Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 73aa800 | toobiwankenobi | 2022-02-22 | add .html for static website |
| Rmd | 64e5fde | toobiwankenobi | 2022-02-16 | change order and naming of supp fig files |
| Rmd | dfe5f09 | toobiwankenobi | 2022-02-09 | change Figure order |
| Rmd | 588dbb1 | toobiwankenobi | 2022-02-06 | Figure Order |
| Rmd | b20b6fb | toobiwankenobi | 2022-02-02 | update code for Supp Figures |
| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
| Rmd | 434eee4 | toobiwankenobi | 2021-09-23 | Figure adaptions and new Supp Figure with gates |
| Rmd | c4e2793 | toobiwankenobi | 2021-08-04 | rearrange figure order to match pre-print |
| html | 4109ff1 | toobiwankenobi | 2021-07-07 | delete html files and adapt gitignore |
| Rmd | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| html | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| 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 | c3e8a47 | toobiwankenobi | 2020-10-12 | clean branch |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
This script generates plots for Figure 5.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
value ?
visible FALSE
code/helper_functions/censor_dat.R
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visible FALSE
code/helper_functions/detect_mRNA_expression.R
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visible FALSE
code/helper_functions/DistanceToClusterCenter.R
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visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
value ?
visible FALSE
code/helper_functions/getSpotnumber.R
value ?
visible FALSE
code/helper_functions/plotCellCounts.R
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visible FALSE
code/helper_functions/plotCellFractions.R
value ?
visible FALSE
code/helper_functions/plotDist.R code/helper_functions/read_Data.R
value ? ?
visible FALSE FALSE
code/helper_functions/scatter_function.R
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visible FALSE
code/helper_functions/sceChecks.R
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visible FALSE
code/helper_functions/validityChecks.R
value ?
visible FALSE library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
library(ggplot2)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggpubr)
library(ggbeeswarm)
library(gridExtra)
library(tidyr)
library(ggpmisc)
library(circlize)
library(dittoSeq)
library(scater)
library(cowplot)
library(cytomapper)
library(corrplot)
library(ggridges)
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")
sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]
# meta data
dat_relation = fread(file = "data/data_for_analysis/protein/Object relationships.csv",stringsAsFactors = FALSE)
dat_relation_rna = fread(file = "data/data_for_analysis/RNA/Object relationships.csv",stringsAsFactors = FALSE)
# image
image_mat_rna <- read.csv("data/data_for_analysis/rna/Image.csv")
# surv_dat
dat_survival_prot <- fread(file = "data/data_for_analysis/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"# subset sce for inflamed/exhausted in high samples
sce_protein_sub <- sce_prot[, sce_prot$dysfunction_density %in% c("high - High Dysfunction", "high - Low Dysfunction")]
# sample 9 images each
images <- data.frame(colData(sce_protein_sub)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(dysfunction_density) %>%
#filter(ImageNumber %in% sample(ImageNumber, 9)) %>%
select(ImageNumber, dysfunction_density)
return <- list()
for (i in c("high - High Dysfunction", "high - Low Dysfunction")){
# title name
title_name <- i
# count interactions in 20 sample images
cur_dat_relation <- data.frame(dat_relation) %>%
filter(FirstImageNumber %in% images[images$dysfunction_density == i,]$ImageNumber) %>%
