Last updated: 2021-04-12
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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 | d8819f2 | toobiwankenobi | 2020-10-08 | read new data (nuclei expansion) and adapt scripts |
| Rmd | a21c858 | toobiwankenobi | 2020-08-06 | adapt pipeline |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
This script generates plots for Figure 2.
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
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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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visible FALSE FALSE
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
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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(ComplexHeatmap)
library(data.table)
library(dplyr)
library(janitor)
library(tidyr)
library(ggpmisc)
library(cowplot)
library(corrplot)
library(gridExtra)
library(ggalluvial)
library(ggbeeswarm)
library(ggpubr)
library(RColorBrewer)
library(colorRamps)
library(circlize)
library(forcats)
library(ggpmisc)sce_rna <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot <- readRDS(file= "data/data_for_analysis/sce_protein.rds")cur_dt <- as.data.table(colData(sce_rna))
# combinations with more than 600 occurrences
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
targets_no_control <- replace(targets, match(c("CXCL8", "CCL18"),targets), c("CCL18", "CXCL8"))
# remove control samples
cur_dt <- cur_dt[MM_location_simplified != "control",]
# extract chemokine columns
chemokines <- cbind(cur_dt[,c("ImageNumber", "MM_location_simplified", "celltype")], cur_dt[,grepl(glob2rx("C*L*"),names(cur_dt)), with = F])
# create combination matrix
m <- make_comb_mat(chemokines, top_n_sets = 11)
# filter based on combination size and combination degree
m <- m[comb_size(m) >= 600 & comb_degree(m) > 0]
# sort according to abundance
m <- m[order(-comb_size(m))]
# extract comb names
comb_names <- comb_name(m)
# count celltypes for each combination
celltypes <- data.table()
location <- data.table()
# summarize statistics for each combination (celltype fractions, location)
for (i in comb_names){
# subset
set <- chemokines[extract_comb(m, i)]
# chemokines celltypes
set1 <- set %>%
group_by(celltype) %>%
summarise(n=n()) %>%
reshape2::dcast(.,i ~ celltype, value.var = "n")
# chemokines by location
set2 <- set %>%
group_by(ImageNumber, MM_location_simplified) %>%
summarise(n=n())
# add images with no combinations to not distort median
set2_add <- distinct(cur_dt[,c("ImageNumber", "MM_location_simplified")], ImageNumber, .keep_all = T)
set2_add$n <- 0
# subset to only contain images which are not already part of set2
set2_add <- set2_add[!(ImageNumber %in% set2$ImageNumber),]
set2 <- set2 %>%
rbind(., set2_add) %>%
group_by(MM_location_simplified) %>%
mutate(median = median(n)) %>%
distinct(MM_location_simplified, median) %>%
reshape2::dcast(.,i ~ MM_location_simplified, value.var = "median")
# add to data.frame
celltypes <- rbind(celltypes, set1, fill = TRUE)
location <- rbind(location, set2, fill = TRUE)
}
# replace NA
celltypes[is.na(celltypes)] <- 0
location[is.na(location)] <- 0
# properties of combination matrix
ss = set_size(m)
cs = comb_size(m)
# create plot
ht = UpSet(m,
set_order = order(ss),
comb_order = order(cs, decreasing = T),
top_annotation = HeatmapAnnotation(
"Number of\nExpressing Cells" = anno_barplot(celltypes[,-1],
ylim = c(0, max(cs)*1.1),
border = FALSE,
gp = gpar(fill = metadata(sce_rna)$colour_vectors$celltype[colnames(celltypes[,-1])]),
axis_param = list(gp = gpar(fontsize=20)),
height = unit(12, "cm")),
annotation_name_side = "left",
annotation_name_rot = 0,
annotation_name_gp = gpar(fontsize=20)),
left_annotation = rowAnnotation(
"Total Number of\nExpressing Cells" = anno_barplot(-ss,
baseline = 0,
axis_param = list(
at = c(0, -5000, -10000, -15000),
labels = c(0, 5000, 10000, 15000),
