Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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This script generates plots for Supplementary Figure 11.
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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code/helper_functions/detect_mRNA_expression.R
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code/helper_functions/DistanceToClusterCenter.R
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code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
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code/helper_functions/getInfoFromString.R
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code/helper_functions/getSpotnumber.R
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code/helper_functions/plotCellCounts.R
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code/helper_functions/plotCellFractions.R
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code/helper_functions/plotDist.R code/helper_functions/read_Data.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(data.table)
library(ggplot2)
library(ComplexHeatmap)
library(rms)
library(ggrepel)
library(ggbeeswarm)
library(circlize)
library(ggpubr)
library(ggridges)
library(gridExtra)
library(rstatix)sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]
# dysfunction stain
sce_dysfunction <- readRDS(file = "data/data_for_analysis/sce_dysfunction.rds")tumor_marker_protein <- c("bCatenin", "Sox9", "pERK", "p75", "Ki67", "SOX10", "PARP", "S100", "MiTF")
tumor_marker_rna <- c("Mart1", "pRB")
# rna data
dat_rna <- data.frame(t(assay(sce_rna[tumor_marker_rna, sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "Tcell_density_score_image", "Description", "MM_location", "Location")])Joining, by = "cellID"# filter
dat_rna <- dat_rna %>%
filter(Location != "CTRL")
# mean per image
dat_rna <- dat_rna %>%
dplyr::select(-cellID) %>%
group_by(Description, Tcell_density_score_image) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("Description", "Tcell_density_score_image"), variable.name = "channel", value.name = "asinh")
# protein data
dat_prot <- data.frame(t(assay(sce_prot[tumor_marker_protein,, sce_prot$celltype == "Tumor"], "asinh")))
dat_prot$cellID <- rownames(dat_prot)
dat_prot <- left_join(dat_prot, data.frame(colData(sce_prot))[,c("cellID", "Tcell_density_score_image", "Description", "MM_location", "Location")])Joining, by = "cellID"# filter
dat_prot <- dat_prot %>%
filter(Location != "CTRL")
# mean per image
dat_prot <- dat_prot %>%
dplyr::select(-cellID) %>%
group_by(Description, Tcell_density_score_image) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_prot <- dat_prot %>%
reshape2::melt(id.vars = c("Description", "Tcell_density_score_image"), variable.name = "channel", value.name = "asinh")
# join both data sets
comb <- rbind(dat_prot, dat_rna)
# adjusted wilcox.test for groups
group_comparison <- list(c("absent", "high"), c("med", "high"))
stat.test <- comb %>%
group_by(channel) %>%
wilcox_test(data = ., asinh ~ Tcell_density_score_image) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(x = "Tcell_density_score_image", dodge = 0.8, comparisons = group_comparison) %>%
filter(is.na(y.position) == FALSE)
# plot
p <- ggplot(comb, aes(x=Tcell_density_score_image, y=asinh)) +
geom_boxplot(alpha=0.2, lwd=1, aes(fill=Tcell_density_score_image)) +
geom_quasirandom(alpha=0.6, size=2, aes(col=Tcell_density_score_image)) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size=18),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank()) +
facet_wrap(~channel, scales = "free") +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
ylab("Mean Count per Image (asinh)") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) +
guides(fill=guide_legend(title="T cell Score", override.aes = c(lwd=0.5, alpha=1)))
leg <- get_legend(p)Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
use `guide = "none"` instead.grid.arrange(p + theme(legend.position = "none"))
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
grid.arrange(leg)
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