select("celltype_first" ,"celltype_second") %>%
dplyr::count(celltype_first,celltype_second) %>%
data.frame()
# remove tumor-tumor interactions
cur_dat_relation <- cur_dat_relation[cur_dat_relation$celltype_first != "Tumor",]
cur_dat_relation <- cur_dat_relation[cur_dat_relation$celltype_first != "unknown",]
cur_dat_relation <- cur_dat_relation[cur_dat_relation$celltype_second != "unknown",]
# count interactions
cur_dat_relation_subcluster <- data.frame(dat_relation) %>%
filter(FirstImageNumber %in% images[images$dysfunction_density == i,]$ImageNumber) %>%
select("celltype_first" , "celltype_clust_second") %>%
dplyr::count(celltype_first, celltype_clust_second) %>%
data.frame()
# make coord diagram
chordDiagramFromDataFrame(cur_dat_relation,
grid.col = metadata(sce_prot)$colour_vectors$celltype,
reduce = 0.05,
transparency = ifelse(cur_dat_relation[[1]] %in% c("CD8+ T cell"),0,0.6),
annotationTrack = c("grid"))
}
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# create legend for tumor subclusters
lgd2 = Legend(labels = names(metadata(sce_prot)$colour_vector$celltype), title = "Cell Type", legend_gp = gpar(fill = unname(metadata(sce_prot)$colour_vector$celltype)))
draw(packLegend(lgd2,
#lgd1,
gap = unit(2, "cm")))
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
celltypes <- data.frame(colData(sce_prot)) %>%
filter(celltype != "Tumor") %>%
group_by(ImageNumber, bcell_patch_score, dysfunction_score, celltype) %>%
summarise(n=n()) %>%
mutate(fraction = n/sum(n)) %>%
filter(is.na(dysfunction_score) == FALSE)
celltypes$bcell_patch_score <- as.character(celltypes$bcell_patch_score)
celltypes$bcell_patch_score <- factor(celltypes$bcell_patch_score, levels = c("No B cells", "No B cell Patches", "Small B cell Patches", "B cell Follicles"))
stat.test <- celltypes %>%
group_by(celltype) %>%
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(x = "dysfunction_score", dodge = 0.8)
ggplot(celltypes, aes(x=celltype, y=fraction)) +
geom_boxplot(alpha=.5, lwd = 1, outlier.shape = NA, aes(fill=dysfunction_score)) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=1, aes(group=dysfunction_score)) +
stat_pvalue_manual(stat.test, x = "celltype", label = "p.adj.signif", size = 7, y.position = -0.05) +
theme_bw() +
theme(text = element_text(size = 20),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="Dysfunction Score", override.aes = c(lwd=0.5, alpha=1)), col=guide_legend(title="B cell Score")) +
xlab("") +
ylab("Cell Type Fractions") +
scale_color_manual(values = c("black", "lightblue", "darkblue", "red", "red4"),
labels = c(" ", "no Bcells", "few Bcells", "small patches", "large patches"),
guide = TRUE)
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# check images above median in Low Dysfunction group for B cells (all have large patches)
celltypes %>%
filter(dysfunction_score == "Low Dysfunction") %>%
filter(celltype == "B cell") %>%
group_by(dysfunction_score) %>%
mutate(median_frac = median(fraction)) %>%
filter(fraction > median_frac)# A tibble: 8 × 7
# Groups: dysfunction_score [1]
ImageNumber bcell_patch_score dysfunction_score celltype n fraction
<int> <fct> <chr> <chr> <int> <dbl>
1 19 Small B cell Patches Low Dysfunction B cell 131 0.0761
2 29 B cell Follicles Low Dysfunction B cell 883 0.134
3 33 B cell Follicles Low Dysfunction B cell 4829 0.365
4 86 B cell Follicles Low Dysfunction B cell 2416 0.253
5 95 B cell Follicles Low Dysfunction B cell 1602 0.116
6 109 B cell Follicles Low Dysfunction B cell 1337 0.255
7 114 B cell Follicles Low Dysfunction B cell 2895 0.362
8 118 B cell Follicles Low Dysfunction B cell 782 0.204
# … with 1 more variable: median_frac <dbl>a <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = T) %>%
group_by(bcell_patch_score, dysfunction_score) %>%
