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 | f9a3a83 | toobiwankenobi | 2022-02-08 | clean repo for release |
| Rmd | 588dbb1 | toobiwankenobi | 2022-02-06 | Figure Order |
| 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 | 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. Panel A was created manually.
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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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(ComplexHeatmap)
library(data.table)
library(dplyr)
library(tidyr)
library(ggpmisc)
library(cowplot)
library(corrplot)
library(gridExtra)
library(ggbeeswarm)
library(ggpubr)
library(RColorBrewer)
library(colorRamps)
library(circlize)
library(forcats)
library(viridis)
library(psych)
library(ggpmisc)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"]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$ImageNumber),]
set2 <- set2 %>%
rbind(., set2_add) %>%
group_by(MM_location_simplified) %>%
mutate(median = median(n)) %>%
distinct(MM_location_simplified, median) %>%
reshape2::dcast(.,i ~ MM_location_simplified, value.var = "median")
# add to data.frame
celltypes <- rbind(celltypes, set1, fill = TRUE)
location <- rbind(location, set2, fill = TRUE)
}
# replace NA
celltypes[is.na(celltypes)] <- 0
location[is.na(location)] <- 0
# properties of combination matrix
ss = set_size(m)
cs = comb_size(m)
# calculate fraction of chemokine combinations per patient
fraction_patient <- cur_dt %>%
select(PatientID, expressor) %>%
filter(expressor != "NA") %>%
group_by(PatientID, expressor) %>%
summarise(n=n()) %>%
group_by(expressor) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(expressor ~ PatientID, value.var = "fraction", fill = 0) %>%
filter(expressor %in% targets) %>%
reshape2::melt(id.vars=c("expressor"), variable.name="PatientID", value.name="fraction")
# how many chemokine combis are found in top10 patients?
fraction_patient <- fraction_patient %>%
group_by(expressor) %>%
slice_max(fraction, n=10) %>%
summarise(top10=round(sum(fraction)*100,0))
# change row-order to match targets and remove expressor column (only keep fractions)
fraction_patient <- fraction_patient[match(targets,fraction_patient$expressor),-1]
# max fraction coming from one image per expressor
fraction_patient$top10 <- paste0(fraction_patient$top10, "%")
# 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=14)),
height = unit(7.5, "cm")),
annotation_name_side = "left",
annotation_name_rot = 0,
annotation_name_gp = gpar(fontsize=14)),
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 = 12)),
border = FALSE,
gp = gpar(fill = "black"),
width = unit(3, "cm"),
),
set_name = anno_text(set_name(m),
location = 0.5,
gp = gpar(fontsize=14),
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(3, "mm"),
lwd = 2,
width = unit(8, "cm"),
height = unit(8, "cm"),
bottom_annotation = HeatmapAnnotation(Top10 = anno_text(t(fraction_patient),
just = "center", location = 0.5,
rot = 90,
gp = gpar(fontsize=13))),
)
# 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 = 13, 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 = 11))
})
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# 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")))
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# 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$cellID <- NULL
# aggregate data
dat_aggr <- dat %>%
filter(expressor %in% colnames(colData(sce_rna))[grepl("CXCL|CCL", colnames(colData(sce_rna)))]) %>%
group_by(celltype, expressor) %>%
summarise_all(funs(mean))Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.# prepare matrix for heatmap
dat_aggr <- dat_aggr %>%
arrange(celltype, expressor)
stats <- dat %>%
filter(expressor %in% colnames(colData(sce_rna))[grepl("CXCL|CCL", colnames(colData(sce_rna)))]) %>%
group_by(celltype, expressor) %>%
summarise(n=n()) %>%
filter(n>1000) %>%
arrange(celltype, expressor)
dat_aggr <- dat_aggr %>%
filter(paste0(celltype,expressor) %in% paste0(stats$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$expressor,
"Cells" = anno_barplot(stats[,3],
height = unit(1.5,"cm"),
axis_param = list(gp = gpar(fontsize=14))),
"Cell Numbers" = anno_text(t(stats[,3]),
which = "column",
rot = 90,
just = "center",
location = 0.5,
gp = gpar(fontsize=14)),
col = list("Chemokine" = metadata(sce_rna)$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 = 13),
column_title_gp = gpar(fontsize = 16),