# only CD8+ T cells
CD8_sce <- sce_dysfunction[,sce_dysfunction$celltype %in% c("CD8_Tcell","CD8_CXCL13+_Tcell")]
pat_dat <- as_tibble(colData(sce_rna)) %>%
filter(is.na(dysfunction_score) == FALSE) %>%
dplyr::select(Description,PatientID) %>%
distinct()
cur_dat <- as_tibble(colData(CD8_sce))
cur_dat <- left_join(cur_dat,pat_dat,"Description")
RNA_dat <- as_tibble(colData(sce_rna))
prot_dat_CXCL13 <- cur_dat %>%
filter(is.na(dysfunction_score) == FALSE) %>%
dplyr::select(PatientID,celltype) %>%
group_by(PatientID,celltype) %>%
dplyr::count(celltype) %>%
ungroup() %>%
group_by(PatientID) %>%
mutate(total_cellcount = sum(n)) %>%
mutate(frac = n/total_cellcount) %>%
filter(celltype == "CD8_CXCL13+_Tcell") %>%
dplyr::select(PatientID,frac) %>%
distinct() %>%
pull(frac,PatientID)
rna_dat <- RNA_dat %>%
filter(is.na(dysfunction_score) == FALSE) %>%
dplyr::select(celltype,PatientID,dysfunction_score,CXCL13) %>%
mutate(celltype2 = paste0(celltype,"_",CXCL13)) %>%
filter(celltype2 %in% c("CD8+ T cell_0","CD8+ T cell_1")) %>%
group_by(PatientID,celltype, .drop = FALSE) %>%
dplyr::count(celltype2) %>%
ungroup() %>%
group_by(PatientID, .drop = FALSE) %>%
mutate(total_cellcount = sum(n),frac=n/total_cellcount) %>%
filter(celltype2 == "CD8+ T cell_0") %>%
mutate(frac_CD8_CXCL13 = 1-frac) %>%
pull(frac_CD8_CXCL13,PatientID)
# check names and only select those names in RNA data that we have in protein data
identical(names(rna_dat),names(prot_dat_CXCL13))[1] FALSErna_dat <- rna_dat[names(prot_dat_CXCL13)]
# merge all data
all_dat <- data.frame(PatientID = names(prot_dat_CXCL13),
prot_CXCL13 = as.vector(prot_dat_CXCL13),
rna_frac = as.vector(rna_dat))
ggplot(all_dat, aes(x=prot_CXCL13, y=rna_frac)) +
geom_point(size=3) +
geom_smooth(method="lm") +
stat_cor(method = "spearman") +
theme_bw() +
theme(text = element_text(size=15)) +
xlab("Fraction CD8+ CXCL13+ T cells (Protein data)") +
ylab("Fraction CD8+ CXCL13+ T cells (RNA data)")`geom_smooth()` using formula 'y ~ x'
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
cor.test(all_dat$prot_CXCL13, all_dat$rna_frac, method = "spearman")
Spearman's rank correlation rho
data: all_dat$prot_CXCL13 and all_dat$rna_frac
S = 214, p-value = 0.004431
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.6852941 cur_dt <- data.frame(colData(sce_rna))
clust <- data.frame()
## wide table for communities
for(i in names(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))])) {
cur_dt_sub <- cur_dt[cur_dt[,i] > 0,]
cur_dt_sub <- cbind(cur_dt_sub[,c(i, "Description")],
cur_dt_sub[,grepl(glob2rx("C*L*"),names(cur_dt_sub))])
# count numbers of chemokine-expressing cells per patch
cur_dt_sub <- cur_dt_sub %>%
group_by(Description) %>%
group_by_at(i, .add = TRUE) %>%
summarise_each(funs(sum))
cur_dt_sub$cluster_type <- i
cur_dt_sub <- cur_dt_sub[,-2]
clust <- rbind(clust, cur_dt_sub)
}Warning: `summarise_each_()` was deprecated in dplyr 0.7.0.
Please use `across()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.# remove pure clusters with low abundance (CCL22, CCL4, CCL8)
clust <- clust[!(clust$cluster_type %in% c("ccl22_pure", "ccl4_pure", "ccl8_pure")),]
# number of patches by image
clust <- clust %>%
group_by(Description, cluster_type) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ cluster_type, value.var = "n", fill = 0)`summarise()` has grouped output by 'Description'. You can override using the
`.groups` argument.# add images with 0 clusters and add more information
to_add <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = TRUE)
clust <- left_join(to_add[,c("Description", "dysfunction_score", "Tcell_density_score_image")], clust)Joining, by = "Description"# repalce NA with 0
cur_dt_wide <- clust %>%
mutate_if(is.numeric,coalesce,0)
# order according to image infiltration score
cur_dt_wide <- cur_dt_wide[order(cur_dt_wide$dysfunction_score),]
# chemokines per image
total_chemokines <- cur_dt %>%
group_by(Description, chemokine) %>%
summarise(n=n()) %>%
group_by(Description) %>%
mutate(fraction = n/sum(n)) %>%
filter(chemokine == TRUE)`summarise()` has grouped output by 'Description'. You can override using the
`.groups` argument.# is a expressing cell part of a milieu?
