Last updated: 2021-02-19
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Knit directory: melanoma_publication_old_data/
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This script generates plots for Supplementary Figure 4.
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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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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visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
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visible FALSE
code/helper_functions/getSpotnumber.R
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visible FALSE
code/helper_functions/plotCellCounts.R
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visible FALSE
code/helper_functions/plotCellFractions.R
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visible FALSE
code/helper_functions/plotDist.R
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visible 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(rms)
library(ggrepel)
library(ggbeeswarm)
library(circlize)
library(ggpubr)
library(ggridges)
library(gridExtra)
library(rstatix)sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_prot[rowData(sce_prot)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)
dat <- data.frame(colData(sce_prot))[,c("cellID", "celltype_clustered")]
dat <- left_join(dat, marker_expression)Joining, by = "cellID"dat$cellID <- NULL
# aggregate the groups
dat_aggr <- dat %>%
group_by(celltype_clustered) %>%
summarise_all(funs(mean))Warning: `funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# number of cells per group
dat_sum <- dat %>%
group_by(celltype_clustered) %>%
summarise(n=n())`summarise()` ungrouping output (override with `.groups` argument)dat_sum <- data.frame(t(dat_sum))
# scale and center expression
dat_aggr[,-c(1)] <- scale(dat_aggr[,-c(1)])
# create matrix
m <- as.matrix(t(dat_aggr[,-c(1)]))
colnames(m) <- dat_aggr$celltype_clustered
# top annotation with number of cells
ha <- HeatmapAnnotation("Number of Cells" = anno_barplot(ifelse(as.numeric(dat_sum[2,])>25000, 25100, as.numeric(dat_sum[2,])),
height = unit(2,"cm"),
ylim = range(0,25000),
gp = gpar(fill = ifelse(as.numeric(dat_sum[2,]) > 25000, "white", "black"),
col = "white")),
"Numbers" = anno_text(round(as.numeric(dat_sum[2,])),
which = "column",
rot = 90,
just = "center",
location = 0.5,
gp = gpar(fontsize=8,col = ifelse(as.numeric(dat_sum[2,]) > 25000, "red", "black"))))
# row_split for markers
rowData(sce_prot)$heatmap_relevance <- ""
rowData(sce_prot[rowData(sce_prot)$good_marker,])$heatmap_relevance <- "lineage"
rowData(sce_prot[grepl("PDL1|CD11b|CD206|PARP|CXCR2|CD11c|pS6|GrzB|IDO1|CD45RA|H3K27me3|TCF7|CD45RO|PD1|pERK|ICOS|Ki67", rownames(sce_prot)),])$heatmap_relevance <- "other"
# plot heatmap
h <- Heatmap(m, name = "Scaled Expression",
row_split = rowData(sce_prot[rowData(sce_prot)$good_marker,])$heatmap_relevance,
cluster_columns = FALSE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=12),
column_names_rot = 90,
column_names_centered = TRUE,
show_column_names = TRUE,
top_annotation = ha,
col = colorRamp2(c(-3, 0, 3), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-3:3),legend_width = unit(6,"cm"), direction="horizontal"),
column_names_side = "top",
height = unit(20, "cm"),
width = unit(20,"cm"))
draw(h, heatmap_legend_side = "bottom")tumor_marker_protein <- c("pS6", "bCatenin", "H3K27me3", "HLADR", "Sox9", "pERK", "p75", "PDL1", "Ki67", "SOX10", "PARP")
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", "dysfunction_score", "Description", "MM_location")])Joining, by = "cellID"# 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")])Joining, by = "cellID"# 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",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(x = "celltype", dodge = 0.8)
