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 | 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 | 90196fc | toobiwankenobi | 2020-10-13 | Supp Figure 2 |
| Rmd | 7affca0 | toobiwankenobi | 2020-10-13 | clean branch and add suppfigure 2 |
This script generates plots for Supplementary Figure 2.
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
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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/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(data.table)
library(survival)
library(ggplot2)
library(broom)
library(dplyr)
library(RColorBrewer)
library(ggalluvial)
library(tidyverse)
library(cowplot)
library(ggbeeswarm)
library(gridExtra)
library(SingleCellExperiment)
library(scater)
library(cba)
library(ComplexHeatmap)
library(reshape2)
library(rms)
library(ggrepel)
library(circlize)
library(coxme)
library(rstatix)
library(ggpubr)# clinical data
dat <- read_csv("data/data_for_analysis/protein/clinical_data_protein.csv")
# SCE object
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
clincial = read.csv(file = "data/data_for_analysis/protein/clinical_data_protein.csv")
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrolcur_dat <- data.frame(colData(sce_rna))
cur_dat <- cur_dat %>%
filter(celltype == "Tumor") %>%
filter(Location != "CTRL")
cur_dat <- cur_dat[,c("ImageNumber", "Mutation", colnames(cur_dat)[grepl(glob2rx("C*L*"),names(cur_dat))])]
# colSums of Chemokines in Tumor Cells (Multiple Producer count more than once)
cur_dat <- cur_dat %>%
group_by(ImageNumber, Mutation) %>%
mutate(cells = n()) %>%
group_by(ImageNumber, cells, Mutation) %>%
summarise_each(funs(sum))Warning: `summarise_each_()` is deprecated as of dplyr 0.7.0.
Please use `across()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.Warning: `funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# compute fractions
cur_dat[,4:14] <- cur_dat[,4:14] / t(cur_dat$cells)
cur_dat <- cur_dat %>%
filter(cells > 200) %>%
reshape2::melt(id.vars=c("ImageNumber", "cells", "Mutation"), variable.name="chemokine", value.name="fraction")
ggplot(cur_dat,aes(x=fct_reorder(chemokine, fraction, .fun = median, .desc = TRUE), y=fraction+0.001)) +
geom_boxplot(alpha=.5) +
geom_quasirandom(alpha=.2) +
theme_bw() +
theme(text=element_text(size=16)) +
ylab("Fraction of Expressing Tumor Cells\n(fraction + 0.001)") +
xlab("") +
scale_y_log10() +
annotation_logticks(sides = "l") +
geom_hline(yintercept = median(cur_dat$fraction+0.001), linetype = 2) Note: as the cohort is very diverse, we are using the BlockID as the minimal unit since clinical parameters are described per BlockID. However, sometimes we do have patients of which we have multiple FFPE blocks (BlockIDs). Nonetheless, clinical parameters are not given per patient but per patient FFPE block and are therefore considered the minimial unit.