summarise(n=n()) %>%
filter(is.na(dysfunction_score) == F) %>%
group_by(dysfunction_score) %>%
mutate(fraction = n / sum(n)) %>%
ungroup()
ggplot(a) +
geom_col(aes(y=dysfunction_score, x=fraction, fill=bcell_patch_score)) +
theme_minimal() +
theme(text = element_text(size = 22)) +
xlab("Fraction of Samples") +
ylab("") +
guides(fill=guide_legend(title="B cell Score"))
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# fraction of all CXCL13 production
cxcl13_fraction <- data.frame(colData(sce_rna)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin samples
filter(CXCL13 == 1) %>%
group_by(Description, celltype, .drop = FALSE) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ celltype, fill = 0, value.var = "n") %>%
reshape2::melt(id.vars=c("Description"), variable.name = "celltype", value.name = "n") %>%
group_by(Description, .drop = FALSE) %>%
mutate(fraction = n / sum(n)) %>%
filter(celltype %in% c("CD8+ T cell", "CD8- T cell", "HLA-DR"))
all_images <- data.frame(colData(sce_rna)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>%
distinct(Description, .keep_all = T) %>%
select(Description)
# left_join to have all images
all_images <- left_join(all_images, cxcl13_fraction)
# add 0 to images that do not containt CXCL13 producing cells
all_images <- all_images %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0) %>%
select(-`NA`) %>%
reshape2::melt(id.vars=c("Description"), variable.name = "celltype", value.name = "fraction")
# get Bcell score for each image
Bcell <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = T) %>%
group_by(Description) %>%
select(Description, bcell_patch_score)
# add Bcell score
all_images <- left_join(all_images, Bcell[,c("Description","bcell_patch_score")])
# stats - is there a difference between celltype fractions in the groups?
all_images %>%
group_by(bcell_patch_score) %>%
wilcox_test(fraction ~ celltype) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1))# A tibble: 12 × 10
bcell_patch_score .y. group1 group2 n1 n2 statistic p p.adj
* <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
1 No B cells frac… CD8- … CD8+ … 54 54 1485 8.52e- 1 8.52e- 1
2 No B cells frac… CD8- … HLA-DR 54 54 1930. 6.97e- 5 1.39e- 4
3 No B cells frac… CD8+ … HLA-DR 54 54 1956. 3.58e- 5 8.59e- 5
4 No B cell Patches frac… CD8- … CD8+ … 52 52 1478. 4.06e- 1 4.87e- 1
5 No B cell Patches frac… CD8- … HLA-DR 52 52 2308. 8.1 e-12 9.72e-11
6 No B cell Patches frac… CD8+ … HLA-DR 52 52 2133 6.87e- 9 4.12e- 8
7 Small B cell Pat… frac… CD8- … CD8+ … 19 19 116 6.1 e- 2 9.15e- 2
8 Small B cell Pat… frac… CD8- … HLA-DR 19 19 349 4.14e- 7 1.66e- 6
9 Small B cell Pat… frac… CD8+ … HLA-DR 19 19 337 2.17e- 6 6.51e- 6
10 B cell Follicles frac… CD8- … CD8+ … 14 14 160 4 e- 3 6.86e- 3
11 B cell Follicles frac… CD8- … HLA-DR 14 14 112. 5.2 e- 1 5.67e- 1
12 B cell Follicles frac… CD8+ … HLA-DR 14 14 68 1.75e- 1 2.33e- 1
# … with 1 more variable: p.adj.signif <chr>ggplot(all_images,aes(x=bcell_patch_score, y = as.numeric(fraction), fill=celltype)) +
geom_boxplot(alpha=1, lwd=1, outlier.shape = NA, aes(fill=celltype)) +
geom_quasirandom(dodge.width=0.75, alpha=1, size=2, aes(fill=celltype)) +
ylab("Fraction of Total CXCL13 Expression") +
#stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
theme_bw() +
theme(text = element_text(size=19),
axis.text.x = element_text(angle=45, vjust=1, hjust=1)) +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype[c("CD8+ T cell", "CD8- T cell", "HLA-DR")]),
breaks = names(metadata(sce_rna)$colour_vectors$celltype[c("CD8+ T cell", "CD8- T cell", "HLA-DR")])) +
guides(fill=guide_legend(title="Cell Type", override.aes = c(lwd=0.5))) +
scale_color_discrete(guide = FALSE)Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
use `guide = "none"` instead.