cur_dt$in_community <- ifelse(rowSums(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))]) > 0 & cur_dt$chemokine == TRUE, TRUE, FALSE)
# fraction in_community 1vs.0 per image
fractions <- cur_dt %>%
filter(chemokine == TRUE) %>%
group_by(Description, in_community) %>%
summarise(n=n()) %>%
group_by(Description) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(Description ~ in_community, value.var = "fraction", fill = 0)`summarise()` has grouped output by 'Description'. You can override using the
`.groups` argument.names(fractions)[2:3] <- c("single", "community")
fractions[, 2:3][is.na(fractions[, 2:3])] <- 0
# chemokines per image (regardless of combination, multi-producing cells count more than once)
chemokines <- cbind(cur_dt[,c("Description", "in_community")], cur_dt[,grepl(glob2rx("C*L*"),names(cur_dt))])
# long table - chemokine / in_community info and count (n) per image
chemokines <- reshape2::melt(chemokines, id.vars = c("Description", "in_community"), variable.name = "chemokine", value.name = "n") %>%
group_by(Description, in_community, chemokine) %>%
summarise(total = sum(n)) %>%
reshape2::dcast(Description + chemokine ~ in_community, value.var = "total") %>%
replace(is.na(.), 0) %>%
reshape2::melt(id.vars = c("Description", "chemokine"), variable.name = "in_community", value.name = "n")`summarise()` has grouped output by 'Description', 'in_community'. You can
override using the `.groups` argument.# combine all information
cur_dt_wide <- left_join(cur_dt_wide, fractions)Joining, by = "Description"cur_dt_wide <- left_join(cur_dt_wide, total_chemokines)Joining, by = "Description"# remove controls and only keep images with dysfunction score
cur_dt_wide_sub <- cur_dt_wide[cur_dt_wide$Description %in% unique(sce_rna[,sce_rna$Location != "CTRL"]$Description),]
cur_dt_wide_sub <- cur_dt_wide[cur_dt_wide$dysfunction_score %in% c("High Dysfunction", "Low Dysfunction"),]
# define subgroups to split heatmap
subgroup = cur_dt_wide_sub[,"dysfunction_score"]
# heatmap annotation
row_ha2 = rowAnnotation("Production Mode of\nChemokine-Expressing Cells" =
anno_barplot(cur_dt_wide_sub[,c("single", "community")],
gp = gpar(fill = c("#F8766D", "#00BFC4")), width = unit(1.5, "cm")),
"Fraction of Chemokine- \n Expressing Cells" =
anno_barplot(cur_dt_wide_sub[,"fraction"],
width = unit(1.5, "cm")),
annotation_name_rot = 90, gap = unit(3, "mm"),
col = list(Relapse = c("no relapse" = "orange", "relapse" = "black", "untreated/lost" = "grey")))
# function for the zoom-in plot
panel_fun_chemokines = function(index, nm) {
image_number = cur_dt_wide_sub[index,"Description"]
if(length(unique(image_number)) > 9){
df = chemokines[chemokines$Description %in% image_number, ]
g = ggplot(df, aes(x = factor(chemokine), y = log10(n+1), fill=in_community)) +
geom_boxplot() +
xlab("Chemokine") +
ylab("# Cells [log10(n+1)]") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position = "none") +
ylim(0,3)
g = grid.grabExpr(print(g))
pushViewport(viewport())
grid.rect()
grid.draw(g)
popViewport()
}
}
# create zoom-in
zoom = anno_zoom(align_to = subgroup,
which = "row", panel_fun = panel_fun_chemokines,
size = unit(6, "cm"),
gap = unit(1, "cm"),
width = unit(10, "cm"))
# heatmap
m <- as.matrix(cur_dt_wide_sub[,grepl(glob2rx("*pure"),names(cur_dt_wide_sub))])
col_names <- c()
for(i in (1:length(colnames(m)))){col_names <- c(col_names,(toupper(str_split(colnames(m), "_")[[i]][1])))}
colnames(m) <- col_names
col_fun = viridis::inferno(max(m)+1)
ht1 = Heatmap(m,
col = col_fun,
left_annotation = row_ha2,
right_annotation = rowAnnotation(foo = zoom, gap = unit(3,"cm")),
row_split = subgroup,
row_title_side = "left",
border = T,
row_gap = unit(3, "mm"),
cluster_rows = T,
cluster_columns = F,
cluster_row_slices = F,
show_heatmap_legend = F,
show_row_dend = F,
name = "Detected Patches",
column_title = "Chemokine Milieu",
column_title_side = "bottom",
column_title_gp = gpar(fontsize=20),
show_row_names = F,
width = unit(15,"cm"))
draw(ht1)Warning: Removed 1 rows containing non-finite values (stat_boxplot).
