%
mutate(TCF7_PD1 = paste(TCF7, PD1, sep = "_")) %>%
group_by(PatientID,BlockID, Description, bcell_patch_score, celltype, TCF7_PD1) %>%
summarise(n=n()) %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score + TCF7_PD1 ~ celltype, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score", "TCF7_PD1"),
variable.name = "celltype", value.name = "n") %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score + celltype ~ TCF7_PD1, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score", "celltype"),
variable.name = "TCF7_PD1", value.name = "n") %>%
group_by(Description, celltype) %>%
mutate(fraction = n/sum(n)) %>%
mutate(total_cells = sum(n)) %>%
ungroup() %>%
filter(celltype %in% c("CD8+ T cell", "CD4+ T cell"))
cur_dat <- left_join(cur_dat, dat_survival_prot_sub[,c("BlockID", "Status_at_3m", "Primary_melanoma_type")]) %>%
distinct(Description, celltype, TCF7_PD1, .keep_all = T)
cur_dat <- left_join(cur_dat, im_size[im_size$Description %in% unique(cur_dat$Description),])
cur_dat$n_per_mm2 <- cur_dat$n / cur_dat$mm2
cur_dat$celltype2 <- paste(cur_dat$celltype, cur_dat$TCF7_PD1, sep = ", ")
stat.test <- cur_dat %>%
filter(TCF7_PD1 %in% c("TCF7+_PD1+", "TCF7+_PD1-")) %>%
mutate(log10_n_per_mm2 = log10(n_per_mm2 + 1)) %>%
group_by(celltype2) %>%
wilcox_test(data = ., log10_n_per_mm2 ~ Status_at_3m) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1))
# check adjusted p-values with another function
p.adjust(stat.test$p, method = "BH")[1] 0.1870000 0.1546667 0.1546667 0.1546667info <- cur_dat[,c("celltype2", "TCF7_PD1")] %>%
distinct(celltype2, .keep_all = T)
# filter relevant tests
stat.test <- left_join(stat.test, info) %>%
filter(TCF7_PD1 %in% c("TCF7+_PD1+", "TCF7+_PD1-"))
stat.test$p_and_p.adj <- paste(paste("p = ", round(stat.test$p,2), sep = ""), paste("p.adj = ", round(stat.test$p.adj,2), sep = ""), sep = "\n")
ggplot(cur_dat[cur_dat$TCF7_PD1 %in% c("TCF7+_PD1+", "TCF7+_PD1-"),], aes(x=Status_at_3m, y=log10(n_per_mm2+1), label=PatientID)) +
geom_boxplot(alpha=.4, outlier.shape = NA, aes(fill=Status_at_3m)) +
geom_beeswarm(size=3) +
geom_label_repel(aes(label=PatientID), max.overlaps = 10, size = 5, alpha=.5, force_pull = 2, force = 0.5) +
stat_pvalue_manual(stat.test, label = "p_and_p.adj", size=5, label.x.npc = "left", y.position = -0.9, bracket.size = 0) +
theme_bw() +
theme(text = element_text(size=18)) +
ylab("Cells per mm2 (log10)") +
scale_fill_manual(values=c("blue", "orange")) +
xlab("") +
guides(fill=guide_legend(title="Response 3m", override.aes = c(lwd=0.5, size=1))) +
facet_wrap(~celltype2, scales = "free")Warning: Duplicated aesthetics after name standardisation: sizeWarning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
increasing max.overlapsWarning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
increasing max.overlaps# number of patients
unique(cur_dat$PatientID) [1] "29" "33" "38" "4" "40" "84" "86" "87" "91" "94" "III"cur_dat %>%
distinct(PatientID, .keep_all = T) %>%
group_by(Status_at_3m) %>%
summarise(n=n())# A tibble: 2 x 2
Status_at_3m n
<chr> <int>
1 NR 7
2 R 4# number of images