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
example <- findPatch(sce_prot[,sce_prot$Description == "C9"], sce_prot[,colData(sce_prot)$celltype %in% c("B cell", "BnT cell")]$cellID,
'cellID',
'Center_X', 'Center_Y',
'Description',
distance = 15,
min_clust_size = 20,
output_colname = "example_patch")Time difference of 2.408804 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'Description',
'example_patch',
distance = 50,
output_colname = "example_milieu",
plot = TRUE)
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
Time difference of 3.101855 secs
[1] "milieus successfully added to sce object"celltypes <- data.frame(colData(sce_prot)) %>%
mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(mmLocationPunch != "LN_M") %>% # remove LN margin images
mutate(TCF7_PD1 = paste(PD1, TCF7, sep = "_")) %>%
group_by(PatientID,BlockID, Description, bcell_patch_score, celltype, TCF7_PD1) %>%
summarise(n=n()) %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score + celltype ~ TCF7_PD1, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score", "celltype"),
variable.name = "TCF7_PD1", value.name = "n") %>%
reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score + TCF7_PD1 ~ celltype, value.var = "n", fill=0) %>%
reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score", "TCF7_PD1"),
variable.name = "celltype", value.name = "n") %>%
group_by(Description, celltype) %>%
mutate(fraction = n/sum(n)) %>%
mutate(total_cells = sum(n)) %>%
ungroup() %>%
filter(celltype %in% c("CD8+ T cell", "CD4+ T cell"))
celltypes$bcell_patch_score <- as.character(celltypes$bcell_patch_score)
celltypes$bcell_patch_score <- factor(celltypes$bcell_patch_score, levels = c("No B cells", "No B cell Patches", "Small B cell Patches", "B cell Follicles"))
celltypes$celltype <- factor(celltypes$celltype, levels = c("CD8+ T cell", "CD4+ T cell"))
stat.test <- celltypes %>%
group_by(celltype, TCF7_PD1) %>%
kruskal_test(data = ., fraction ~ bcell_patch_score) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
mutate(group1 = celltype, group2 = TCF7_PD1) %>%
add_xy_position()
ggplot(celltypes, aes(x=TCF7_PD1, y=fraction)) +
geom_boxplot(alpha=1, lwd = 0.5, outlier.size = 0.5, aes(fill=bcell_patch_score)) +
stat_pvalue_manual(x = "group2", stat.test, y.position = -0.1, size = 6) +
theme_bw() +
theme(text = element_text(size = 14),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="B cell Score", override.aes = c(lwd=0.5))) +
xlab("") +
ylab("Fraction of Population") +
facet_wrap(~celltype, scales = "free") +
ylim(-0.1,1.1)Warning: Removed 40 rows containing non-finite values (stat_boxplot).
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# magnify PD1+TCF7+ population
ggplot(celltypes[celltypes$TCF7_PD1 == "PD1+_TCF7+",], aes(x=TCF7_PD1, y=fraction)) +
geom_boxplot(alpha=1, lwd = 0.5, outlier.size = 0.5, aes(fill=bcell_patch_score)) +
theme_bw() +
theme(text = element_text(size = 14),
axis.text.x = element_blank(),
legend.position = "none") +
xlab("") +
ylab("") +
facet_wrap(~celltype, scales = "free") +
coord_cartesian(ylim = c(0,0.05))Warning: Removed 10 rows containing non-finite values (stat_boxplot).