dat[dat$BlockID %in% unique(sce_prot[,sce_prot$Location == "CTRL"]$BlockID),]$MM_location <- "Control"
# remove control samples
dat <- dat[dat$BlockID %in% unique(sce_prot[,sce_prot$Location != "CTRL"]$BlockID),]
p1 <- unique(dat[,c("BlockID","MM_location")]) %>%
ggplot()+
geom_bar(aes(y=MM_location),stat ="count") +
xlab("Biopsy Blocks per Location") +
ylab("Metastasis Location") +
theme_bw()+
theme(text = element_text(size=16))
p2 <- dat %>%
ggplot()+
geom_bar(aes(x=BlockID, fill=(MM_location)),stat="count")+
theme_bw()+
theme(axis.text.x = element_blank(),
axis.ticks.x=element_blank(),
text = element_text(size=16)) +
ylab("Number of Samples") +
xlab("Biopsy Blocks") +
scale_y_continuous(limits = c(0,4), expand = c(0, 0)) +
guides(fill=guide_legend(title="Metasis Location"))
plot_grid(p1,p2,rel_widths = c(1.25,3)) sce_rna$MM_location <- ifelse(sce_rna$MM_location %in% c("skin", "skin_undefine"), "skin_undefined", sce_rna$MM_location)
groups <- data.frame(colData(sce_rna)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(MM_location) %>%
distinct(PatientID, .keep_all = T) %>%
summarise(n=n()) %>%
filter(n>=10) %>%
arrange(-n)fractions_per_image <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, MM_location, expressor, celltype) %>%
summarise(n = n()) %>%
group_by(ImageNumber) %>%
mutate(fraction_per_image = n / sum(n)) %>%
group_by(ImageNumber, expressor) %>%
mutate(group_fraction = sum(fraction_per_image)) %>%
ungroup() %>%
filter(expressor %in% targets & MM_location %in% groups$MM_location)
# fraction of expressor cells per image
fraction_expressor_per_image <- fractions_per_image %>%
distinct(ImageNumber, MM_location, expressor, .keep_all = T) %>%
reshape2::dcast(ImageNumber + MM_location ~ expressor, value.var = "group_fraction", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location"), variable.name = "expressor",
value.name = "fraction_per_image")
# fraction of celltype expressing a certain combi per image
celltype_fractions <- fractions_per_image %>%
distinct(ImageNumber, celltype, expressor, .keep_all = T) %>%
reshape2::dcast(ImageNumber + MM_location + expressor ~ celltype, value.var = "fraction_per_image", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location", "expressor"),
variable.name = "celltype", value.name = "fraction_per_image") %>%
reshape2::dcast(ImageNumber + MM_location + celltype ~ expressor, value.var = "fraction_per_image", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location", "celltype"),
variable.name = "expressor", value.name = "fraction_per_image") %>%
group_by(MM_location, expressor, celltype) %>%
summarise(sum_fraction = sum(fraction_per_image)) %>% # sum-up fractions over all images
group_by(MM_location, expressor) %>%
mutate(proportions = sum_fraction / sum(sum_fraction)) # calculate proportions for each expressor# calculate signif of expressor-fractions per MM_location
fraction_expressor_per_image %>%
group_by(expressor) %>%
wilcox_test(fraction_per_image ~ MM_location) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
arrange(p.adj)# A tibble: 14 x 10
expressor .y. group1 group2 n1 n2 statistic p p.adj
<fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
1 CCL19 frac… LN skin_… 49 52 1739 0.00129 0.0181
2 CXCL13 frac… LN skin_… 49 52 1634. 0.0138 0.0966
3 CCL22 frac… LN skin_… 49 52 1557 0.0548 0.145
4 CXCL12_C… frac… LN skin_… 49 52 1535 0.062 0.145
5 CXCL8 frac… LN skin_… 49 52 1580 0.0378 0.145
