Last updated: 2021-04-13
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Knit directory: melanoma_publication_old_data/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 | 2ac1833 | toobiwankenobi | 2021-01-08 | changes to Figures |
| Rmd | 545c207 | toobiwankenobi | 2020-12-22 | clean up branch |
| Rmd | 64d1f24 | toobiwankenobi | 2020-12-22 | start new branch with clean scripts |
| Rmd | 9442cb9 | toobiwankenobi | 2020-12-22 | add all new files |
| Rmd | 1af3353 | toobiwankenobi | 2020-10-16 | add stuff |
| Rmd | a6b51cd | toobiwankenobi | 2020-10-14 | clean scripts, add new subfigures |
| Rmd | d8819f2 | toobiwankenobi | 2020-10-08 | read new data (nuclei expansion) and adapt scripts |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
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
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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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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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code/helper_functions/scatter_function.R
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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(umap)
library(data.table)
library(fpc)
library(ggplot2)
library(cba)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggbeeswarm)
library(destiny)
library(scater)
library(dittoSeq)
library(gridExtra)
library(ggpmisc)
library(neighbouRhood)
library(cowplot)
library(viridis)
library(ggpubr)
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/rna/Object relationships.csv",stringsAsFactors = FALSE)# prepare data
dat_relation$cellID_first <- paste("RNA", paste(dat_relation$`First Image Number`, dat_relation$`First Object Number`, sep = "_"), sep = "_")
dat_relation$cellID_second <- paste("RNA", paste(dat_relation$`Second Image Number`, dat_relation$`Second Object Number`, sep = "_"), sep = "_")# Load and prepare
dat_cells = fread(file = "data/data_for_analysis/rna/cell.csv",stringsAsFactors = FALSE)
dat_relation = fread(file = "data/data_for_analysis/rna/Object relationships.csv",stringsAsFactors = FALSE)
# Number of cores used for multicore:
if(detectCores() >= 12){
ncores = round(detectCores()/1.25,0)
}
if(detectCores() > 1 & detectCores() < 12){
ncores = round(detectCores()/2,0)
}
if(detectCores() == 1){
ncores = 1
}
n_perm = 100 start = Sys.time()
cur_sce <- as.data.frame(colData(sce_rna))
# add same cellID to dat_cells as in sce object
dat_cells$cellID <- paste("RNA_", paste(dat_cells$ImageNumber, dat_cells$ObjectNumber, sep = "_"), sep = "")
image_df <- data.frame()
for(size in c(2,3,4,5,6)) {
images <- data.frame()
for(i in colnames(cur_sce[,grepl("CCL|CXCL",colnames(cur_sce))])){
# add chemokine info to celltype
sce_info <- cur_sce[,c("cellID", i , "Description")]
# add celltype information
dat_cells_tmp <- left_join(as.data.frame(dat_cells), sce_info, by = "cellID")
#assign labels and groups
dat_cells_tmp$label <- dat_cells_tmp[,i]
dat_cells_tmp$group <- dat_cells_tmp$Description
dat_cells_tmp <- as.data.table(dat_cells_tmp)
# subset dat_relation and dat_cells
dat_cells_sub <- dat_cells_tmp#[dat_cells$Description == "P3",]
dat_relation_sub <- dat_relation#[dat_relation$`First Image Number` == unique(sce_rna[,sce_rna$Description == "P3"]$ImageNumber),]
# Prepare the data
d = neighbouRhood::prepare_tables(dat_cells_sub, dat_relation_sub)
# Calculate the baseline statistics
dat_baseline = neighbouRhood::apply_labels(d[[1]], d[[2]]) %>%
neighbouRhood::aggregate_classic_patch(., patch_size = size)
