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 3.
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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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(dplyr)
library(ggplot2)
library(ggbeeswarm)
library(tidyr)
library(scater)
library(dittoSeq)
library(gridExtra)
library(cowplot)
library(data.table)
library(ggpmisc)
library(ggpubr)
library(rstatix)# SCE object
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
# 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)
targets <- metadata(sce_rna)$chemokines_morethan600_withcontroltargets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
frac <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(Description, Tcell_density_score_image, expressor) %>%
summarise(n=n()) %>%
mutate(fraction = n / sum(n)) %>%
filter(expressor %in% targets) %>%
reshape2::dcast(Description + Tcell_density_score_image ~ expressor, value.var = "fraction", fill = 0) %>%
reshape2::melt(id.vars = c("Description", "Tcell_density_score_image"), variable.name = "expressor", value.name = "fraction")
stat.test <- frac %>%
group_by(expressor) %>%
kruskal_test(data = ., fraction ~ Tcell_density_score_image) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
arrange(p.adj) %>%
mutate(group1 = expressor, group2 = expressor) %>%
add_x_position()
frac$expressor <- factor(frac$expressor, levels = stat.test$expressor)
ggplot(frac, aes(x=expressor, y = fraction)) +
geom_boxplot(alpha=.75, outlier.size = 0.5, aes(fill = Tcell_density_score_image)) +
stat_pvalue_manual(x = "group1", y.position = 0.055, stat.test, size = 4) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size = 15),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="T cell Score")) +
xlab("") +
ylab("Fractions") +
coord_cartesian(ylim = c(0,0.06))tumor_marker_protein <- c("bCatenin", "Sox9", "pERK", "p75", "Ki67", "SOX10", "PARP", "S100", "MiTF")
tumor_marker_rna <- c("Mart1", "pRB")
# rna data
dat_rna <- data.frame(t(assay(sce_rna[tumor_marker_rna, sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "Tcell_density_score_image", "Description", "MM_location", "Location")])
# filter
dat_rna <- dat_rna %>%
filter(Location != "CTRL")
# mean per image
dat_rna <- dat_rna %>%
select(-cellID) %>%
group_by(Description, Tcell_density_score_image) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_rna <- dat_rna %>%
reshape2::melt(id.vars = c("Description", "Tcell_density_score_image"), variable.name = "channel", value.name = "asinh")
# protein data
dat_prot <- data.frame(t(assay(sce_prot[tumor_marker_protein,, sce_prot$celltype == "Tumor"], "asinh")))
dat_prot$cellID <- rownames(dat_prot)
dat_prot <- left_join(dat_prot, data.frame(colData(sce_prot))[,c("cellID", "Tcell_density_score_image", "Description", "MM_location", "Location")])
# filter
dat_prot <- dat_prot %>%
filter(Location != "CTRL")
# mean per image
dat_prot <- dat_prot %>%
select(-cellID) %>%
group_by(Description, Tcell_density_score_image) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
# melt
dat_prot <- dat_prot %>%
reshape2::melt(id.vars = c("Description", "Tcell_density_score_image"), variable.name = "channel", value.name = "asinh")
# join both data sets
comb <- rbind(dat_prot, dat_rna)
# adjusted wilcox.test for groups
group_comparison <- list(c("absent", "high"), c("med", "high"))
stat.test <- comb %>%
group_by(channel) %>%
wilcox_test(data = ., asinh ~ Tcell_density_score_image) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
add_xy_position(x = "Tcell_density_score_image", dodge = 0.8, comparisons = group_comparison) %>%
filter(is.na(y.position) == FALSE)
# plot
p <- ggplot(comb, aes(x=Tcell_density_score_image, y=asinh)) +
geom_boxplot(alpha=0.2, lwd=1, aes(fill=Tcell_density_score_image)) +
geom_quasirandom(alpha=0.6, size=2, aes(col=Tcell_density_score_image)) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size=18),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank()) +
facet_wrap(~channel, scales = "free") +
stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) +
xlab("") +