6 CXCL9_CC… frac… LN skin_… 49 52 1523 0.0487 0.145
7 CCL18 frac… LN skin_… 49 52 1501 0.124 0.217
8 CXCL10_C… frac… LN skin_… 49 52 1054 0.121 0.217
9 CXCL10 frac… LN skin_… 49 52 1095 0.225 0.315
10 CXCL12 frac… LN skin_… 49 52 1457 0.215 0.315
11 CCL4 frac… LN skin_… 49 52 1382 0.465 0.592
12 CCL2 frac… LN skin_… 49 52 1326. 0.724 0.845
13 CXCL10_C… frac… LN skin_… 49 52 1270 0.981 0.981
14 CXCL9 frac… LN skin_… 49 52 1288 0.926 0.981
# … with 1 more variable: p.adj.signif <chr>plot_list <- list()
for(i in groups$MM_location) {
a <- fraction_expressor_per_image %>%
filter(MM_location == i) %>%
group_by(ImageNumber, expressor) %>%
ggplot(., aes(y=as.factor(expressor), x=fraction_per_image)) +
geom_boxplot() +
geom_point(alpha=0.2) +
theme_bw() +
theme(axis.title.y = element_blank(),
axis.text.y = element_text(hjust=0.5)) +
xlab("Cell Fraction per Image") +
coord_cartesian(xlim = c(0,0.05))
b <- celltype_fractions %>%
filter(MM_location == i) %>%
ggplot(., aes(y=expressor, x=-proportions, fill=celltype)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank()) +
guides(fill=guide_legend(title = "Cell Type",nrow=2,byrow=TRUE)) +
xlab("Producing Cell Types") +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype),
breaks = names(metadata(sce_rna)$colour_vectors$celltype),
labels = names(metadata(sce_rna)$colour_vectors$celltype)) +
scale_x_continuous(breaks=c(-1.00,-0.75,-0.5, -0.25, 0.00),
labels=c("100%", "75%", "50%", "25%", "0%"))
leg <- get_legend(b)
grid.arrange(b+theme(legend.position = "none"),a,nrow=1,
widths = c(.75,1),
top = i)
}grid.arrange(leg)# add control location to sce
sce_rna$MM_location_simplified2 <- sce_rna$MM_location_simplified
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "Skin"]$MM_location_simplified2 <- "control skin"
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "Lymphnode"]$MM_location_simplified2 <- "control LN"
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "PSO"]$MM_location_simplified2 <- "control psoriasis"
frac <- data.frame(colData(sce_rna)) %>%
filter(MM_location_simplified2 != "control psoriasis") %>%
group_by(Description, MM_location_simplified2, expressor) %>%
summarise(n=n()) %>%
mutate(fraction = n / sum(n)) %>%
filter(expressor %in% targets) %>%
reshape2::dcast(Description + MM_location_simplified2 ~ expressor, value.var = "fraction", fill = 0) %>%
reshape2::melt(id.vars = c("Description", "MM_location_simplified2"), variable.name = "expressor", value.name = "fraction")
ggplot(frac, aes(x=expressor, y = fraction)) +
geom_boxplot(alpha=1, outlier.size = 0.5, aes(fill = MM_location_simplified2)) +
scale_color_discrete(guide=FALSE) +
theme_bw() +
theme(text = element_text(size = 15),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="Met Location", override.aes = aes(lwd=0.5))) +
xlab("") +
ylab("Fractions") +
coord_cartesian(ylim = c(0,0.06))targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
# subset sce_rna
group_size <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, Mutation) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(Mutation) %>%
summarise(n=n()) %>%
filter(n>10 & Mutation != "")
sce_rna_sub <- sce_rna[,sce_rna$Mutation %in% group_size$Mutation]
a <- plotCellCounts(sce = sce_rna_sub,
sce_sub = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets)],
cellID = "cellID",
colour_by = "expressor",
split_by = "Mutation",
imageID = "ImageNumber",