# Calculate the permutation statistics
# This will run the test using parallel computing. The name of the idcol does actually not matter.
set.seed(12312)
dat_perm = rbindlist(mclapply(1:n_perm, function(x){
dat_labels = neighbouRhood::shuffle_labels(d[[1]])
neighbouRhood::apply_labels(dat_labels, d[[2]]) %>%
neighbouRhood::aggregate_classic_patch(., patch_size = size)
},mc.cores = ncores
), idcol = 'run')
# calc p values
dat_p <- neighbouRhood::calc_p_vals(dat_baseline, dat_perm, n_perm = n_perm, p_tresh = 0.01)
# select interactions between chemokine+ cells
dat_p$interaction <- paste(dat_p$FirstLabel, dat_p$SecondLabel, sep = "_")
dat_p_wide <- dat_p %>%
reshape2::dcast(group ~ interaction, value.var = "sigval", fill = 0) %>%
select(group, `1_1`)
summary <- as.data.frame(dat_p_wide) %>%
group_by(`1_1`) %>%
summarise(n=n(),.groups = 'drop') %>%
ungroup() %>%
mutate(percentage_sig = (n/sum(n)) * 100)
images <- rbind(images, cbind(summary[1,], i))
}
# calculate percentage of images with significant patches
images$percentage_sig <- 100 - images$percentage_sig
images$patch_size <- size
images <- select(images, percentage_sig, i, patch_size)
colnames(images) <- c("significant_images", "chemokine", "patch_size")
# add to data.frame
image_df <- rbind(image_df, images)
}
end = Sys.time()
print(end-start)Time difference of 23.48571 minsdat <- image_df %>%
reshape2::dcast(chemokine ~ patch_size, value.var = "significant_images", fill = 0)
rownames(dat) <- dat$chemokine
dat$chemokine <- NULL
m <- t(as.matrix(dat))
col_fun = viridis::inferno(100)
Heatmap(m,
cluster_rows = FALSE,
col = col_fun,
column_title = "Self-Interaction",
column_title_side = "bottom",
show_row_names = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.1f", m[i, j]), x, y, gp = gpar(fontsize = 15, col = "grey"))
},
heatmap_legend_param = list(
title = "% Significant\nImages", at = c(0, 10, 20, 30, 40, 50),
labels = c("0%", "10%", "20%", "30%","40%", "50%")),
row_title = "Motif Size",
row_names_side = "left",
width = unit(15, "cm"),
height = unit(8, "cm"))start = Sys.time()
cur_sce <- as.data.frame(colData(sce_rna))
image_df <- data.frame()
for(size in c(2,3,4,5,6,7,8,9,10)) {
images <- data.frame()
for(i in c("CXCL10")){
# add chemokine info to celltype
sce_info <- cur_sce[,c("cellID", i , "Description")]
# add celltype information
dat_cells_tmp <- left_join(as.data.frame(dat_cells), sce_info, by = "cellID")
#assign labels and groups
dat_cells_tmp$label <- dat_cells_tmp[,i]
dat_cells_tmp$group <- dat_cells_tmp$Description
dat_cells_tmp <- as.data.table(dat_cells_tmp)
# subset dat_relation and dat_cells
dat_cells_sub <- dat_cells_tmp[Description == "P3",]
dat_relation_sub <- dat_relation[dat_relation$`First Image Number` == unique(sce_rna[,sce_rna$Description == "P3"]$ImageNumber),]
# Prepare the data
d = neighbouRhood::prepare_tables(dat_cells_sub, dat_relation_sub)
# Calculate the baseline statistics
dat_baseline = neighbouRhood::apply_labels(d[[1]], d[[2]]) %>%
neighbouRhood::aggregate_classic_patch(., patch_size = size)