unique(cur_dat$Description) [1] "D5" "F5" "M10" "N10" "H2" "J7" "K7" "L7" "H7" "I7" "L10" "L8"
[13] "J8" "H8" "E2" "F2" "A4" "P3" "A2" "L1" "M1" "D4" "E4" "F4" cur_dat %>%
distinct(Description, .keep_all = T) %>%
group_by(Status_at_3m) %>%
summarise(n=n())# A tibble: 2 x 2
Status_at_3m n
<chr> <int>
1 NR 16
2 R 8targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
# top abundant chemokines
cur_rna <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL")
# protein data
cur_prot <- data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL")
# sum
rna_sum <- cur_rna %>%
group_by(Description) %>%
mutate(total_cells=n()) %>%
ungroup() %>%
group_by(Description, total_cells, expressor) %>%
summarise(n=n()) %>%
mutate(fraction=n/total_cells) %>%
reshape2::dcast(Description ~ expressor, value.var = "fraction", fill = 0)
# only keep highly abundant chemokines
rna_sum <- rna_sum[,c("Description", targets)]
prot_sum <- cur_prot %>%
mutate(celltype2 = ifelse(celltype %in% c("CD4+ T cell", "CD8+ T cell"),
paste(paste(celltype, TCF7, sep="_"), PD1, sep = "_"), celltype)) %>%
group_by(Description, celltype2) %>%
summarise(n = n()) %>%
group_by(Description) %>%
mutate(fraction = n/sum(n)) %>%
#filter(!celltype2 %in% c("CD4+ T cell_TCF7+_PD1+", "CD4+ T cell_TCF7-_PD1+", "CD8+ T cell_TCF7+_PD1+")) %>%
reshape2::dcast(Description ~ celltype2, value.var = "fraction", fill = 0) %>%
select(Description,contains(c("CD8+ T cell", "CD4+ T cell")))
# equal images
all(rna_sum$Description == prot_sum$Description)[1] TRUE# correlation
cor <- cor(rna_sum[,-1], prot_sum[,-1], method = "pearson")
ha <- t(str_split_fixed(colnames(cor), "_", n=3))
dat_sum <- cur_prot %>%
filter(celltype %in% c("CD4+ T cell", "CD8+ T cell")) %>%
mutate(celltype2 = ifelse(celltype %in% c("CD4+ T cell", "CD8+ T cell"),
paste(paste(celltype, TCF7, sep="_"), PD1, sep = "_"), celltype)) %>%
group_by(celltype2) %>%
summarise(n = n())
ha1 <- HeatmapAnnotation("TCF7_PD1" = anno_text(paste(ha[2,], ha[3,], sep = " ")),
"Cell Type" = ha[1,],
"Number of Cells" = anno_barplot(dat_sum$n,
height = unit(2,"cm"),
axis_param = list(gp = gpar(fontsize=14))),
"Numbers" = anno_text(dat_sum$n,
which = "column",
rot = 0,
height = unit(0.5,"cm"),
just = "center",
location = 0.5),
col = list("Cell Type" = metadata(sce_prot)$colour_vectors$celltype[c("CD4+ T cell", "CD8+ T cell")]),
show_legend = FALSE)
h <- Heatmap(cor,
name = "Pearson\nCorrelation",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_names = FALSE,
show_row_names = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", cor[i, j]), x, y, gp = gpar(fontsize = 15, col = "black"))
},
col = colorRamp2(c(-1, 0, 1), c("red", "white", "blue")),
row_title = "Expressor",
row_names_side = "left",
top_annotation = ha1,
width = unit(18, "cm"),
height = unit(10, "cm"),
show_heatmap_legend = FALSE)
# draw heatmap
draw(h)lgd1 = color_mapping_legend(h@matrix_color_mapping, plot = FALSE, legend_direction = "vertical", legend_width=unit(3,"cm"), at = c(-1:1))
lgd2 = color_mapping_legend(ha1@anno_list$`Cell Type`@color_mapping, plot = FALSE, legend_direction = "vertical", nrow = 4)