normalize = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$chemokine_combinations) +
guides(fill=guide_legend("Chemokine")) +
theme(text = element_text(size=18))
b <- plotCellCounts(sce = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets)],
cellID = "cellID",
colour_by = "celltype",
split_by = "Mutation",
imageID = "ImageNumber",
proportion = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$celltype) +
guides(fill=guide_legend("Cell Type")) +
theme(text = element_text(size=18))
# fraction of chemokine-expressing cells per image
chemo <- data.frame(colData(sce_rna_sub)) %>%
#filter(MM_location_simplified2 %in% c("skin_subcutaneous", "LN")) %>%
group_by(ImageNumber, Mutation, chemokine, MM_location_simplified2) %>%
summarise(n=n()) %>%
group_by(ImageNumber) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(ImageNumber + Mutation + MM_location_simplified2 ~ chemokine, value.var = "fraction")
median_expression <- median(chemo$`TRUE`)
stat.test <- chemo %>%
wilcox_test(`TRUE` ~ Mutation) %>%
adjust_pvalue(method="BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(x = "Mutation")
stat.test$y.position <- stat.test$y.position - 0.125
c <- ggplot(chemo, aes(x=Mutation, y=`TRUE`)) +
geom_boxplot(alpha=0.2, lwd=1.5) +
geom_quasirandom(aes(col=MM_location_simplified2), size=2, alpha=.8) +
#geom_hline(yintercept = median_expression, linetype=2, size=2) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
ylab("Fraction of Chemokine-Expressing Cells") +
theme_bw() +
theme(text = element_text(size=16),
axis.text.x = element_text(angle=45, hjust=1, vjust=1)) +
guides(col=guide_legend("Location")) +
coord_cartesian(ylim=c(0,0.35))
p <- grid.arrange(a,b, ncol=2, nrow=1)grid.arrange(p,c,nrow=1, widths = c(0.65,0.35))# MM_location for Mutations
data.frame(colData(sce_rna_sub)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(Mutation, MM_location_simplified) %>%
summarise(n=n()) %>%
group_by(Mutation) %>%
mutate(percentage = n / sum(n) * 100)# A tibble: 9 x 4
# Groups: Mutation [3]
Mutation MM_location_simplified n percentage
<chr> <chr> <int> <dbl>
1 BRAF LN 30 42.3
2 BRAF other 10 14.1
3 BRAF skin 31 43.7
4 NRAS LN 16 32
5 NRAS other 10 20
6 NRAS skin 24 48
7 wt LN 6 21.4
8 wt other 5 17.9
9 wt skin 17 60.7targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
# subset sce_rna
group_size <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, Mutation) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(Mutation) %>%
summarise(n=n()) %>%
filter(n>10 & Mutation != "")
sce_rna_sub <- sce_rna[,sce_rna$Mutation %in% group_size$Mutation]
a1 <- plotCellCounts(sce = sce_rna_sub[,sce_rna_sub$MM_location == "LN"],
sce_sub = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets & sce_rna_sub$MM_location == "LN")],
cellID = "cellID",
colour_by = "expressor",
split_by = "Mutation",
imageID = "ImageNumber",
normalize = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$chemokine_combinations) +
guides(fill=guide_legend("Chemokine")) +
theme(text = element_text(size=16)) +
ylab("Normalized Cell Fraction")
b1 <- plotCellCounts(sce = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets & sce_rna_sub$MM_location == "LN")],
cellID = "cellID",
colour_by = "celltype",
split_by = "Mutation",
imageID = "ImageNumber",
proportion = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$celltype) +
guides(fill=guide_legend("Cell Type")) +
theme(text = element_text(size=16))