# Calculate the permutation statistics
# This will run the test using parallel computing. The name of the idcol does actually not matter.
set.seed(12312)
dat_perm = rbindlist(mclapply(1:n_perm, function(x){
dat_labels = neighbouRhood::shuffle_labels(d[[1]])
neighbouRhood::apply_labels(dat_labels, d[[2]]) %>%
neighbouRhood::aggregate_classic_patch(., patch_size = size)
},mc.cores = ncores
), idcol = 'run')
# calc p values
dat_p <- neighbouRhood::calc_p_vals(dat_baseline, dat_perm, n_perm = n_perm, p_tresh = 0.01)
# select interactions between chemokine+ cells
dat_p$interaction <- paste(dat_p$FirstLabel, dat_p$SecondLabel, sep = "_")
images <- rbind(images,dat_p)
}
# calculate percentage of images with significant patches
images$patch_size <- size
# add to data.frame
image_df <- rbind(image_df, images)
}
end = Sys.time()
print(end-start)Time difference of 1.236454 mins# Significant Self-Interatcions
image_df[image_df$interaction == "1_1",] group FirstLabel SecondLabel p_gt p_lt direction p sig
1: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
2: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
3: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
4: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
5: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
6: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
7: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
8: 58 1 1 0.00990099 1 TRUE 0.00990099 TRUE
9: 58 1 1 1.00000000 1 FALSE 1.00000000 FALSE
sigval interaction patch_size
1: 1 1_1 2
2: 1 1_1 3
3: 1 1_1 4
4: 1 1_1 5
5: 1 1_1 6
6: 1 1_1 7
7: 1 1_1 8
8: 1 1_1 9
9: 0 1_1 10example <- findPatch(sce_rna[,sce_rna$ImageNumber == 58], sce_rna[,sce_rna$CXCL10 == 1]$cellID,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
distance = 20,
min_clust_size = 10,
output_colname = "example_cluster")Time difference of 1.191686 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
'example_cluster',
distance = 25,
output_colname = "chemokine_community_i",
plot = TRUE)Time difference of 3.529992 secs
[1] "milieus successfully added to sce object"example <- findPatch(sce_rna[,sce_rna$ImageNumber == 58], sce_rna[,sce_rna$CXCL10 == 1]$cellID,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
distance = 20,
min_clust_size = 10,
output_colname = "example_cluster")Time difference of 0.8591537 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
'example_cluster',
distance = 25,
output_colname = "chemokine_community_i",
plot = TRUE,
xlim = c(725,850),
ylim = c(500,675),
point_size = 14)Warning: Removed 533 rows containing missing values (geom_point).Time difference of 3.343175 secs
[1] "milieus successfully added to sce object"# define fractions of chemokines present in community
cur_dt <- data.frame(colData(sce_rna))
plot_list <- list()
for(i in names(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))])) {
chemokine_name <- toupper(str_split(i, "_")[[1]][1])
# select all cells that are in a milieu
unique_comms <- unique(cur_dt[cur_dt[,i] > 0,i])
cur_dt_sub <- cur_dt[cur_dt[,i] %in% unique_comms,]
cur_dt_sub <- cbind(cur_dt_sub[,i],
cur_dt_sub[,grepl(glob2rx("C*L*"),names(cur_dt_sub))])
colnames(cur_dt_sub)[1] <- i
# add celltype and MM_location_simplified
cur_dt_sub$cellID <- rownames(cur_dt_sub)
cur_dt_sub <- left_join(cur_dt_sub, cur_dt[,c("cellID", "celltype", "MM_location_simplified")])
# melt the table
cur_dt_sub <- cur_dt_sub %>%