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
# manual legends
lgd1 = Legend(labels = c("Stand-alone", "Milieu"), title = "Production Mode", legend_gp = gpar(fill = c("#F8766D", "#00BFC4")))
lgd2 = Legend(col_fun = colorRamp2(c(0:max(m)), colors = col_fun),
at = seq(0, max(m)+2, by=5), title = "Detected Milieus", direction = "horizontal", grid_width = unit(2, "cm"))
# Draw Legend
draw(packLegend(lgd2, lgd1, direction = "horizontal"))
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
tumor_marker_protein <- c("pS6", "bCatenin", "H3K27me3", "HLADR", "Sox9", "pERK", "p75", "PDL1", "Ki67", "SOX10", "PARP")
tumor_marker_rna <- c("B2M")
# rna data
dat_rna <- data.frame(t(assay(sce_rna[tumor_marker_rna, sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "dysfunction_score", "Description")])Joining, by = "cellID"# filter
dat_rna <- dat_rna %>%
filter(dysfunction_score %in% c("High Dysfunction", "Low Dysfunction"))
# mean per image
dat_rna <- as_tibble(dat_rna) %>%
dplyr::select(-cellID) %>%
group_by(Description, dysfunction_score) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("Description", "dysfunction_score"), variable.name = "channel", value.name = "asinh")
# protein data
dat_prot <- data.frame(t(assay(sce_prot[tumor_marker_protein,, sce_prot$celltype == "Tumor"], "asinh")))
dat_prot$cellID <- rownames(dat_prot)
dat_prot <- left_join(dat_prot, data.frame(colData(sce_prot))[,c("cellID", "dysfunction_score", "Description")])Joining, by = "cellID"# filter
dat_prot <- dat_prot %>%
filter(dysfunction_score %in% c("High Dysfunction", "Low Dysfunction"))
# mean per image
dat_prot <- dat_prot %>%
dplyr::select(-cellID) %>%
group_by(Description, dysfunction_score) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_prot <- dat_prot %>%
reshape2::melt(id.vars = c("Description", "dysfunction_score"), variable.name = "channel", value.name = "asinh")
# join both data sets
comb <- rbind(dat_prot, dat_rna)
stat.test <- comb %>%
group_by(channel) %>%
wilcox_test(data = ., asinh ~ dysfunction_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(dodge = 0.8)
# plot
p <- ggplot(comb, aes(x=dysfunction_score, y=asinh)) +
geom_boxplot(alpha=0.2, lwd=1, aes(fill=dysfunction_score)) +
geom_quasirandom(alpha=0.6, size=2, aes(col=dysfunction_score)) +
scale_color_discrete(guide = "none") +
theme_bw() +
theme(text = element_text(size=18),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank()) +
facet_wrap(~channel, scales = "free", ncol = 4) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
ylab("Mean Count per Image (asinh)") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.2))) +
guides(fill=guide_legend(title="Dysfunction Score", override.aes = c(lwd=0.5, alpha=1)))
leg <- get_legend(p)
grid.arrange(p + theme(legend.position = "none"))
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
grid.arrange(leg)
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
y <- c(rep(1:10,16),rep(11,7))
# add the group information to the sce object
sce_rna$groups <- y[colData(sce_rna)$ImageNumber]
# now we use the function written by Nils
plotDist(sce_rna["S100", sce_rna$celltype == "Tumor"], plot_type = "ridges",
colour_by = "groups", split_by = "rows",
exprs_values = "asinh") +
geom_vline(xintercept = 3)# manual gating
sce_rna$S100 <- ifelse(assay(sce_rna["S100",], "asinh") > 3, "positive", "negative")
# fraction of S100 tumor cells per image
s100 <- data.frame(colData(sce_rna)) %>%
filter(celltype == "Tumor") %>%
group_by(ImageNumber, dysfunction_score, S100) %>%
summarise(n=n()) %>%
mutate(fraction = n/sum(n)) %>%
reshape2::dcast(ImageNumber + dysfunction_score ~ S100, fill = 0, value.var = "fraction") %>%