a2 <- plotCellCounts(sce = sce_rna_sub[,sce_rna_sub$MM_location == "skin_subcutaneous"],
sce_sub = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets & sce_rna_sub$MM_location == "skin_subcutaneous")],
cellID = "cellID",
colour_by = "expressor",
split_by = "Mutation",
imageID = "ImageNumber",
normalize = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$chemokine_combinations) +
guides(fill=guide_legend("Chemokine")) +
theme(text = element_text(size=16)) +
ylab("Normalized Cell Fraction")
b2 <- plotCellCounts(sce = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets & sce_rna_sub$MM_location == "skin_subcutaneous")],
cellID = "cellID",
colour_by = "celltype",
split_by = "Mutation",
imageID = "ImageNumber",
proportion = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$celltype) +
guides(fill=guide_legend("Cell Type")) +
theme(text = element_text(size=16))
# fraction of chemokine-expressing cells per image
chemo <- data.frame(colData(sce_rna_sub)) %>%
filter(MM_location %in% c("skin_subcutaneous", "LN")) %>%
group_by(ImageNumber, Mutation, chemokine, MM_location) %>%
summarise(n=n()) %>%
group_by(ImageNumber) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(ImageNumber + Mutation + MM_location ~ chemokine, value.var = "fraction")
median_expression <- median(chemo$`TRUE`)
stat.test <- chemo %>%
group_by(MM_location) %>%
wilcox_test(`TRUE` ~ Mutation) %>%
adjust_pvalue(method="BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(x = "Mutation")
stat.test$y.position <- stat.test$y.position - 0.025
c <- ggplot(chemo, aes(x=Mutation, y=`TRUE`)) +
geom_boxplot(alpha=0.2, lwd=1.5) +
geom_quasirandom(aes(col=MM_location), size=2, alpha=.8) +
#geom_hline(yintercept = median_expression, linetype=2, size=2) +
#stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
ylab("Fraction of Chemokine-Expressing Cells") +
theme_bw() +
theme(text = element_text(size=16),
axis.text.x = element_text(angle=45, hjust=1, vjust=1)) +
guides(col=guide_legend("Location")) +
coord_cartesian(ylim=c(0,0.25)) +
facet_wrap(~MM_location)
margin_top <- theme(plot.margin = unit(c(1,1,2,1), "cm"))
margin_bottom <- theme(plot.margin = unit(c(2,1,1,1), "cm"))
p <- grid.arrange(a1+margin_top,b1+margin_top,a2+margin_bottom,b2+margin_bottom, ncol=2, nrow=2)
grid.arrange(p,c,nrow=1, widths = c(0.65,0.35))
# MM_location for Mutations
data.frame(colData(sce_rna_sub)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(Mutation, MM_location_simplified) %>%
summarise(n=n()) %>%
group_by(Mutation) %>%
mutate(percentage = n / sum(n) * 100)tumor_marker_protein <- c("bCatenin", "Sox9", "pERK", "p75", "Ki67", "SOX10", "PARP", "S100", "MiTF", "IDO1", "PDL1")
tumor_marker_rna <- c("Mart1", "pRB", "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", "Mutation", "Description", "MM_location", "Location")])
# filter
dat_rna <- dat_rna %>%
filter(Location != "CTRL")
# mean per image
dat_rna <- dat_rna %>%
select(-cellID) %>%
group_by(Description, Mutation) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("Description", "Mutation"), 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", "Mutation", "Description", "MM_location", "Location")])
# filter
dat_prot <- dat_prot %>%
filter(Location != "CTRL")
# mean per image
dat_prot <- dat_prot %>%
select(-cellID) %>%
group_by(Description, Mutation) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_prot <- dat_prot %>%
