Last updated: 2021-02-10
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Knit directory: melanoma_publication_old_data/
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| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
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This script generates plots for Supplementary Figure 2.
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
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code/helper_functions/findCommunity.R
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code/helper_functions/getInfoFromString.R
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code/helper_functions/plotBarFracCluster.R
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code/helper_functions/sceChecks.R
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code/helper_functions/validityChecks.R
value ?
visible FALSE library(data.table)
library(survival)
library(ggplot2)
library(broom)
library(dplyr)
library(RColorBrewer)
library(ggalluvial)
library(tidyverse)
library(cowplot)
library(ggbeeswarm)
library(gridExtra)
library(SingleCellExperiment)
library(scater)
library(cba)
library(ComplexHeatmap)
library(reshape2)
library(rms)
library(ggrepel)
library(circlize)
library(coxme)
library(rstatix)
library(ggpubr)# clinical data
dat <- read_csv("data/protein/clinical_data_protein.csv")
dat_survival = fread(file = "data/survdat_for_modelling.csv",stringsAsFactors = FALSE)
# SCE object
sce_prot = readRDS(file = "data/sce_protein.rds")
sce_rna = readRDS(file = "data/sce_RNA.rds")
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrolNote: as the cohort is very diverse, we are using the BlockID as the minimal unit since clinical parameters are described per BlockID. However, sometimes we do have patients of which we have multiple FFPE blocks (BlockIDs). Nonetheless, clinical parameters are not given per patient but per patient FFPE block and are therefore considered the minimial unit.
dat[dat$BlockID %in% unique(sce_prot[,sce_prot$Location == "CTRL"]$BlockID),]$MM_location <- "Control"
# remove control samples
dat <- dat[dat$BlockID %in% unique(sce_prot[,sce_prot$Location != "CTRL"]$BlockID),]
p1 <- unique(dat[,c("BlockID","MM_location")]) %>%
ggplot()+
geom_bar(aes(y=MM_location),stat ="count") +
xlab("BlockIDs per Location") +
ylab("Metastasis Location") +
theme_bw()+
theme(text = element_text(size=16))
p2 <- dat %>%
ggplot()+
geom_bar(aes(x=BlockID, fill=(MM_location)),stat="count")+
theme_bw()+
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)) +
ylab("Number of Samples") +
guides(fill=guide_legend(title="Metasis Location")) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=7))
plot_grid(p1,p2,rel_widths = c(1.25,3)) sce_rna$MM_location <- ifelse(sce_rna$MM_location %in% c("skin", "skin_undefine"), "skin_undefined", sce_rna$MM_location)
groups <- data.frame(colData(sce_rna)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(MM_location) %>%
distinct(PatientID, .keep_all = T) %>%
summarise(n=n()) %>%
filter(n>=10) %>%
arrange(-n)fractions_per_image <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, MM_location, expressor, celltype) %>%
summarise(n = n()) %>%
group_by(ImageNumber) %>%
mutate(fraction_per_image = n / sum(n)) %>%
group_by(ImageNumber, expressor) %>%
mutate(group_fraction = sum(fraction_per_image)) %>%
ungroup() %>%
filter(expressor %in% targets & MM_location %in% groups$MM_location)
# fraction of expressor cells per image
fraction_expressor_per_image <- fractions_per_image %>%
distinct(ImageNumber, MM_location, expressor, .keep_all = T) %>%
reshape2::dcast(ImageNumber + MM_location ~ expressor, value.var = "group_fraction", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location"), variable.name = "expressor",
value.name = "fraction_per_image")
# fraction of celltype expressing a certain combi per image
celltype_fractions <- fractions_per_image %>%
distinct(ImageNumber, celltype, expressor, .keep_all = T) %>%
reshape2::dcast(ImageNumber + MM_location + expressor ~ celltype, value.var = "fraction_per_image", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location", "expressor"),