reshape2::melt(id.vars = c("cellID", "celltype", "MM_location_simplified", i), variable.name = "chemokine", value.name = "status")
# all cells that do not produce a chemokine
non_producer <- cur_dt_sub %>%
group_by(cellID) %>%
summarise(sum = sum(status)) %>%
filter(sum == 0) %>%
select(cellID)
# all cells that produce a chemokine - regardless of what chemokine
producer <- cur_dt_sub %>%
group_by(cellID) %>%
summarise(sum = sum(status)) %>%
filter(sum > 0) %>%
select(cellID)
# select non-producing cells and count
non_producer <- cur_dt_sub[cur_dt_sub$cellID %in% non_producer$cellID,] %>%
distinct(cellID, .keep_all = TRUE) %>%
group_by(celltype, MM_location_simplified, chemokine) %>%
summarise(n=n())
non_producer$chemokine <- "no chemokine"
# select producing cells and count chemokines
producer <- cur_dt_sub[cur_dt_sub$cellID %in% producer$cellID,] %>%
filter(status == 1) %>%
group_by(celltype, MM_location_simplified, chemokine) %>%
summarise(n=n())
summary <- rbind(producer, non_producer)
# celltypes numbers
summary_celltypes <- summary %>%
group_by(celltype) %>%
summarise(n=sum(n))
# chemokines per celltype numbers
summary_chemokines <- summary %>%
group_by(celltype, chemokine) %>%
summarise(n=sum(n))
# color_vector for cells and chemokines
col_vector_cells <- metadata(sce_rna)$colour_vector$celltype
col_vector_chemokines <- metadata(sce_rna)$colour_vectors$chemokine_single
col_vector <- c(col_vector_cells, col_vector_chemokines)
# add "no chemokine" to col_vector
col_vector <- c(col_vector, "white")
names(col_vector) <- c(names(col_vector[-length(col_vector)]), "no chemokine")
# create labels for middle of sunburst plot
# Number of detected Patches
numberOfPatches <- paste(length(unique_comms), ifelse(length(unique_comms)>1," Milieus", " Milieu"), sep = "")
# Median Number of Chemokine XY Producing Cells in a Patch
medianCells <- cur_dt[cur_dt[,i] > 0 & cur_dt[,chemokine_name] == 1,] %>%
group_by_at(i) %>%
summarise(n=n()) %>%
mutate(median = median(n))
medianCells <- paste(round(unique(medianCells$median)), " Cells", sep = "")
# Percentage of chemokines produced by milieu cells
percentageInPatches <- cur_dt[cur_dt[,chemokine_name] == 1,] %>%
mutate(in_patch = ifelse(.[,i] > 0, 1, 0)) %>%
group_by(in_patch) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) * 100) %>%
filter(in_patch == 1)
percentageInPatches <- paste(round(unique(percentageInPatches$percentage)), "%", sep = "")
label <- paste(paste(numberOfPatches, medianCells, sep = "\n"), percentageInPatches, sep = "\n")
# sunburst plot
plt <- ggplot() +
geom_text(aes(x=0,y=0, label = label, size=1)) +
geom_col(aes(x = 2, y = n, fill = celltype),
data = summary_celltypes,
color = "white",
lwd = 1) +
geom_col(aes(x = 3, y = n, group = celltype, fill = chemokine),
data = summary_chemokines) +
xlim(0, 3.5) + labs(x = NULL, y = NULL) +
scale_fill_manual(values = unname(col_vector),
breaks = names(col_vector),
labels = names(col_vector)) +
ggtitle(chemokine_name) +
theme_void() +
theme(axis.ticks=element_blank(),
plot.margin = unit(c(0,0,0,0), "cm"),
axis.text=element_blank(),
axis.title=element_blank(),
legend.position = "none",
text = element_text(size = 18),
plot.title = element_text(hjust = 0.5)) +
coord_polar(theta = "y")
# add to list
plot_list[[i]] <- plot_grid(plt)
}
# plot sunburst plots (without CCL4, CCL22, CCL8 - low abundance communities)
plot_grid(plot_list$cxcl8_pure, plot_list$ccl2_pure,