ylab("Mean Count per Image (asinh)") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) +
guides(fill=guide_legend(title="T cell Score", override.aes = c(lwd=0.5, alpha=1)))
leg <- get_legend(p)
grid.arrange(p + theme(legend.position = "none"))grid.arrange(leg)# prepare data and add cellID
dat_relation_rna$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$`Second Object Number`, sep = "_"), sep = "_")
# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_rna))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_rna))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")
dat_relation_rna <- left_join(dat_relation_rna, celltype_first, by = "cellID_first")
dat_relation_rna <- left_join(dat_relation_rna, celltype_second, by = "cellID_second")
colnames(dat_relation_rna)[5] <- "FirstImageNumber"# number of interactions CD8+ T cell and tumor per image
dat_relation_sub <- dat_relation_rna[dat_relation_rna$celltype_first %in% c("CD8+ T cell") &
dat_relation_rna$celltype_second == "Tumor"]
# count number of interactions
count <- dat_relation_sub %>%
group_by(FirstImageNumber) %>%
summarise(n=n())
names(count)[1] <- "ImageNumber"
# fraction of exhausted cd8 per image
expressor_frac <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, expressor) %>%
summarise(n=n()) %>%
mutate(fraction=n/sum(n)) %>%
filter(expressor %in% targets) %>%
reshape2::dcast(ImageNumber ~ expressor, value.var = "fraction", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber"), variable.name = "expressor", value.name = "fraction")
# correlation plot
cur_dat <- left_join(expressor_frac, count)
cur_dat[is.na(cur_dat$n),]$n <- 0
ggplot(cur_dat, aes(x=log10(fraction), y=log10(n))) +
geom_point(size=2) +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..rr.label.. ,")")),
size = 3, cor.coef.name = "R", label.sep="\n", label.x = -1.875, label.y = 0.3) +
theme_bw() +
theme(text = element_text(size=15)) +
xlab("Chemokine Fraction (log10)") +
ylab("Number of CD8/Tumor Interactions (log10)") +
facet_wrap(~expressor)Warning: Removed 684 rows containing non-finite values (stat_smooth).Warning: Removed 684 rows containing non-finite values (stat_cor).
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rstatix_0.6.0 ggpubr_0.4.0
[3] ggpmisc_0.3.7 data.table_1.13.6
[5] cowplot_1.1.1 gridExtra_2.3
[7] dittoSeq_1.0.2 scater_1.16.2
[9] tidyr_1.1.2 ggbeeswarm_0.6.0
[11] ggplot2_3.3.3 dplyr_1.0.2
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0 GenomicRanges_1.42.0
[17] GenomeInfoDb_1.26.2 IRanges_2.24.1
[19] S4Vectors_0.28.1 BiocGenerics_0.36.0
[21] MatrixGenerics_1.2.0 matrixStats_0.57.0
[23] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ggsignif_0.6.0
[3] ellipsis_0.3.1 rio_0.5.16
[5] ggridges_0.5.3 rprojroot_2.0.2
[7] XVector_0.30.0 BiocNeighbors_1.6.0
[9] fs_1.5.0 rstudioapi_0.13
[11] farver_2.0.3 ggrepel_0.9.0
[13] splines_4.0.3 knitr_1.30
[15] broom_0.7.3 pheatmap_1.0.12
[17] compiler_4.0.3 backports_1.2.1
[19] Matrix_1.3-2 limma_3.44.3
[21] later_1.1.0.1 BiocSingular_1.4.0
[23] htmltools_0.5.0 tools_4.0.3
[25] rsvd_1.0.3 gtable_0.3.0
[27] glue_1.4.2 GenomeInfoDbData_1.2.4
[29] reshape2_1.4.4 Rcpp_1.0.5
[31] carData_3.0-4 cellranger_1.1.0
[33] vctrs_0.3.6 nlme_3.1-151
[35] DelayedMatrixStats_1.10.1 xfun_0.20
[37] stringr_1.4.0 openxlsx_4.2.3
[39] lifecycle_0.2.0 irlba_2.3.3
[41] edgeR_3.30.3 zlibbioc_1.36.0
[43] scales_1.1.1 hms_0.5.3
[45] promises_1.1.1 RColorBrewer_1.1-2
[47] yaml_2.2.1 curl_4.3
[49] stringi_1.5.3 zip_2.1.1
[51] BiocParallel_1.22.0 rlang_0.4.10
[53] pkgconfig_2.0.3 bitops_1.0-6
[55] evaluate_0.14 lattice_0.20-41
[57] purrr_0.3.4 labeling_0.4.2
[59] tidyselect_1.1.0 plyr_1.8.6
[61] magrittr_2.0.1 R6_2.5.0
[63] generics_0.1.0 DelayedArray_0.16.0
[65] pillar_1.4.7 haven_2.3.1
[67] whisker_0.4 foreign_0.8-81
[69] withr_2.3.0 mgcv_1.8-33
[71] abind_1.4-5 RCurl_1.98-1.2
[73] tibble_3.0.4 crayon_1.3.4
[75] car_3.0-10 rmarkdown_2.6
[77] viridis_0.5.1 locfit_1.5-9.4
[79] grid_4.0.3 readxl_1.3.1
[81] git2r_0.28.0 forcats_0.5.0
[83] digest_0.6.27 httpuv_1.5.4
[85] munsell_0.5.0 beeswarm_0.2.3
[87] viridisLite_0.3.0 vipor_0.4.5