Last updated: 2021-02-18
Checks: 6 1
Knit directory: melanoma_publication_old_data/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
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 ee1595d. 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: ._.DS_Store
Ignored: analysis/._clinical metadata preparation.Rmd
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/
Ignored: output/.DS_Store
Ignored: output/._.DS_Store
Ignored: output/._protein_neutrophil.png
Ignored: output/._rna_neutrophil.png
Ignored: output/PSOCKclusterOut/
Ignored: output/bcell_grouping.png
Ignored: output/dysfunction_correlation.pdf
Unstaged changes:
Modified: .gitignore
Modified: analysis/04_1_Protein_celltype_classification.rmd
Modified: analysis/04_1_RNA_celltype_classification.rmd
Modified: analysis/04_2_RNA_classification_subclustering.rmd
Modified: analysis/04_2_protein_classification_subclustering.rmd
Modified: analysis/07_TCF7_PD1_gating.rmd
Modified: analysis/08_color_vectors.rmd
Modified: analysis/09_Tcell_Score.Rmd
Modified: analysis/10_Dysfunction_Score.rmd
Modified: analysis/11_Bcell_Score.Rmd
Modified: analysis/Figure_1.rmd
Modified: analysis/Figure_2.rmd
Modified: analysis/Figure_3.rmd
Modified: analysis/Figure_4.rmd
Modified: analysis/Figure_5.rmd
Modified: analysis/Supp-Figure_1.rmd
Modified: analysis/Supp-Figure_2.rmd
Modified: analysis/Supp-Figure_3.rmd
Modified: analysis/Supp-Figure_4.rmd
Modified: analysis/Supp-Figure_5.rmd
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/Figure_3.rmd) and HTML (docs/Figure_3.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 | 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
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
value ?
visible 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(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 22.66207 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 1.084213 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 1.181726 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 3.4429 secs
[1] "milieus successfully added to sce object"example <- findPatch(sce_rna[,sce_rna$ImageNumber == 58], sce_rna[,sce_rna$CXCL10 == 1]$cellID,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
distance = 20,
min_clust_size = 10,
output_colname = "example_cluster")Time difference of 0.8379529 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
'example_cluster',
distance = 25,
output_colname = "chemokine_community_i",
plot = TRUE,
xlim = c(725,850),
ylim = c(500,675),
point_size = 14)Warning: Removed 533 rows containing missing values (geom_point).Time difference of 3.235821 secs
[1] "milieus successfully added to sce object"# define fractions of chemokines present in community
cur_dt <- data.frame(colData(sce_rna))
plot_list <- list()
for(i in names(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))])) {
chemokine_name <- toupper(str_split(i, "_")[[1]][1])
# select all cells that are in a milieu
unique_comms <- unique(cur_dt[cur_dt[,i] > 0,i])
cur_dt_sub <- cur_dt[cur_dt[,i] %in% unique_comms,]
cur_dt_sub <- cbind(cur_dt_sub[,i],
cur_dt_sub[,grepl(glob2rx("C*L*"),names(cur_dt_sub))])
colnames(cur_dt_sub)[1] <- i
# add celltype and MM_location_simplified
cur_dt_sub$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