Last updated: 2021-02-10
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Knit directory: melanoma_publication_old_data/
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|---|---|---|---|---|
| Rmd | 2e443a5 | toobiwankenobi | 2021-02-09 | remove files that are not needed |
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
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| Rmd | 545c207 | toobiwankenobi | 2020-12-22 | clean up branch |
| Rmd | 58c40e5 | toobiwankenobi | 2020-10-19 | correct files that don’t work |
| 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 4.
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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code/helper_functions/sceChecks.R
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code/helper_functions/validityChecks.R
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visible FALSE library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
library(ggplot2)
library(cba)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggpubr)
library(ggbeeswarm)
library(gridExtra)
library(tidyr)
library(ggpmisc)
library(circlize)
library(coxme)
library(dittoSeq)
library(scater)
library(cowplot)
library(survminer)
library(ggalluvial)
library(cytomapper)
library(ggrepel)
library(rstatix)sce_rna = readRDS(file = "data/sce_RNA.rds")
sce_prot = readRDS(file = "data/sce_protein.rds")
# meta data
dat_relation = fread(file = "data/protein/Object relationships.csv",stringsAsFactors = FALSE)
dat_relation_rna = fread(file = "data/RNA/Object relationships.csv",stringsAsFactors = FALSE)
# image
image_mat_prot <- read.csv("data/protein/Image.csv")
# surv_dat
dat_survival_prot <- fread(file = "data/protein/clinical_data_protein.csv")# prepare data and add cellID
dat_relation$cellID_first <- paste("protein", paste(dat_relation$`First Image Number`, dat_relation$`First Object Number`, sep = "_"), sep = "_")
dat_relation$cellID_second <- paste("protein", paste(dat_relation$`Second Image Number`, dat_relation$`Second Object Number`, sep = "_"), sep = "_")
# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_prot))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_prot))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")
dat_relation <- left_join(dat_relation, celltype_first, by = "cellID_first")
dat_relation <- left_join(dat_relation, celltype_second, by = "cellID_second")
colnames(dat_relation)[5] <- "FirstImageNumber"# prepare data and add cellID
dat_relation_rna$cellID_first <- paste("RNA", paste(dat_relation_rna$`First Image Number`, dat_relation_rna$`First Object Number`, sep = "_"), sep = "_")
dat_relation_rna$cellID_second <- paste("RNA", paste(dat_relation_rna$`Second Image Number`, dat_relation_rna$`Second Object Number`, sep = "_"), sep = "_")
# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_rna))[,c("cellID", "celltype_rf", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_rna))[,c("cellID", "celltype_rf", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")
dat_relation_rna <- left_join(dat_relation_rna, celltype_first, by = "cellID_first")
dat_relation_rna <- left_join(dat_relation_rna, celltype_second, by = "cellID_second")
colnames(dat_relation_rna)[5] <- "FirstImageNumber"tumor_marker_protein <- c("pS6", "H3K27me3", "HLADR", "PDL1", "IDO1")
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", "Tcell_density_score_image", "Description", "MM_location", "Location")])
# filter
dat_rna <- dat_rna %>%
filter(Location != "CTRL")
# mean per image
dat_rna <- dat_rna %>%
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")])
# filter
dat_prot <- dat_prot %>%
filter(Location != "CTRL")
# mean per image
dat_prot <- dat_prot %>%
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") %>%
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,
group=Tcell_density_score_image)) +
geom_boxplot(alpha=0.2, lwd=1, aes(group=Tcell_density_score_image, fill = Tcell_density_score_image)) +
facet_wrap(~channel, scales = "free", ncol=3) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
geom_quasirandom(alpha=0.6, size=2, aes(group=Tcell_density_score_image, col = Tcell_density_score_image)) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size=18),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
xlab("") +
ylab("Mean Count per Image (asinh)") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +
guides(fill=guide_legend(title="Tcell Score", override.aes = c(lwd=0.5, alpha=1)))
leg <- get_legend(p)
grid.arrange(p + theme(legend.position = "none"))grid.arrange(leg)all_mask <- loadImages(x = "/home/ubuntu/bbvolume/Data/Analysis/melanoma_cohort_new_cp_pipeline/rna/cpout/",
pattern = "ilastik_s2_Probabilities_equalized_cellmask.tiff")# we load the metadata for the images.