# plot
p <- 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=2, aes(col=dysfunction_score)) +
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", ncol = 3) +
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, 0.2))) +
guides(fill=guide_legend(title="Dysfunction Score", override.aes = c(lwd=0.5, alpha=1)))
leg <- get_legend(p)
grid.arrange(p + theme(legend.position = "none"))grid.arrange(leg)y <- c(rep(1:10,16),rep(11,7))
# add the group information to the sce object
sce_rna$groups <- y[colData(sce_rna)$ImageNumber]
# now we use the function written by Nils
plotDist(sce_rna["S100", sce_rna$celltype == "Tumor"], plot_type = "ridges",
colour_by = "groups", split_by = "rows",
exprs_values = "asinh") +
geom_vline(xintercept = 3)# manual gating
sce_rna$S100 <- ifelse(assay(sce_rna["S100",], "asinh") > 3, "positive", "negative")
# fraction of S100 tumor cells per image
s100 <- data.frame(colData(sce_rna)) %>%
filter(celltype == "Tumor") %>%
group_by(ImageNumber, dysfunction_score, S100) %>%
summarise(n=n()) %>%
mutate(fraction = n/sum(n)) %>%
filter(is.na(dysfunction_score) == F & S100 == "positive")`summarise()` regrouping output by 'ImageNumber', 'dysfunction_score' (override with `.groups` argument)s100$dysfunction_score <- factor(s100$dysfunction_score)
stat.test <- s100 %>%
group_by(S100) %>%
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(dodge = 0.8)
ggplot(s100, aes(x=dysfunction_score, y=fraction)) +
geom_boxplot(alpha=0.2, lwd=1.5, aes(fill = dysfunction_score)) +
geom_quasirandom(aes(col=dysfunction_score), size=3) +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7, y.position = 1) +
xlab("") +
ylab("Fraction of S100+ Tumor Cells") +
theme_bw() +
theme(text = element_text(size=16),
legend.position = "none") +
ylim(0,1.05)cur_dt <- data.frame(colData(sce_rna))
clust <- data.frame()
## wide table for communities
for(i in names(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))])) {
cur_dt_sub <- cur_dt[cur_dt[,i] > 0,]
cur_dt_sub <- cbind(cur_dt_sub[,c(i, "Description")],
cur_dt_sub[,grepl(glob2rx("C*L*"),names(cur_dt_sub))])
# count numbers of chemokine-expressing cells per patch
cur_dt_sub <- cur_dt_sub %>%
group_by(Description) %>%
group_by_at(i, .add = TRUE) %>%
summarise_each(funs(sum))
cur_dt_sub$cluster_type <- i
cur_dt_sub <- cur_dt_sub[,-2]
clust <- rbind(clust, cur_dt_sub)
}Warning: `summarise_each_()` is deprecated as of dplyr 0.7.0.
Please use `across()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# remove pure clusters with low abundance (CCL22, CCL4, CCL8)
clust <- clust[!(clust$cluster_type %in% c("ccl22_pure", "ccl4_pure", "ccl8_pure")),]
# number of patches by image
clust <- clust %>%
group_by(Description, cluster_type) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ cluster_type, value.var = "n", fill = 0)`summarise()` regrouping output by 'Description' (override with `.groups` argument)# add images with 0 clusters and add more information
to_add <- data.frame(colData(sce_rna)) %>%
distinct(Description, .keep_all = TRUE)
clust <- left_join(to_add[,c("Description", "dysfunction_score", "Tcell_density_score_image")], clust)Joining, by = "Description"# repalce NA with 0
cur_dt_wide <- clust %>%
mutate_if(is.numeric,coalesce,0)
# order according to image infiltration score
cur_dt_wide <- cur_dt_wide[order(cur_dt_wide$dysfunction_score),]
# chemokines per image
total_chemokines <- cur_dt %>%
group_by(Description, chemokine) %>%
summarise(n=n()) %>%
group_by(Description) %>%
mutate(fraction = n/sum(n)) %>%
filter(chemokine == TRUE)`summarise()` regrouping output by 'Description' (override with `.groups` argument)# is a expressing cell part of a milieu?