labels_rot = 0,
gp = gpar(fontsize = 15)),
border = FALSE,
gp = gpar(fill = "black"),
width = unit(5, "cm"),
),
set_name = anno_text(set_name(m),
location = 0.5,
gp = gpar(fontsize=15),
just = "center",
width = max_text_width(set_name(m)) + unit(4, "mm")),
annotation_name_gp = gpar(fontsize=20)),
right_annotation = NULL,
show_row_names = FALSE,
pt_size = unit(5, "mm"),
lwd = 2,
width = unit(14, "cm"),
height = unit(14, "cm")
)
# draw heatmap
ht = draw(ht)
# add absolute numbers on top of barplot
od = column_order(ht)
row_od = row_order(ht)
decorate_annotation("Number of\nExpressing Cells", {
grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"),
default.units = "native", just = c("left", "bottom"),
gp = gpar(fontsize = 15, col = "#404040"), rot = 45)
})
decorate_annotation("Total Number of\nExpressing Cells", {
grid.text(ss[row_od],
x = unit(-ss[row_od], "native") + unit(-0.75, "cm"),
y = rev(seq_len(length(-ss))),
default.units = "native", rot = 0,
gp = gpar(fontsize = 13))
})# legend for celltypes
lgd1 = Legend(labels = colnames(celltypes[,-1]),
title = "Celltypes",
legend_gp = gpar(fill = metadata(sce_rna)$colour_vectors$celltype[colnames(celltypes[,-1])],
fontsize = 18),
nrow = 5)
draw(packLegend(lgd1,column_gap = unit(0.5, "cm"),
max_height = unit(7, "cm")))# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_rna[rowData(sce_rna)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)
# chemokine info
chemo <- data.frame(colData(sce_rna))[,c("cellID", "expressor", "celltype")]
dat <- left_join(chemo, marker_expression, by = "cellID")
dat$cellID <- NULL
# aggregate data
dat_aggr <- dat %>%
filter(expressor %in% colnames(colData(sce_rna))[grepl("CXCL|CCL", colnames(colData(sce_rna)))]) %>%
group_by(celltype, expressor) %>%
summarise_all(funs(mean))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.# prepare matrix for heatmap
dat_aggr <- dat_aggr %>%
arrange(celltype, expressor)
stats <- dat %>%
filter(expressor %in% colnames(colData(sce_rna))[grepl("CXCL|CCL", colnames(colData(sce_rna)))]) %>%
group_by(celltype, expressor) %>%
summarise(n=n()) %>%
filter(n>1000) %>%
arrange(celltype, expressor)
dat_aggr <- dat_aggr %>%
filter(paste0(celltype,expressor) %in% paste0(stats$celltype,stats$expressor))
# factorize expressor for column sorting in heatmap
dat_aggr$expressor <- factor(dat_aggr$expressor, levels = c("CCL4", "CCL18", "CCL22", "CXCL8",
"CCL8", "CXCL9", "CXCL10", "CXCL13", "CCL2", "CXCL12", "CCL19"))
stats$expressor <- factor(stats$expressor, levels = c("CCL4", "CCL18", "CCL22", "CXCL8",
"CCL8", "CXCL9", "CXCL10", "CXCL13", "CCL2", "CXCL12", "CCL19"))
dat_aggr <- dat_aggr %>%
arrange(celltype, expressor)
stats <- stats %>%
arrange(celltype, expressor)
# create and scale scale matrix
m <- as.matrix(t(dat_aggr[,-c(1:2)]))
m <- t(scale(t(m)))
colnames(m) <- dat_aggr$celltype
# create top annotations
ha <- HeatmapAnnotation("Chemokine" = dat_aggr$expressor,
"Cells" = anno_barplot(stats[,3],
height = unit(1.5,"cm"),
axis_param = list(gp = gpar(fontsize=14))),
"Cell Numbers" = anno_text(t(stats[,3]),
which = "column",
rot = 90,
just = "center",
location = 0.5,
gp = gpar(fontsize=10)),
col = list("Chemokine" = metadata(sce_rna)$colour_vectors$chemokine_single),
show_legend = FALSE,
annotation_name_gp = gpar(fontsize = 16))
# row_split for markers
rowData(sce_rna)$heatmap_relevance <- ""
rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance <- "Lineage"
rowData(sce_rna[grepl("CXCL|CCL|DapB", rownames(sce_rna)),])$heatmap_relevance <- "Chemokine"
rowData(sce_rna[grepl("B2M|GLUT1|CD134|Lag3|CD163|cleavedPARP|pRB", rownames(sce_rna)),])$heatmap_relevance <- "Other"
# create heatmap
h <- Heatmap(m, name = "Scaled\nExpression",
row_split = rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance,
cluster_columns = FALSE,
show_column_names = FALSE,
top_annotation = ha,
show_heatmap_legend = FALSE,
column_split = colnames(m),