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# what fraction of each celltype is part of a milieu?
cur_dat <- data.frame(colData(sce_prot)) %>%
mutate(MMLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(MMLocationPunch != "LN_M") %>%
filter(Location != "CTRL") %>%
filter(celltype %in% c("CD8+ T cell", "CD4+ T cell")) %>%
mutate(status = paste(TCF7, PD1, sep = "_")) %>%
mutate(milieu_binary = ifelse(bcell_milieu > 0, 1, 0)) %>%
group_by(Description, milieu_binary, celltype, status) %>%
summarise(n=n()) %>%
group_by(Description, celltype, status) %>%
mutate(fraction_in_milieu = n / sum(n)) %>%
filter(milieu_binary == 1)
# what fraction of the total cell area is made up by cells that are part of a milieu
milieu_area <- data.frame(colData(sce_prot)) %>%
mutate(MMLocationPunch = paste(MM_location, Location, sep = "_")) %>%
filter(MMLocationPunch != "LN_M") %>%
filter(Location != "CTRL") %>%
mutate(milieu_binary = ifelse(bcell_milieu > 0, 1, 0)) %>%
group_by(Description, milieu_binary) %>%
summarise(area = sum(Area)) %>%
group_by(Description) %>%
mutate(fraction_of_area = area / sum(area)) %>%
filter(row_number() == n())
sum <- left_join(cur_dat, milieu_area, by="Description")
# calculate metric - milieu_fraction divided by milieu-area-fraction (this normalizes the first metric)
sum$metric <- sum$fraction_in_milieu / sum$fraction_of_area
sum$celltype <- factor(sum$celltype, levels = c("CD8+ T cell", "CD4+ T cell"))
# one-sample t test
stat.test <- sum %>%
group_by(celltype, status) %>%
mutate(log10_metric = log10(metric)) %>%
t_test(log10_metric ~ 1, mu = 0, alternative = "greater", detailed = TRUE) %>%
adjust_pvalue() %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1))
ggplot(sum, aes(x=sum$status, y=log10(metric))) +
geom_boxplot(outlier.shape = NA) +
geom_quasirandom(size=0.75) +
geom_hline(yintercept = 0) +
facet_wrap(~celltype) +
theme_bw() +
theme(text=element_text(size=14),
axis.text.x = element_text(angle=45, vjust=1, hjust=1)) +
stat_pvalue_manual(
stat.test, x = "status", y.position = 1.5,
label = "p.adj.signif",
position = position_dodge(0.8),
size=6) +
ylab("Enrichment in B cell milieus (log10)") +
xlab("")
| Version | Author | Date |
|---|---|---|
| 3697a9b | 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] RANN_2.6.1 concaveman_1.1.0
[3] sf_1.0-5 rstatix_0.7.0
[5] ggridges_0.5.3 corrplot_0.92
[7] cytomapper_1.6.0 EBImage_4.36.0
[9] cowplot_1.1.1 scater_1.22.0
[11] scuttle_1.4.0 dittoSeq_1.6.0
[13] ggpmisc_0.4.5 ggpp_0.4.3
[15] gridExtra_2.3 ggbeeswarm_0.6.0
[17] ggpubr_0.4.0 RColorBrewer_1.1-2
[19] circlize_0.4.13 colorRamps_2.3
[21] ComplexHeatmap_2.10.0 data.table_1.14.2
[23] forcats_0.5.1 stringr_1.4.0