cur_dt$in_community <- ifelse(rowSums(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))]) > 0 & cur_dt$chemokine == TRUE, TRUE, FALSE)
# fraction in_community 1vs.0 per image
fractions <- cur_dt %>%
filter(chemokine == TRUE) %>%
group_by(Description, in_community) %>%
summarise(n=n()) %>%
group_by(Description) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(Description ~ in_community, value.var = "fraction", fill = 0)`summarise()` regrouping output by 'Description' (override with `.groups` argument)names(fractions)[2:3] <- c("single", "community")
fractions[, 2:3][is.na(fractions[, 2:3])] <- 0
# chemokines per image (regardless of combination, multi-producing cells count more than once)
chemokines <- cbind(cur_dt[,c("Description", "in_community")], cur_dt[,grepl(glob2rx("C*L*"),names(cur_dt))])
# long table - chemokine / in_community info and count (n) per image
chemokines <- reshape2::melt(chemokines, id.vars = c("Description", "in_community"), variable.name = "chemokine", value.name = "n") %>%
group_by(Description, in_community, chemokine) %>%
summarise(total = sum(n)) %>%
reshape2::dcast(Description + chemokine ~ in_community, value.var = "total") %>%
replace(is.na(.), 0) %>%
reshape2::melt(id.vars = c("Description", "chemokine"), variable.name = "in_community", value.name = "n")`summarise()` regrouping output by 'Description', 'in_community' (override with `.groups` argument)# combine all information
cur_dt_wide <- left_join(cur_dt_wide, fractions)Joining, by = "Description"cur_dt_wide <- left_join(cur_dt_wide, total_chemokines)Joining, by = "Description"# remove controls and only keep images with dysfunction score
cur_dt_wide_sub <- cur_dt_wide[cur_dt_wide$Description %in% unique(sce_rna[,sce_rna$Location != "CTRL"]$Description),]
cur_dt_wide_sub <- cur_dt_wide[cur_dt_wide$dysfunction_score %in% c("high dysfunction", "low dysfunction"),]
# define subgroups to split heatmap
subgroup = cur_dt_wide_sub[,"dysfunction_score"]
# heatmap annotation
row_ha2 = rowAnnotation("Production Mode of\nChemokine-Expressing Cells" =
anno_barplot(cur_dt_wide_sub[,c("single", "community")],
gp = gpar(fill = c("#F8766D", "#00BFC4")), width = unit(1.5, "cm")),
"Fraction of Chemokine- \n Expressing Cells" =
anno_barplot(cur_dt_wide_sub[,"fraction"],
width = unit(1.5, "cm")),
annotation_name_rot = 90, gap = unit(3, "mm"),
col = list(Relapse = c("no relapse" = "orange", "relapse" = "black", "untreated/lost" = "grey")))
# function for the zoom-in plot
panel_fun_chemokines = function(index, nm) {
image_number = cur_dt_wide_sub[index,"Description"]
if(length(unique(image_number)) > 9){
df = chemokines[chemokines$Description %in% image_number, ]
g = ggplot(df, aes(x = factor(chemokine), y = log10(n+1), fill=in_community)) +
geom_boxplot() +
xlab("Chemokine") +
ylab("# Cells [log10(n+1)]") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position = "none") +
ylim(0,3)
g = grid.grabExpr(print(g))
pushViewport(viewport())
grid.rect()
grid.draw(g)
popViewport()
}
}
# create zoom-in
zoom = anno_zoom(align_to = subgroup,
which = "row", panel_fun = panel_fun_chemokines,
size = unit(6, "cm"),
gap = unit(1, "cm"),
width = unit(10, "cm"))
# heatmap
m <- as.matrix(cur_dt_wide_sub[,grepl(glob2rx("*pure"),names(cur_dt_wide_sub))])
col_names <- c()
for(i in (1:length(colnames(m)))){col_names <- c(col_names,(toupper(str_split(colnames(m), "_")[[i]][1])))}