column_title_rot = 90,
cluster_column_slices = TRUE,
row_names_gp = gpar(fontsize = 16),
column_title_gp = gpar(fontsize = 23),
row_title_gp = gpar(fontsize = 23),
col = colorRamp2(c(-3, 0, 3), c("blue", "white", "red")),
height = unit(20, "cm"),
width = unit(25,"cm"))
# draw heatmap
draw(h)lgd1 = color_mapping_legend(h@matrix_color_mapping, plot = FALSE, legend_direction = "horizontal", legend_width=unit(3,"cm"), at = c(-3:3))
lgd2 = color_mapping_legend(ha@anno_list$Chemokine@color_mapping, plot = FALSE, legend_direction = "horizontal", nrow = 4)
lgd_list = packLegend(lgd1,lgd2,direction = "horizontal", gap = unit(1,"cm"))
draw(lgd_list)# top abundant chemokines
cur_rna <- data.frame(colData(sce_rna))
# sum
rna_sum <- cur_rna %>%
group_by(Description, expressor) %>%
summarise(n = n()) %>%
reshape2::dcast(Description ~ expressor, value.var = "n", fill = 0)
# only keep highly abundant chemokines
rna_sum <- rna_sum[,colnames(rna_sum) %in% targets]
# correlation
cor <- cor(rna_sum, rna_sum, method = "pearson")
corrplot(cor,
order = "FPC",
addCoef.col = "white",
method = "circle",
tl.col="black",
tl.cex = 1.5)# top abundant chemokines
cur_rna <- data.frame(colData(sce_rna))
# protein data
cur_prot <- data.frame(colData(sce_prot))
# sum
rna_sum <- cur_rna %>%
group_by(Description) %>%
mutate(total_cells=n()) %>%
ungroup() %>%
group_by(Description, total_cells, expressor) %>%
summarise(n=n()) %>%
mutate(fraction=n/total_cells) %>%
reshape2::dcast(Description ~ expressor, value.var = "fraction", fill = 0)
# only keep highly abundant chemokines
rna_sum <- rna_sum[,c("Description", targets)]
prot_sum <- cur_prot %>%
group_by(Description, celltype) %>%
summarise(n = n()) %>%
group_by(Description) %>%
mutate(fraction = n/sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)
# equal images
all(rna_sum$Description == prot_sum$Description)[1] TRUE# correlation
cor <- cor(rna_sum[,-1], prot_sum[,-1], method = "pearson")
corrplot(cor,
addCoef.col = "white",
method = "circle",
tl.col="black",
tl.cex = 1.5)# sum
chemokines <- data.frame(colData(sce_rna)) %>%
group_by(Description) %>%
mutate(total_cells=n()) %>%
ungroup() %>%
group_by(Description, total_cells, expressor) %>%
summarise(n=n()) %>%
mutate(fraction=n/total_cells) %>%
reshape2::dcast(Description ~ expressor, value.var = "fraction", fill = 0) %>%
select(Description, CXCL12, CXCL9, CCL22)
celltypes <- data.frame(colData(sce_prot)) %>%
group_by(Description, celltype) %>%
summarise(n=n()) %>%
mutate(fraction=n/sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0) %>%
select(Description, `CD8+ T cell`, pDC, `FOXP3+ T cell`) %>%
reshape2::melt(id.vars = "Description", variable.name = "celltype", value.name = "cell_fraction")
sum <- left_join(celltypes, chemokines, by = "Description") %>%
reshape2::melt(id.vars = c("Description", "celltype", "cell_fraction"), variable.name = "expressor", value.name = "chemo_fraction")
sum <- sum %>%
mutate(celltype_chemo = paste(celltype, expressor, sep = " ~ ")) %>%
filter(celltype_chemo %in% c("`CD8+ T cell` ~ CXCL9", "pDC ~ CXCL12", "`FOXP3+ T cell` ~ CCL22"))
sum$celltype_chemo <- factor(sum$celltype_chemo, levels = c("pDC ~ CXCL12", "`FOXP3+ T cell` ~ CCL22","`CD8+ T cell` ~ CXCL9"))
ggplot(sum, aes(x=log10(chemo_fraction), y=log10(cell_fraction))) +
geom_point() +
geom_smooth(method="lm") +
ylab("Celltype Fraction (log10)\n") +
xlab("Chemokine Fraction (log10)") +
scale_y_continuous(position = "right") +
theme_bw() +
theme(text=element_text(size=16),
plot.margin = margin(0.5,0.5,0.5,0.5, "cm"),
axis.title.y = element_text(hjust=1)) +
guides(col=guide_legend(title="Chemokines")) +
stat_poly_eq(formula = y ~ x, size=7,
aes(label = ..rr.label..),
parse = TRUE) +
facet_wrap(~celltype_chemo, ncol = 2, scales = "free", as.table = FALSE)Warning: Removed 48 rows containing non-finite values (stat_smooth).Warning: Removed 48 rows containing non-finite values (stat_poly_eq).