[25] purrr_0.3.4 readr_2.1.2
[27] tidyr_1.2.0 tibble_3.1.6
[29] ggplot2_3.3.5 tidyverse_1.3.1
[31] reshape2_1.4.4 SingleCellExperiment_1.16.0
[33] SummarizedExperiment_1.24.0 Biobase_2.54.0
[35] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[37] IRanges_2.28.0 S4Vectors_0.32.3
[39] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[41] matrixStats_0.61.0 dplyr_1.0.7
[43] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2
[3] tidyselect_1.1.1 htmlwidgets_1.5.4
[5] BiocParallel_1.28.3 munsell_0.5.0
[7] ScaledMatrix_1.2.0 units_0.7-2
[9] codetools_0.2-18 withr_2.4.3
[11] colorspace_2.0-2 highr_0.9
[13] knitr_1.37 rstudioapi_0.13
[15] ggsignif_0.6.3 labeling_0.4.2
[17] git2r_0.29.0 GenomeInfoDbData_1.2.7
[19] farver_2.1.0 pheatmap_1.0.12
[21] rhdf5_2.38.0 rprojroot_2.0.2
[23] vctrs_0.3.8 generics_0.1.2
[25] xfun_0.29 R6_2.5.1
[27] doParallel_1.0.16 clue_0.3-60
[29] rsvd_1.0.5 locfit_1.5-9.4
[31] bitops_1.0-7 rhdf5filters_1.6.0
[33] DelayedArray_0.20.0 assertthat_0.2.1
[35] promises_1.2.0.1 scales_1.1.1
[37] beeswarm_0.4.0 gtable_0.3.0
[39] beachmat_2.10.0 processx_3.5.2
[41] rlang_1.0.0 MatrixModels_0.5-0
[43] systemfonts_1.0.3 GlobalOptions_0.1.2
[45] broom_0.7.12 yaml_2.2.2
[47] abind_1.4-5 modelr_0.1.8
[49] backports_1.4.1 httpuv_1.6.5
[51] tools_4.1.2 ellipsis_0.3.2
[53] raster_3.5-15 jquerylib_0.1.4
[55] proxy_0.4-26 Rcpp_1.0.8
[57] plyr_1.8.6 sparseMatrixStats_1.6.0
[59] zlibbioc_1.40.0 classInt_0.4-3
[61] RCurl_1.98-1.5 ps_1.6.0
[63] GetoptLong_1.0.5 viridis_0.6.2
[65] haven_2.4.3 ggrepel_0.9.1
[67] cluster_2.1.2 fs_1.5.2
[69] magrittr_2.0.2 SparseM_1.81
[71] reprex_2.0.1 whisker_0.4
[73] hms_1.1.1 mime_0.12
[75] evaluate_0.14 fftwtools_0.9-11
[77] xtable_1.8-4 jpeg_0.1-9
[79] readxl_1.3.1 shape_1.4.6
[81] compiler_4.1.2 V8_4.0.0
[83] KernSmooth_2.23-20 crayon_1.4.2
[85] htmltools_0.5.2 later_1.3.0
[87] tzdb_0.2.0 tiff_0.1-11
[89] lubridate_1.8.0 DBI_1.1.2
[91] dbplyr_2.1.1 Matrix_1.4-0
[93] car_3.0-12 cli_3.1.1
[95] parallel_4.1.2 pkgconfig_2.0.3
[97] getPass_0.2-2 sp_1.4-6
[99] terra_1.5-17 xml2_1.3.3
[101] foreach_1.5.2 svglite_2.0.0
[103] vipor_0.4.5 bslib_0.3.1
[105] XVector_0.34.0 rvest_1.0.2
[107] callr_3.7.0 digest_0.6.29
[109] rmarkdown_2.11 cellranger_1.1.0
[111] DelayedMatrixStats_1.16.0 curl_4.3.2
[113] shiny_1.7.1 quantreg_5.87
[115] rjson_0.2.21 lifecycle_1.0.1
[117] jsonlite_1.7.3 Rhdf5lib_1.16.0
[119] carData_3.0-5 BiocNeighbors_1.12.0
[121] viridisLite_0.4.0 fansi_1.0.2
[123] pillar_1.7.0 lattice_0.20-45
[125] fastmap_1.1.0 httr_1.4.2
[127] glue_1.6.1 png_0.1-7
[129] iterators_1.0.13 svgPanZoom_0.3.4
[131] class_7.3-20 stringi_1.7.6
[133] sass_0.4.0 HDF5Array_1.22.1
[135] BiocSingular_1.10.0 e1071_1.7-9
[137] irlba_2.3.5