colnames(m) <- col_names
col_fun = viridis::inferno(max(m)+1)
ht1 = Heatmap(m,
col = col_fun,
left_annotation = row_ha2,
right_annotation = rowAnnotation(foo = zoom, gap = unit(3,"cm")),
row_split = subgroup,
row_title_side = "left",
border = T,
row_gap = unit(3, "mm"),
cluster_rows = T,
cluster_columns = F,
cluster_row_slices = F,
show_heatmap_legend = F,
show_row_dend = F,
name = "Detected Patches",
column_title = "Chemokine Milieu",
column_title_side = "bottom",
column_title_gp = gpar(fontsize=20),
show_row_names = F,
width = unit(15,"cm"))
draw(ht1)Warning: Removed 1 rows containing non-finite values (stat_boxplot).# manual legends
lgd1 = Legend(labels = c("Stand-alone", "Milieu"), title = "Production Mode", legend_gp = gpar(fill = c("#F8766D", "#00BFC4")))
lgd2 = Legend(col_fun = colorRamp2(c(0:max(m)), colors = col_fun),
at = seq(0, max(m)+2, by=5), title = "Detected Milieus", direction = "horizontal", grid_width = unit(2, "cm"))
# Draw Legend
draw(packLegend(lgd2, lgd1, direction = "horizontal"))
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 gridExtra_2.3
[3] ggridges_0.5.3 ggpubr_0.4.0
[5] circlize_0.4.12 ggbeeswarm_0.6.0
[7] ggrepel_0.9.0 rms_6.1-0
[9] SparseM_1.78 Hmisc_4.4-2
[11] Formula_1.2-4 survival_3.2-7
[13] lattice_0.20-41 ComplexHeatmap_2.4.3
[15] data.table_1.13.6 forcats_0.5.0
[17] stringr_1.4.0 dplyr_1.0.2
[19] purrr_0.3.4 readr_1.4.0
[21] tidyr_1.1.2 tibble_3.0.4
[23] ggplot2_3.3.3 tidyverse_1.3.0
[25] reshape2_1.4.4 SingleCellExperiment_1.12.0
[27] SummarizedExperiment_1.20.0 Biobase_2.50.0
[29] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[31] IRanges_2.24.1 S4Vectors_0.28.1
[33] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[35] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6
[4] splines_4.0.3 TH.data_1.0-10 digest_0.6.27
[7] htmltools_0.5.0 viridis_0.5.1 fansi_0.4.1
[10] magrittr_2.0.1 checkmate_2.0.0 cluster_2.1.0
[13] openxlsx_4.2.3 modelr_0.1.8 sandwich_3.0-0
[16] jpeg_0.1-8.1 colorspace_2.0-0 rvest_0.3.6
[19] haven_2.3.1 xfun_0.20 crayon_1.3.4
[22] RCurl_1.98-1.2 jsonlite_1.7.2 zoo_1.8-8
[25] glue_1.4.2 gtable_0.3.0 zlibbioc_1.36.0
[28] XVector_0.30.0 MatrixModels_0.4-1 GetoptLong_1.0.5
[31] DelayedArray_0.16.0 car_3.0-10 shape_1.4.5
[34] abind_1.4-5 scales_1.1.1 mvtnorm_1.1-1
[37] DBI_1.1.0 Rcpp_1.0.5 viridisLite_0.3.0
[40] htmlTable_2.1.0 clue_0.3-58 foreign_0.8-81
[43] htmlwidgets_1.5.3 httr_1.4.2 RColorBrewer_1.1-2
[46] ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3
[49] nnet_7.3-14 dbplyr_2.0.0 labeling_0.4.2
[52] tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
[55] munsell_0.5.0 cellranger_1.1.0 tools_4.0.3
[58] cli_2.2.0 generics_0.1.0 broom_0.7.3
[61] evaluate_0.14 yaml_2.2.1 knitr_1.30
[64] fs_1.5.0 zip_2.1.1 nlme_3.1-151
[67] whisker_0.4 quantreg_5.82 xml2_1.3.2
[70] compiler_4.0.3 rstudioapi_0.13 beeswarm_0.2.3
[73] curl_4.3 png_0.1-7 ggsignif_0.6.0
[76] reprex_0.3.0 stringi_1.5.3 Matrix_1.3-2
[79] vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0
[82] GlobalOptions_0.1.2 bitops_1.0-6 conquer_1.0.2
[85] httpuv_1.5.4 R6_2.5.0 latticeExtra_0.6-29
[88] promises_1.1.1 rio_0.5.16 vipor_0.4.5
[91] codetools_0.2-18 polspline_1.1.19 MASS_7.3-53
[94] assertthat_0.2.1 rprojroot_2.0.2 rjson_0.2.20
[97] withr_2.3.0 multcomp_1.4-15 GenomeInfoDbData_1.2.4
[100] hms_0.5.3 rpart_4.1-15 rmarkdown_2.6
[103] carData_3.0-4 git2r_0.28.0 lubridate_1.7.9.2
[106] base64enc_0.1-3