plot_list$cxcl10_pure, plot_list$cxcl9_pure,
plot_list$ccl18_pure, plot_list$ccl19_pure,
plot_list$cxcl12_pure, plot_list$cxcl13_pure,
plot_list$ccl4_pure, plot_list$ccl22_pure,
plot_list$ccl8_pure,
ncol = 3, aligh = "hv")Warning in as_grob.default(plot): Cannot convert object of class character into
a grob.# create legend for chemokines
lgd1 = Legend(labels = names(col_vector_chemokines), title = "Outer Circle\nChemokine", legend_gp = gpar(fill = unname(col_vector_chemokines)))
# create legend for celltypes
lgd2 = Legend(labels = names(col_vector_cells), title = "Inner Circle\nCell Type ", legend_gp = gpar(fill = unname(col_vector_cells)))
draw(packLegend(lgd2, lgd1, direction = "horizontal"))milieus <- data.frame(colData(sce_rna)) %>%
filter(celltype == "CD8+ T cell") %>%
select(cellID, contains("pure")) %>%
mutate_if(is.numeric, ~1 * (. > 0))
milieus$number_of_milieus <- rowSums(milieus[,-1])
# keep CD8+ T cells that are part of at least one milieu
milieus <- milieus %>%
filter(number_of_milieus > 0) %>%
select(-number_of_milieus) %>%
reshape2::melt(id.vars = "cellID", variable.name = "milieu", value.name = "is_part") %>%
filter(is_part > 0) %>%
select(cellID, milieu)marker_rna <- c("Lag3", "T8_CXCL13", "T5_CCL4")
# rna data
dat_rna <- data.frame(t(assay(sce_rna[marker_rna, sce_rna$celltype == "CD8+ T cell"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(milieus, dat_rna)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("cellID", "milieu"), variable.name = "channel", value.name = "asinh")
# remove CCL4/CCL8/CXCL8 milieus due to too few data points
dat_rna <- dat_rna %>%
filter(!(milieu %in% c("ccl4_pure", "ccl8_pure", "cxcl8_pure")))
# rename milieus
dat_rna <- dat_rna %>%
mutate(milieu_short = toupper(str_split(milieu, "_", n = 2, simplify = TRUE)[,1]))
col_vector_chemokines <- metadata(sce_rna)$colour_vectors$chemokine_single
# add channel medium
dat_rna <- dat_rna %>%
group_by(channel) %>%
mutate(channel_median = median(asinh))
# one-sample t test
stat.test <- data.frame()
# loop through all channels (each has a different µ)
for(j in unique(dat_rna$channel)){
cur.mu <- unique(dat_rna[dat_rna$channel == j, ]$channel_median)
# calculate p-value for different milieus in one channel and adjust pvalue
cur.test <- dat_rna[dat_rna$channel == j, ] %>%
group_by(channel) %>%
wilcox_test(asinh ~ milieu_short, ref.group = ".all.") %>%
adjust_pvalue(method = "BH") %>%
add_x_position(x="milieu_short")
stat.test <- rbind(stat.test, cur.test)
}
# adjust again for testing across different channels
stat.test <- stat.test %>%
group_by(channel) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1))
# plot
plot_list <- list()
ylim_list <- list("Lag3" = c(0,0.9), "T8_CXCL13" = c(0,2.3), "T5_CCL4" = c(0,1))
for(i in unique(dat_rna$channel)){
cur.stat.test <- stat.test[stat.test$channel == i, ]
plot_list[[i]] <- ggplot(dat_rna[dat_rna$channel == i,], aes(x=milieu_short, y=asinh)) +
geom_boxplot(alpha=1, lwd=0.5, outlier.shape = NA, position = position_dodge(1.1), aes(fill=milieu_short)) +
theme_bw() +
theme(text = element_text(size=18),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
guides(fill=guide_legend("Milieu", override.aes = list(alpha=1)), col="none") +
stat_pvalue_manual(
cur.stat.test, x = "xmin", y.position = ylim_list[[i]][2]-0.05,
label = "p.adj.signif",
position = position_dodge(0.8),
size=4) +
ylab("Mean Expression (asinh)") +
xlab("") +