reshape2::melt(id.vars = c("ImageNumber", "dysfunction_score"), variable.name = "S100", value.name = "fraction") %>%
filter(is.na(dysfunction_score) == F & S100 == "positive")`summarise()` has grouped output by 'ImageNumber', 'dysfunction_score'. You can
override using the `.groups` argument.s100$dysfunction_score <- factor(s100$dysfunction_score)
stat.test <- s100 %>%
group_by(S100) %>%
wilcox_test(data = ., fraction ~ dysfunction_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_x_position(dodge = 0.8)
ggplot(s100, aes(x=dysfunction_score, y=fraction)) +
geom_boxplot(alpha=0.2, lwd=1.5, aes(fill = dysfunction_score)) +
geom_quasirandom(aes(col=dysfunction_score), size=3) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7, y.position = 1) +
xlab("") +
ylab("Fraction of S100+ Tumor Cells") +
theme_bw() +
theme(text = element_text(size=16),
legend.position = "none") +
ylim(0,1.05)
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 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=en_US.UTF-8
[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 stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rstatix_0.7.0 gridExtra_2.3
[3] ggridges_0.5.3 ggpubr_0.4.0
[5] circlize_0.4.13 ggbeeswarm_0.6.0
[7] ggrepel_0.9.1 rms_6.2-0
[9] SparseM_1.81 Hmisc_4.6-0
[11] Formula_1.2-4 survival_3.2-13
[13] lattice_0.20-45 ComplexHeatmap_2.10.0
[15] data.table_1.14.2 forcats_0.5.1
[17] stringr_1.4.0 purrr_0.3.4
[19] readr_2.1.2 tidyr_1.2.0
[21] tibble_3.1.6 ggplot2_3.3.5
[23] tidyverse_1.3.1 reshape2_1.4.4
[25] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[27] Biobase_2.54.0 GenomicRanges_1.46.1
[29] GenomeInfoDb_1.30.1 IRanges_2.28.0
[31] S4Vectors_0.32.3 BiocGenerics_0.40.0
[33] MatrixGenerics_1.6.0 matrixStats_0.61.0
[35] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.4.1 plyr_1.8.6
[4] splines_4.1.2 TH.data_1.1-0 digest_0.6.29
[7] foreach_1.5.2 htmltools_0.5.2 magick_2.7.3
[10] viridis_0.6.2 fansi_1.0.2 magrittr_2.0.2
[13] checkmate_2.0.0 cluster_2.1.2 doParallel_1.0.16
[16] tzdb_0.2.0 modelr_0.1.8 sandwich_3.0-1
[19] jpeg_0.1-9 colorspace_2.0-2 rvest_1.0.2
[22] haven_2.4.3 xfun_0.29 callr_3.7.0
[25] crayon_1.4.2 RCurl_1.98-1.5 jsonlite_1.7.3
[28] zoo_1.8-9 iterators_1.0.13 glue_1.6.1
[31] gtable_0.3.0 zlibbioc_1.40.0 XVector_0.34.0
[34] MatrixModels_0.5-0 GetoptLong_1.0.5 DelayedArray_0.20.0
[37] car_3.0-12 shape_1.4.6 abind_1.4-5
[40] scales_1.1.1 mvtnorm_1.1-3 DBI_1.1.2
[43] Rcpp_1.0.8 viridisLite_0.4.0 htmlTable_2.4.0
[46] clue_0.3-60 foreign_0.8-82 htmlwidgets_1.5.4
[49] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
[52] farver_2.1.0 pkgconfig_2.0.3 nnet_7.3-17
[55] sass_0.4.0 dbplyr_2.1.1 utf8_1.2.2
[58] labeling_0.4.2 tidyselect_1.1.1 rlang_1.0.0
[61] later_1.3.0 munsell_0.5.0 cellranger_1.1.0
[64] tools_4.1.2 cli_3.1.1 generics_0.1.2
[67] broom_0.7.12 evaluate_0.14 fastmap_1.1.0
[70] yaml_2.2.2 processx_3.5.2 knitr_1.37
[73] fs_1.5.2 nlme_3.1-155 whisker_0.4
[76] quantreg_5.87 xml2_1.3.3 compiler_4.1.2
[79] rstudioapi_0.13 beeswarm_0.4.0 png_0.1-7
[82] ggsignif_0.6.3 reprex_2.0.1 bslib_0.3.1
[85] stringi_1.7.6 highr_0.9 ps_1.6.0
[88] Matrix_1.4-0 vctrs_0.3.8 pillar_1.7.0
[91] lifecycle_1.0.1 jquerylib_0.1.4 GlobalOptions_0.1.2
[94] bitops_1.0-7 httpuv_1.6.5 R6_2.5.1
[97] latticeExtra_0.6-29 promises_1.2.0.1 vipor_0.4.5
[100] codetools_0.2-18 polspline_1.1.19 MASS_7.3-55
[103] assertthat_0.2.1 rprojroot_2.0.2 rjson_0.2.21
[106] withr_2.4.3 multcomp_1.4-18 GenomeInfoDbData_1.2.7
[109] mgcv_1.8-38 parallel_4.1.2 hms_1.1.1
[112] rpart_4.1.16 rmarkdown_2.11 carData_3.0-5
[115] git2r_0.29.0 getPass_0.2-2 lubridate_1.8.0
[118] base64enc_0.1-3