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] forcats_0.5.0 circlize_0.4.12
[3] colorRamps_2.3 RColorBrewer_1.1-2
[5] ggpubr_0.4.0 ggbeeswarm_0.6.0
[7] ggalluvial_0.12.3 gridExtra_2.3
[9] corrplot_0.84 cowplot_1.1.1
[11] ggpmisc_0.3.7 ggplot2_3.3.3
[13] tidyr_1.1.2 janitor_2.1.0
[15] dplyr_1.0.2 data.table_1.13.6
[17] ComplexHeatmap_2.4.3 SingleCellExperiment_1.12.0
[19] SummarizedExperiment_1.20.0 Biobase_2.50.0
[21] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[23] IRanges_2.24.1 S4Vectors_0.28.1
[25] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[27] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ggsignif_0.6.0 rjson_0.2.20
[4] ellipsis_0.3.1 rio_0.5.16 rprojroot_2.0.2
[7] snakecase_0.11.0 XVector_0.30.0 GlobalOptions_0.1.2
[10] fs_1.5.0 clue_0.3-58 rstudioapi_0.13
[13] farver_2.0.3 lubridate_1.7.9.2 splines_4.0.3
[16] knitr_1.30 polynom_1.4-0 broom_0.7.3
[19] cluster_2.1.0 png_0.1-7 compiler_4.0.3
[22] backports_1.2.1 Matrix_1.3-2 later_1.1.0.1
[25] htmltools_0.5.0 tools_4.0.3 gtable_0.3.0
[28] glue_1.4.2 GenomeInfoDbData_1.2.4 reshape2_1.4.4
[31] Rcpp_1.0.5 carData_3.0-4 cellranger_1.1.0
[34] vctrs_0.3.6 nlme_3.1-151 xfun_0.20
[37] stringr_1.4.0 openxlsx_4.2.3 lifecycle_0.2.0
[40] rstatix_0.6.0 zlibbioc_1.36.0 scales_1.1.1
[43] hms_0.5.3 promises_1.1.1 yaml_2.2.1
[46] curl_4.3 stringi_1.5.3 zip_2.1.1
[49] shape_1.4.5 rlang_0.4.10 pkgconfig_2.0.3
[52] bitops_1.0-6 evaluate_0.14 lattice_0.20-41
[55] purrr_0.3.4 labeling_0.4.2 tidyselect_1.1.0
[58] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
[61] generics_0.1.0 DelayedArray_0.16.0 pillar_1.4.7
[64] haven_2.3.1 whisker_0.4 foreign_0.8-81
[67] withr_2.3.0 mgcv_1.8-33 abind_1.4-5
[70] RCurl_1.98-1.2 tibble_3.0.4 crayon_1.3.4
[73] car_3.0-10 rmarkdown_2.6 GetoptLong_1.0.5
[76] readxl_1.3.1 git2r_0.28.0 digest_0.6.27
[79] httpuv_1.5.4 munsell_0.5.0 beeswarm_0.2.3
[82] vipor_0.4.5