lgd_list = packLegend(lgd1,lgd2,direction = "vertical", gap = unit(1,"cm"))
draw(lgd_list)
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 ggrepel_0.9.0
[3] circlize_0.4.12 cowplot_1.1.1
[5] ggpubr_0.4.0 corrplot_0.84
[7] ggbeeswarm_0.6.0 dittoSeq_1.0.2
[9] scater_1.16.2 ComplexHeatmap_2.4.3
[11] ggpmisc_0.3.7 gridExtra_2.3
[13] RColorBrewer_1.1-2 colorRamps_2.3
[15] data.table_1.13.6 forcats_0.5.0
[17] stringr_1.4.0 dplyr_1.0.2
[19] purrr_0.3.4 readr_1.4.0
[21] tidyr_1.1.2 tibble_3.0.4
[23] ggplot2_3.3.3 tidyverse_1.3.0
[25] reshape2_1.4.4 cytomapper_1.0.0
[27] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[29] Biobase_2.50.0 GenomicRanges_1.42.0
[31] GenomeInfoDb_1.26.2 IRanges_2.24.1
[33] S4Vectors_0.28.1 BiocGenerics_0.36.0
[35] MatrixGenerics_1.2.0 matrixStats_0.57.0
[37] EBImage_4.32.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.1.4 tidyselect_1.1.0
[3] htmlwidgets_1.5.3 ranger_0.12.1
[5] BiocParallel_1.22.0 munsell_0.5.0
[7] destiny_3.2.0 codetools_0.2-18
[9] withr_2.3.0 colorspace_2.0-0
[11] knitr_1.30 rstudioapi_0.13
[13] robustbase_0.93-7 ggsignif_0.6.0
[15] vcd_1.4-8 VIM_6.0.0
[17] TTR_0.24.2 labeling_0.4.2
[19] git2r_0.28.0 GenomeInfoDbData_1.2.4
[21] farver_2.0.3 pheatmap_1.0.12
[23] rprojroot_2.0.2 vctrs_0.3.6
[25] generics_0.1.0 xfun_0.20
[27] ggthemes_4.2.0 R6_2.5.0
[29] clue_0.3-58 rsvd_1.0.3
[31] RcppEigen_0.3.3.9.1 locfit_1.5-9.4
[33] bitops_1.0-6 DelayedArray_0.16.0
[35] assertthat_0.2.1 promises_1.1.1
[37] scales_1.1.1 nnet_7.3-14
[39] beeswarm_0.2.3 gtable_0.3.0
[41] rlang_0.4.10 scatterplot3d_0.3-41
[43] splines_4.0.3 GlobalOptions_0.1.2
[45] hexbin_1.28.2 broom_0.7.3
[47] yaml_2.2.1 abind_1.4-5
[49] modelr_0.1.8 backports_1.2.1
[51] httpuv_1.5.4 tools_4.0.3
[53] ellipsis_0.3.1 raster_3.4-5
[55] proxy_0.4-24 ggridges_0.5.3
[57] Rcpp_1.0.5 plyr_1.8.6
[59] zlibbioc_1.36.0 RCurl_1.98-1.2
[61] GetoptLong_1.0.5 viridis_0.5.1
[63] zoo_1.8-8 haven_2.3.1
[65] cluster_2.1.0 fs_1.5.0
[67] magrittr_2.0.1 RSpectra_0.16-0
[69] openxlsx_4.2.3 lmtest_0.9-38
[71] reprex_0.3.0 pcaMethods_1.80.0
[73] whisker_0.4 hms_0.5.3
[75] fftwtools_0.9-9 evaluate_0.14
[77] smoother_1.1 rio_0.5.16
[79] jpeg_0.1-8.1 readxl_1.3.1
[81] shape_1.4.5 compiler_4.0.3
[83] crayon_1.3.4 htmltools_0.5.1.1
[85] mgcv_1.8-33 later_1.1.0.1
[87] tiff_0.1-6 lubridate_1.7.9.2
[89] DBI_1.1.0 dbplyr_2.0.0
[91] MASS_7.3-53 boot_1.3-25
[93] Matrix_1.3-2 car_3.0-10
[95] cli_2.2.0 pkgconfig_2.0.3
[97] foreign_0.8-81 laeken_0.5.1
[99] sp_1.4-5 xml2_1.3.2
[101] vipor_0.4.5 XVector_0.30.0
[103] rvest_0.3.6 digest_0.6.27
[105] rmarkdown_2.6 cellranger_1.1.0
[107] edgeR_3.30.3 DelayedMatrixStats_1.10.1
[109] curl_4.3 ggplot.multistats_1.0.0
[111] rjson_0.2.20 nlme_3.1-151
[113] lifecycle_0.2.0 jsonlite_1.7.2
[115] carData_3.0-4 BiocNeighbors_1.6.0
[117] viridisLite_0.3.0 limma_3.44.3
[119] fansi_0.4.1 pillar_1.4.7
[121] lattice_0.20-41 httr_1.4.2
[123] DEoptimR_1.0-8 glue_1.4.2
[125] xts_0.12.1 zip_2.1.1
[127] png_0.1-7 class_7.3-17
[129] stringi_1.5.3 RcppHNSW_0.3.0
[131] BiocSingular_1.4.0 irlba_2.3.3
[133] knn.covertree_1.0 e1071_1.7-4