image_mat_rna <- as.data.frame(read.csv(file = "data/rna/Image.csv",stringsAsFactors = FALSE))
# we extract only the FileNames of the masks as they are in the all_masks object
cur_df <- data.frame(cellmask = image_mat_rna$FileName_cellmask,
ImageNumber = image_mat_rna$ImageNumber,
Description = image_mat_rna$Metadata_Description)
# we set the rownames of the extracted data to be equal to the names of all_masks
rownames(cur_df) <- gsub(pattern = ".tiff",replacement = "",image_mat_rna$FileName_cellmask)
# we add the extracted information via mcols in the order of the all_masks object
mcols(all_mask) <- cur_df[names(all_mask),]all_mask <- scaleImages(all_mask,2^16-1)# subset masks
mask_sub <- all_mask[mcols(all_mask)$Description %in% c("L11", "N3")]
sce_rna_sub <- sce_rna[,sce_rna$Description %in% c("L11", "N3")]
plotCells(mask = mask_sub,
object = sce_rna_sub,
img_id = "Description", cell_id = "CellNumber",
colour_by = c("CD8", "Mart1", "SOX10", "B2M"),
colour = list(CD8 = c("black", "green"),
Mart1 = c("black", "blue"),
SOX10 = c("black", "blue"),
B2M = c("black", "red")),
display = "single",
exprs_values = "scaled_asinh",
scale = TRUE)tumor_dat <- data.frame(t(assay(sce_rna["B2M", sce_rna$celltype == "Tumor" & sce_rna$Location != "CTRL"], "asinh")))
tumor_dat$Description <- sce_rna[, sce_rna$celltype == "Tumor" & sce_rna$Location != "CTRL"]$Description
tumor_dat <- tumor_dat %>%
group_by(Description) %>%
summarise(mean_B2M = mean(B2M))
cur_df <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(Description, BlockID, celltype) %>%
summarise(n=n()) %>%
mutate(fraction = n/sum(n)) %>%
ungroup() %>%
complete(Description, celltype, fill = list(fraction = 0)) %>%
filter(celltype == "Tcytotoxic")
cur_df_chemokine <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(Description, chemokine) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ chemokine, value.var = "n", fill = 0) %>%
mutate(fraction_positive = `TRUE` / (`FALSE` + `TRUE`))
tumor_dat <- left_join(tumor_dat, cur_df)
tumor_dat_chemokine <- left_join(tumor_dat, cur_df_chemokine)
# remove bad images and controls
tumor_dat <- tumor_dat
tumor_dat_chemokine <- tumor_dat_chemokine
# boxplot
a <- ggplot(tumor_dat, aes(y=mean_B2M, x=log10(fraction))) +
geom_point(size=3) +
geom_smooth(method = "lm", formula = y ~ poly(x,2)) +
stat_poly_eq(formula = y ~ poly(x,2),
aes(label = ..rr.label..),
parse = TRUE, size=10) +
ylab("Mean B2M Expression (asinh)") +
xlab("Cytotoxic T cell Fraction (log10)") +
theme_bw() +
theme(text = element_text(size=18))
b <- ggplot(tumor_dat_chemokine, aes(y=mean_B2M, x=log10(fraction_positive))) +
geom_point(size=3) +
geom_smooth(method = "lm", formula = y~poly(x,2)) +
stat_poly_eq(formula = y ~ poly(x,2),
aes(label = ..rr.label..),
parse = TRUE, size=10) +
ylab("Mean B2M Expression (asinh)") +
xlab("Chemokine-Expressing Cell Fraction (log10)") +
theme_bw() +
theme(text = element_text(size=18))
grid.arrange(a,b, nrow=1)Warning: Removed 6 rows containing non-finite values (stat_smooth).Warning: Removed 6 rows containing non-finite values (stat_poly_eq).# number of interactions tcytotoxic and tumor per image
dat_relation_sub <- dat_relation_rna[dat_relation_rna$celltype_first %in% c("exhausted Tcytotoxic", "Tcytotoxic") &
dat_relation_rna$celltype_second == "Tumor"]
# count number of interactions
count <- dat_relation_sub %>%
group_by(FirstImageNumber) %>%
summarise(n=n())
names(count)[1] <- "ImageNumber"
# fraction of exhausted cd8 per image
dysfunction <- data.frame(colData(sce_rna)) %>%
mutate(celltype2 = paste(celltype, CXCL13, sep = "_")) %>%
group_by(ImageNumber, celltype2) %>%
summarise(n=n()) %>%
reshape2::dcast(ImageNumber ~ celltype2, value.var = "n", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber"), variable.name = "celltype2", value.name = "n") %>%