geom_hline(aes(yintercept = channel_median, group = channel), colour = 'black', linetype = 2, size=1) +
scale_fill_manual(values = unname(col_vector_chemokines),
breaks = names(col_vector_chemokines),
labels = names(col_vector_chemokines)) +
coord_cartesian(ylim=ylim_list[[i]]) +
facet_wrap(~channel)
}
leg_c <- cowplot::get_legend(plot_list[[1]])
grid.arrange(plot_list[[1]] + theme(legend.position = "none"),
plot_list[[2]] + theme(legend.position = "none") + ylab(""),
plot_list[[3]] + theme(legend.position = "none") + ylab(""),
ncol=2)grid.arrange(leg_c)
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] ggpubr_0.4.0 viridis_0.5.1
[7] viridisLite_0.3.0 cowplot_1.1.1
[9] neighbouRhood_0.4 magrittr_2.0.1
[11] dtplyr_1.0.1 ggpmisc_0.3.7
[13] gridExtra_2.3 dittoSeq_1.0.2
[15] scater_1.16.2 destiny_3.2.0
[17] ggbeeswarm_0.6.0 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 fpc_2.2-9
[25] data.table_1.13.6 umap_0.2.7.0
[27] forcats_0.5.0 stringr_1.4.0
[29] dplyr_1.0.2 purrr_0.3.4
[31] readr_1.4.0 tidyr_1.1.2
[33] tibble_3.0.4 ggplot2_3.3.3
[35] tidyverse_1.3.0 reshape2_1.4.4
[37] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[39] Biobase_2.50.0 GenomicRanges_1.42.0
[41] GenomeInfoDb_1.26.2 IRanges_2.24.1
[43] S4Vectors_0.28.1 BiocGenerics_0.36.0
[45] MatrixGenerics_1.2.0 matrixStats_0.57.0
[47] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] reticulate_1.18 tidyselect_1.1.0
[3] ranger_0.12.1 BiocParallel_1.22.0
[5] munsell_0.5.0 units_0.6-7
[7] codetools_0.2-18 withr_2.3.0
[9] colorspace_2.0-0 knitr_1.30
[11] rstudioapi_0.13 robustbase_0.93-7
[13] ggsignif_0.6.0 vcd_1.4-8
[15] VIM_6.0.0 TTR_0.24.2
[17] labeling_0.4.2 git2r_0.28.0
[19] GenomeInfoDbData_1.2.4 farver_2.0.3
[21] pheatmap_1.0.12 rprojroot_2.0.2
[23] vctrs_0.3.6 generics_0.1.0
[25] xfun_0.20 ggthemes_4.2.0
[27] diptest_0.75-7 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] flexmix_2.3-17 bitops_1.0-6
[35] DelayedArray_0.16.0 assertthat_0.2.1
[37] promises_1.1.1 scales_1.1.1
[39] nnet_7.3-14 beeswarm_0.2.3
[41] gtable_0.3.0 rlang_0.4.10
[43] scatterplot3d_0.3-41 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 ggridges_0.5.3
[55] Rcpp_1.0.5 plyr_1.8.6
[57] zlibbioc_1.36.0 classInt_0.4-3
[59] RCurl_1.98-1.2 openssl_1.4.3
[61] GetoptLong_1.0.5 zoo_1.8-8
[63] haven_2.3.1 ggrepel_0.9.0
[65] cluster_2.1.0 fs_1.5.0
[67] RSpectra_0.16-0 openxlsx_4.2.3
[69] lmtest_0.9-38 reprex_0.3.0
[71] pcaMethods_1.80.0 whisker_0.4
[73] hms_0.5.3 evaluate_0.14
[75] smoother_1.1 rio_0.5.16
[77] mclust_5.4.7 readxl_1.3.1
[79] shape_1.4.5 compiler_4.0.3
[81] V8_3.4.0 KernSmooth_2.23-18
[83] crayon_1.3.4 htmltools_0.5.0
[85] later_1.1.0.1 lubridate_1.7.9.2
[87] DBI_1.1.0 dbplyr_2.0.0
[89] MASS_7.3-53 boot_1.3-25
[91] Matrix_1.3-2 car_3.0-10
[93] cli_2.2.0 pkgconfig_2.0.3
[95] foreign_0.8-81 laeken_0.5.1
[97] sp_1.4-5 xml2_1.3.2
[99] vipor_0.4.5 XVector_0.30.0
[101] rvest_0.3.6 digest_0.6.27
[103] rmarkdown_2.6 cellranger_1.1.0
[105] edgeR_3.30.3 DelayedMatrixStats_1.10.1
[107] curl_4.3 kernlab_0.9-29
[109] modeltools_0.2-23 ggplot.multistats_1.0.0
[111] rjson_0.2.20 lifecycle_0.2.0
[113] jsonlite_1.7.2 carData_3.0-4
[115] BiocNeighbors_1.6.0 askpass_1.1
[117] limma_3.44.3 fansi_0.4.1
[119] pillar_1.4.7 lattice_0.20-41
[121] httr_1.4.2 DEoptimR_1.0-8
[123] glue_1.4.2 xts_0.12.1
[125] zip_2.1.1 png_0.1-7
[127] prabclus_2.3-2 class_7.3-17
[129] stringi_1.5.3 RcppHNSW_0.3.0
[131] BiocSingular_1.4.0 irlba_2.3.3
[133] e1071_1.7-4