row_title_gp = gpar(fontsize = 16),
col = colorRamp2(c(-3, 0, 3), c("blue", "white", "red")),
height = unit(15, "cm"),
width = unit(11,"cm"))
# draw heatmap
draw(h)
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
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$Chemokine@color_mapping, plot = FALSE, legend_direction = "horizontal", nrow = 4)
lgd_list = packLegend(lgd1,lgd2,direction = "horizontal", gap = unit(1,"cm"))
draw(lgd_list)
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
# define chemokines
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
# 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 <- psych::corr.test(rna_sum[,-1], prot_sum[,-1], method = "pearson",adjust = "BH")
cur_dat <- as.data.frame(cor$r)
cur_dat$variable <- rownames(cur_dat)
dat_long <- reshape2::melt(cur_dat,id.vars="variable")
colnames(dat_long) <- c("chemokines","celltypes","correlation")
p_dat <- as_tibble(cor$p)
p_dat$variable <- rownames(cur_dat)
pdat_long <- reshape2::melt(p_dat,id.vars="variable")
colnames(pdat_long) <- c("chemokines","celltypes","p_adj")
dat_long$p_adj <- pdat_long$p_adj
dat_long <- dat_long %>%
mutate(sig = ifelse(p_adj <= 0.001 & p_adj > 0.0001,0.001,p_adj))
dat_long <- dat_long %>%
mutate(sig = case_when(p_adj <= 0.0001 ~ "< 0.0001",
p_adj <= 0.001 & p_adj > 0.0001 ~ "< 0.001",
p_adj <= 0.01 & p_adj > 0.001 ~ "< 0.01",
p_adj <= 0.1 & p_adj > 0.01 ~ "< 0.1",
p_adj >0.1 ~ "ns"))
# plot
a <- ggplot()+
geom_tile(data = dat_long, aes(x = chemokines,y = celltypes,fill=sig),color = "gray",size = 0.1, alpha = 0.5)+
scale_fill_manual(values = c("< 0.0001" = "darkgreen", "< 0.001" = "green3", "< 0.01"="green", "< 0.1" = "lightgray","ns" = "white"), name = "adj p-value" )+
theme_classic()+
theme(axis.text.x = element_text(angle = 90,hjust = 1,vjust = 0.5),
text = element_text(size=18)) +
geom_point(data = dat_long, aes(x=chemokines,y = celltypes),size=5.5, show.legend = FALSE) +
geom_point(data = dat_long, aes(x=chemokines,y = celltypes, color=correlation),size= 5, shape=19) +
scale_color_gradient2(low="blue",mid= "white", high="red", name = "Pearson correlation") +
xlab("Chemokines") +
ylab("Cell Types")
a + theme(legend.position="none")
| Version | Author | Date |
|---|---|---|
| 3697a9b | toobiwankenobi | 2022-02-22 |
legend <- get_legend(a)
as_ggplot(legend)
| 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] psych_2.1.9 viridis_0.6.2
[3] viridisLite_0.4.0 forcats_0.5.1
[5] circlize_0.4.13 colorRamps_2.3
[7] RColorBrewer_1.1-2 ggpubr_0.4.0
[9] ggbeeswarm_0.6.0 gridExtra_2.3
[11] corrplot_0.92 cowplot_1.1.1
[13] ggpmisc_0.4.5 ggpp_0.4.3
[15] ggplot2_3.3.5 tidyr_1.2.0
[17] data.table_1.14.2 ComplexHeatmap_2.10.0
[19] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[21] Biobase_2.54.0 GenomicRanges_1.46.1
[23] GenomeInfoDb_1.30.1 IRanges_2.28.0
[25] S4Vectors_0.32.3 BiocGenerics_0.40.0
[27] MatrixGenerics_1.6.0 matrixStats_0.61.0
[29] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ggsignif_0.6.3 rjson_0.2.21
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.34.0
[7] GlobalOptions_0.1.2 fs_1.5.2 clue_0.3-60
[10] rstudioapi_0.13 farver_2.1.0 MatrixModels_0.5-0
[13] fansi_1.0.2 codetools_0.2-18 mnormt_2.0.2
[16] doParallel_1.0.16 knitr_1.37 jsonlite_1.7.3
[19] broom_0.7.12 cluster_2.1.2 png_0.1-7
[22] compiler_4.1.2 httr_1.4.2 backports_1.4.1
[25] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0
[28] cli_3.1.1 later_1.3.0 htmltools_0.5.2
[31] quantreg_5.87 tools_4.1.2 gtable_0.3.0
[34] glue_1.6.1 GenomeInfoDbData_1.2.7 reshape2_1.4.4
[37] Rcpp_1.0.8 carData_3.0-5 jquerylib_0.1.4
[40] vctrs_0.3.8 nlme_3.1-155 iterators_1.0.13
[43] xfun_0.29 stringr_1.4.0 ps_1.6.0
[46] lifecycle_1.0.1 rstatix_0.7.0 getPass_0.2-2
[49] zlibbioc_1.40.0 scales_1.1.1 promises_1.2.0.1
[52] parallel_4.1.2 SparseM_1.81 yaml_2.2.2
[55] sass_0.4.0 stringi_1.7.6 highr_0.9
[58] foreach_1.5.2 shape_1.4.6 rlang_1.0.0
[61] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
[64] lattice_0.20-45 purrr_0.3.4 labeling_0.4.2
[67] processx_3.5.2 tidyselect_1.1.1 plyr_1.8.6
[70] magrittr_2.0.2 R6_2.5.1 magick_2.7.3
[73] generics_0.1.2 DelayedArray_0.20.0 DBI_1.1.2
[76] pillar_1.7.0 whisker_0.4 withr_2.4.3
[79] abind_1.4-5 RCurl_1.98-1.5 tibble_3.1.6
[82] crayon_1.4.2 car_3.0-12 utf8_1.2.2
[85] tmvnsim_1.0-2 rmarkdown_2.11 GetoptLong_1.0.5
[88] callr_3.7.0 git2r_0.29.0 digest_0.6.29
[91] httpuv_1.6.5 munsell_0.5.0 beeswarm_0.4.0
[94] vipor_0.4.5 bslib_0.3.1