group_by(ImageNumber) %>%
mutate(fraction = n / sum(n)) %>%
filter(celltype2 == "Tcytotoxic_1") %>%
ungroup() %>%
select(ImageNumber, fraction)
# correlation plot
cur_dat <- left_join(count, dysfunction)
ggplot(cur_dat, aes(x=log10(fraction), y=log10(n))) +
geom_point(size=3) +
geom_smooth(method="lm") +
stat_poly_eq(formula = y ~ x,
aes(label = ..rr.label..),
parse = TRUE, size=10) +
theme_bw() +
theme(text = element_text(size=14)) +
xlab("Fraction of Dysfunctional Cytotoxic T Cells (log10)") +
ylab("Number of CD8/Tumor Interactions\n(log10)")Warning: Removed 59 rows containing non-finite values (stat_smooth).Warning: Removed 59 rows containing non-finite values (stat_poly_eq).# add dysfunction score to dat_relation
ex_score <- data.frame(colData(sce_prot)) %>%
distinct(ImageNumber, .keep_all = T) %>%
select(ImageNumber, dysfunction_score, MM_location)
ex_score$FirstImageNumber <- ex_score$ImageNumber
dat_relation <- left_join(dat_relation, ex_score[,c("FirstImageNumber", "dysfunction_score", "MM_location")])
sum <- dat_relation %>%
filter(celltype_first == "Tcytotoxic" & celltype_second == "Tumor" & !is.na(dysfunction_score)) %>%
group_by(FirstImageNumber, MM_location, dysfunction_score, celltype_first, celltype_clust_second) %>%
summarise(n=n()) %>%
reshape2::dcast(FirstImageNumber + MM_location + dysfunction_score + celltype_first ~ celltype_clust_second, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("FirstImageNumber", "MM_location", "dysfunction_score", "celltype_first"), variable.name = "celltype", value.name = "n")
#sum <- sum[sum$MM_location == "skin_subcutaneous",]
# calculate fractions for every image (makes it more comparable)
sum2 <- sum %>%
group_by(FirstImageNumber) %>%
mutate(fraction = n/sum(n)) %>%
ungroup()
stat.test <- sum2 %>%
group_by(celltype) %>%
wilcox_test(data = ., fraction ~ dysfunction_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj") %>%
add_x_position(x = "celltype", dodge = 0.8)
ggplot(sum2, aes(x=celltype, y=fraction)) +
geom_boxplot(alpha=.2, lwd=1, aes(fill = dysfunction_score)) +
geom_quasirandom(alpha=.6, dodge.width=.75, size=2, aes(group = dysfunction_score, col=dysfunction_score)) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7, y.position = 0.9) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size = 16),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
guides(fill=guide_legend(title="Dysfunction Score", override.aes = aes(lwd=0.5))) +
xlab("") +
ylab("Fraction of Interactions") +
ylim(0,1)tumor_marker_protein <- c("S100", "MiTF")
tumor_marker_rna <- c("Mart1", "pRB")
# rna data
dat_rna <- data.frame(t(assay(sce_rna[tumor_marker_rna, sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "dysfunction_score", "Description", "MM_location")])
# filter
dat_rna <- dat_rna %>%
filter(dysfunction_score %in% c("high dysfunction", "low dysfunction"))
# mean per image
dat_rna <- dat_rna %>%
select(-cellID) %>%
group_by(Description, dysfunction_score) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("Description", "dysfunction_score"), variable.name = "channel", value.name = "asinh")
# protein data
dat_prot <- data.frame(t(assay(sce_prot[tumor_marker_protein,, sce_prot$celltype == "Tumor"], "asinh")))
dat_prot$cellID <- rownames(dat_prot)
dat_prot <- left_join(dat_prot, data.frame(colData(sce_prot))[,c("cellID", "dysfunction_score", "Description", "MM_location")])
# filter
dat_prot <- dat_prot %>%
filter(dysfunction_score %in% c("high dysfunction", "low dysfunction"))
# mean per image
dat_prot <- dat_prot %>%
select(-cellID) %>%
group_by(Description, dysfunction_score) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_prot <- dat_prot %>%