reshape2::melt(id.vars = c("Description", "Mutation"), variable.name = "channel", value.name = "asinh")
# join both data sets
comb <- rbind(dat_prot, dat_rna) %>%
filter(Mutation %in% c("BRAF", "NRAS", "wt"))
stat.test <- comb %>%
group_by(channel) %>%
wilcox_test(data = ., asinh ~ Mutation) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(x = "Mutation", dodge = 0.8) %>%
filter(is.na(y.position) == FALSE)
# plot
p <- ggplot(comb, aes(x=Mutation, y=asinh)) +
geom_boxplot(alpha=0.2, lwd=1, aes(fill=Mutation)) +
geom_quasirandom(alpha=0.6, size=2, aes(col=Mutation)) +
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="Mutation", override.aes = c(lwd=0.5, alpha=1)))
leg <- get_legend(p)
grid.arrange(p + theme(legend.position = "none"))grid.arrange(leg)
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] ggpubr_0.4.0 rstatix_0.6.0
[3] coxme_2.2-16 bdsmatrix_1.3-4
[5] circlize_0.4.12 ggrepel_0.9.0
[7] rms_6.1-0 SparseM_1.78
[9] Hmisc_4.4-2 Formula_1.2-4
[11] lattice_0.20-41 reshape2_1.4.4
[13] ComplexHeatmap_2.4.3 cba_0.2-21
[15] proxy_0.4-24 scater_1.16.2
[17] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[19] Biobase_2.50.0 GenomicRanges_1.42.0
[21] GenomeInfoDb_1.26.2 IRanges_2.24.1
[23] S4Vectors_0.28.1 BiocGenerics_0.36.0
[25] MatrixGenerics_1.2.0 matrixStats_0.57.0
[27] gridExtra_2.3 ggbeeswarm_0.6.0
[29] cowplot_1.1.1 forcats_0.5.0
[31] stringr_1.4.0 purrr_0.3.4
[33] readr_1.4.0 tidyr_1.1.2
[35] tibble_3.0.4 tidyverse_1.3.0
[37] ggalluvial_0.12.3 RColorBrewer_1.1-2
[39] dplyr_1.0.2 broom_0.7.3
[41] ggplot2_3.3.3 survival_3.2-7
[43] data.table_1.13.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1
[3] plyr_1.8.6 splines_4.0.3
[5] BiocParallel_1.22.0 TH.data_1.0-10
[7] digest_0.6.27 htmltools_0.5.1.1
[9] viridis_0.5.1 fansi_0.4.1
[11] magrittr_2.0.1 checkmate_2.0.0
[13] cluster_2.1.0 openxlsx_4.2.3
[15] modelr_0.1.8 sandwich_3.0-0
[17] jpeg_0.1-8.1 colorspace_2.0-0
[19] rvest_0.3.6 haven_2.3.1
[21] xfun_0.20 crayon_1.3.4
[23] RCurl_1.98-1.2 jsonlite_1.7.2
[25] zoo_1.8-8 glue_1.4.2
[27] gtable_0.3.0 zlibbioc_1.36.0
[29] XVector_0.30.0 MatrixModels_0.4-1
[31] GetoptLong_1.0.5 DelayedArray_0.16.0
[33] car_3.0-10 BiocSingular_1.4.0
[35] shape_1.4.5 abind_1.4-5
[37] scales_1.1.1 mvtnorm_1.1-1
[39] DBI_1.1.0 Rcpp_1.0.5
[41] viridisLite_0.3.0 htmlTable_2.1.0
[43] clue_0.3-58 foreign_0.8-81
[45] rsvd_1.0.3 htmlwidgets_1.5.3
[47] httr_1.4.2 ellipsis_0.3.1
[49] farver_2.0.3 pkgconfig_2.0.3
[51] nnet_7.3-14 dbplyr_2.0.0
[53] utf8_1.1.4 labeling_0.4.2
[55] tidyselect_1.1.0 rlang_0.4.10
[57] later_1.1.0.1 munsell_0.5.0
[59] cellranger_1.1.0 tools_4.0.3
[61] cli_2.2.0 generics_0.1.0
[63] evaluate_0.14 yaml_2.2.1
[65] knitr_1.30 fs_1.5.0
[67] zip_2.1.1 nlme_3.1-151
[69] whisker_0.4 quantreg_5.82
[71] xml2_1.3.2 compiler_4.0.3
[73] rstudioapi_0.13 curl_4.3
[75] beeswarm_0.2.3 png_0.1-7
[77] ggsignif_0.6.0 reprex_0.3.0
[79] stringi_1.5.3 Matrix_1.3-2
[81] vctrs_0.3.6 pillar_1.4.7
[83] lifecycle_0.2.0 GlobalOptions_0.1.2
[85] BiocNeighbors_1.6.0 bitops_1.0-6
[87] irlba_2.3.3 conquer_1.0.2
[89] httpuv_1.5.4 R6_2.5.0
[91] latticeExtra_0.6-29 promises_1.1.1
[93] rio_0.5.16 vipor_0.4.5
[95] codetools_0.2-18 polspline_1.1.19
[97] MASS_7.3-53 assertthat_0.2.1
[99] rprojroot_2.0.2 rjson_0.2.20
[101] withr_2.3.0 multcomp_1.4-15
[103] GenomeInfoDbData_1.2.4 hms_0.5.3
[105] rpart_4.1-15 rmarkdown_2.6
[107] DelayedMatrixStats_1.10.1 carData_3.0-4
[109] git2r_0.28.0 lubridate_1.7.9.2
[111] base64enc_0.1-3