reshape2::melt(id.vars = c("Description", "dysfunction_score"), variable.name = "channel", value.name = "asinh")
# join both data sets
comb <- rbind(dat_prot, dat_rna)
stat.test <- comb %>%
group_by(channel) %>%
wilcox_test(data = ., asinh ~ dysfunction_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj") %>%
add_xy_position(x = "celltype", dodge = 0.8)
# plot
ggplot(comb, aes(x=dysfunction_score, y=asinh)) +
geom_boxplot(alpha=0.2, lwd=1, aes(fill=dysfunction_score)) +
geom_quasirandom(alpha=0.6, size=3, aes(col=dysfunction_score)) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size=18),
legend.position = "none") +
facet_wrap(~channel, scales = "free") +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
ylab("Mean Expression (asinh)") +
xlab("") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.2)))# fraction of exhausted cd8 per image
dysfunction <- data.frame(colData(sce_rna)) %>%
mutate(celltype2 = paste(celltype, CXCL13, sep = "_")) %>%
group_by(ImageNumber, celltype2) %>%
summarise(n=n()) %>%
reshape2::dcast(ImageNumber ~ celltype2, value.var = "n", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber"), variable.name = "celltype2", value.name = "n") %>%
group_by(ImageNumber) %>%
mutate(fraction = n / sum(n)) %>%
filter(celltype2 == "Tcytotoxic_1") %>%
ungroup() %>%
select(ImageNumber, fraction)
# rna data
dat_rna <- data.frame(t(assay(sce_rna["S100", sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "ImageNumber")])
# mean per image
dat_rna <- dat_rna %>%
select(-cellID) %>%
group_by(ImageNumber) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("ImageNumber"), variable.name = "channel", value.name = "asinh")
# correlation plot
cur_dat <- left_join(dysfunction, dat_rna)
# high density images
cur_dat <- cur_dat[cur_dat$ImageNumber %in% unique(sce_rna[,colData(sce_rna)$dysfunction_score %in% c("high dysfunction", "low dysfunction")]$ImageNumber),]
ggplot(cur_dat, aes(x=asinh, y=log10(fraction))) +
geom_point(size=3) +
geom_smooth(method="lm") +
stat_poly_eq(formula = y ~ x,
aes(label = ..rr.label..),
parse = TRUE, size=10) +
theme_bw() +
theme(text = element_text(size=15)) +
xlab("Mean S100 (asinh)") +
ylab("Fraction of Dysfunctional T cells\n(log10)")Warning: Removed 1 rows containing non-finite values (stat_smooth).Warning: Removed 1 rows containing non-finite values (stat_poly_eq).# 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(Internal_pat_ID, .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 4im_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"), ]$mm2)
im_size <- im_size[im_size != "G1 - split",]
cur_dat <- data.frame(colData(sce_prot[,sce_prot$BlockID %in% unique(dat_survival_prot_sub$BlockID)])) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
#filter(mmLocationPunch != "LN_M") %>% # remove LN margin images
#filter(celltype != "Tumor") %>%
group_by(PatientID,BlockID, Description, bcell_patch_score, celltype_clustered) %>%
summarise(n=n()) %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score ~ celltype_clustered, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score"),
variable.name = "celltype", value.name = "n") %>%
group_by(Description) %>%
mutate(fraction = n/sum(n)) %>%
mutate(total_cells = sum(n)) %>%
ungroup() %>%
filter(celltype %in% c("Tumor_1", "Tumor_9"))
cur_dat <- left_join(cur_dat, dat_survival_prot_sub[,c("BlockID", "Status_at_3m", "Primary_melanoma_type")]) %>%
distinct(Description, celltype, .keep_all = T)
cur_dat <- left_join(cur_dat, im_size[im_size$Description %in% unique(cur_dat$Description),])
cur_dat$n_per_mm2 <- cur_dat$n / cur_dat$mm2
stat.test <- cur_dat %>%
group_by(celltype) %>%
mutate(y_log10 = log10(n_per_mm2+1)) %>%
wilcox_test(data = ., y_log10 ~ Status_at_3m) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj") %>%
add_xy_position(x = "Status_at_3m", dodge = 0.8)
ggplot(cur_dat, aes(x=Status_at_3m, y=log10(n_per_mm2+1), group = Status_at_3m, label=PatientID)) +
geom_boxplot(alpha=.4, outlier.shape = NA, aes(fill=Status_at_3m)) +
geom_beeswarm(size=3) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 6) +
geom_label_repel(aes(group=Status_at_3m , label=PatientID), max.overlaps = 12, size = 5, alpha=.5) +
theme_bw() +
theme(text = element_text(size=18),
legend.position = "none") +
ylab("Cells per mm2 (log10)") +
xlab("") +
guides(fill=guide_legend(title="Response 3m", override.aes = c(lwd=0.5, size=1))) +
facet_wrap(~celltype, scales = "free") +
scale_y_continuous(expand = c(0.1, 0))Warning: Duplicated aesthetics after name standardisation: sizeWarning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
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] cytomapper_1.3.1 EBImage_4.32.0
[5] ggalluvial_0.12.3 survminer_0.4.8
[7] cowplot_1.1.1 scater_1.16.2
[9] dittoSeq_1.0.2 coxme_2.2-16
[11] bdsmatrix_1.3-4 survival_3.2-7
[13] ggpmisc_0.3.7 gridExtra_2.3
[15] ggbeeswarm_0.6.0 ggpubr_0.4.0
[17] RColorBrewer_1.1-2 circlize_0.4.12
[19] colorRamps_2.3 ComplexHeatmap_2.4.3
[21] cba_0.2-21 proxy_0.4-24
[23] data.table_1.13.6 forcats_0.5.0
[25] stringr_1.4.0 dplyr_1.0.2
[27] purrr_0.3.4 readr_1.4.0
[29] tidyr_1.1.2 tibble_3.0.4
[31] ggplot2_3.3.3 tidyverse_1.3.0
[33] reshape2_1.4.4 SingleCellExperiment_1.12.0
[35] SummarizedExperiment_1.20.0 Biobase_2.50.0
[37] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[39] IRanges_2.24.1 S4Vectors_0.28.1
[41] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[43] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.1.4 shinydashboard_0.7.1
[3] tidyselect_1.1.0 htmlwidgets_1.5.3
[5] BiocParallel_1.22.0 munsell_0.5.0
[7] codetools_0.2-18 withr_2.3.0
[9] colorspace_2.0-0 knitr_1.30
[11] rstudioapi_0.13 ggsignif_0.6.0
[13] labeling_0.4.2 git2r_0.28.0
[15] GenomeInfoDbData_1.2.4 KMsurv_0.1-5
[17] farver_2.0.3 pheatmap_1.0.12
[19] rprojroot_2.0.2 vctrs_0.3.6
[21] generics_0.1.0 xfun_0.20
[23] R6_2.5.0 clue_0.3-58
[25] rsvd_1.0.3 locfit_1.5-9.4
[27] bitops_1.0-6 DelayedArray_0.16.0
[29] assertthat_0.2.1 promises_1.1.1
[31] scales_1.1.1 beeswarm_0.2.3
[33] gtable_0.3.0 rlang_0.4.10
[35] systemfonts_0.3.2 GlobalOptions_0.1.2
[37] splines_4.0.3 broom_0.7.3
[39] yaml_2.2.1 abind_1.4-5
[41] modelr_0.1.8 backports_1.2.1
[43] httpuv_1.5.4 tools_4.0.3
[45] ellipsis_0.3.1 raster_3.4-5
[47] polynom_1.4-0 ggridges_0.5.3
[49] Rcpp_1.0.5 plyr_1.8.6
[51] zlibbioc_1.36.0 RCurl_1.98-1.2
[53] GetoptLong_1.0.5 viridis_0.5.1
[55] zoo_1.8-8 haven_2.3.1
[57] cluster_2.1.0 fs_1.5.0
[59] magrittr_2.0.1 openxlsx_4.2.3
[61] reprex_0.3.0 whisker_0.4
[63] hms_0.5.3 mime_0.9
[65] evaluate_0.14 fftwtools_0.9-9
[67] xtable_1.8-4 rio_0.5.16
[69] jpeg_0.1-8.1 readxl_1.3.1
[71] shape_1.4.5 compiler_4.0.3
[73] crayon_1.3.4 htmltools_0.5.0
[75] mgcv_1.8-33 later_1.1.0.1
[77] tiff_0.1-6 lubridate_1.7.9.2
[79] DBI_1.1.0 dbplyr_2.0.0
[81] Matrix_1.3-2 car_3.0-10
[83] cli_2.2.0 pkgconfig_2.0.3
[85] km.ci_0.5-2 foreign_0.8-81
[87] sp_1.4-5 xml2_1.3.2
[89] svglite_1.2.3.2 vipor_0.4.5
[91] XVector_0.30.0 rvest_0.3.6
[93] digest_0.6.27 rmarkdown_2.6
[95] cellranger_1.1.0 survMisc_0.5.5
[97] edgeR_3.30.3 DelayedMatrixStats_1.10.1
[99] gdtools_0.2.3 curl_4.3
[101] shiny_1.5.0 rjson_0.2.20
[103] lifecycle_0.2.0 nlme_3.1-151
[105] jsonlite_1.7.2 carData_3.0-4
[107] BiocNeighbors_1.6.0 viridisLite_0.3.0
[109] limma_3.44.3 fansi_0.4.1
[111] pillar_1.4.7 lattice_0.20-41
[113] fastmap_1.0.1 httr_1.4.2
[115] glue_1.4.2 zip_2.1.1
[117] png_0.1-7 svgPanZoom_0.3.4
[119] stringi_1.5.3 BiocSingular_1.4.0
[121] irlba_2.3.3