variable.name = "celltype", value.name = "fraction_per_image") %>%
reshape2::dcast(ImageNumber + MM_location + celltype ~ expressor, value.var = "fraction_per_image", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location", "celltype"),
variable.name = "expressor", value.name = "fraction_per_image") %>%
group_by(MM_location, expressor, celltype) %>%
summarise(sum_fraction = sum(fraction_per_image)) %>% # sum-up fractions over all images
group_by(MM_location, expressor) %>%
mutate(proportions = sum_fraction / sum(sum_fraction)) # calculate proportions for each expressor
#median_celltype_fraction <- fractions_per_image %>%
#distinct(ImageNumber, celltype, expressor, .keep_all = T) %>%
#reshape2::dcast(ImageNumber + MM_location + expressor ~ celltype, value.var = "fraction_per_image", fill = 0) %>%
#reshape2::melt(id.vars = c("ImageNumber", "MM_location", "expressor"),
#variable.name = "celltype", value.name = "fraction_per_image") %>%
#reshape2::dcast(ImageNumber + MM_location + celltype ~ expressor, value.var = "fraction_per_image", fill = 0) %>%
#reshape2::melt(id.vars = c("ImageNumber", "MM_location", "celltype"),
#variable.name = "expressor", value.name = "fraction_per_image") %>%
#group_by(MM_location, expressor, celltype) %>%
#summarise(median_fraction = median(fraction_per_image)) %>%
#group_by(MM_location, expressor) %>%
#mutate(proportions = median_fraction / sum(median_fraction))plot_list <- list()
for(i in groups$MM_location) {
a <- fraction_expressor_per_image %>%
filter(MM_location == i) %>%
group_by(ImageNumber, expressor) %>%
ggplot(., aes(y=expressor, x=fraction_per_image)) +
geom_boxplot() +
geom_point(alpha=0.2) +
theme_bw() +
theme(axis.title.y = element_blank(),
axis.text.y = element_text(hjust=0.5)) +
#scale_x_log10() +
#annotation_logticks(sides = "bottom") +
xlab("Cell Fraction per Image") +
coord_cartesian(xlim = c(0,0.05))
b <- celltype_fractions %>%
filter(MM_location == i) %>%
ggplot(., aes(y=expressor, x=-proportions, fill=celltype)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank(),
legend.position = "none") +
xlab("Producing Cell Types") +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype),
breaks = names(metadata(sce_rna)$colour_vectors$celltype),
labels = names(metadata(sce_rna)$colour_vectors$celltype)) +
scale_x_continuous(breaks=c(-1.00,-0.75,-0.5, -0.25, 0.00),
labels=c("100%", "75%", "50%", "25%", "0%"))
grid.arrange(b,a,nrow=1,
widths = c(.75,1),
top = i)
}# add control location to sce
sce_rna$MM_location_simplified2 <- sce_rna$MM_location_simplified
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "Skin"]$MM_location_simplified2 <- "control skin"
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "Lymphnode"]$MM_location_simplified2 <- "control LN"
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "PSO"]$MM_location_simplified2 <- "control psoriasis"
frac <- data.frame(colData(sce_rna)) %>%
filter(MM_location_simplified2 != "control psoriasis") %>%
group_by(Description, MM_location_simplified2, expressor) %>%
summarise(n=n()) %>%
mutate(fraction = n / sum(n)) %>%
filter(expressor %in% targets) %>%
reshape2::dcast(Description + MM_location_simplified2 ~ expressor, value.var = "fraction", fill = 0) %>%
reshape2::melt(id.vars = c("Description", "MM_location_simplified2"), variable.name = "expressor", value.name = "fraction")
ggplot(frac, aes(x=expressor, y = fraction, fill = MM_location_simplified2)) +
geom_boxplot(alpha=1, outlier.size = 0.5) +
#geom_jitter(size = 0.75, alpha=0.6, position = position_jitterdodge(dodge.width = 0.75,jitter.width = 0.05), aes(col=MM_location_simplified2)) +
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="Met Location", override.aes = aes(lwd=0.5))) +
xlab("") +
ylab("Fractions") +
coord_cartesian(ylim = c(0,0.075))targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
# subset sce_rna
group_size <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, Mutation) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(Mutation) %>%
summarise(n=n()) %>%
filter(n>10 & Mutation != "")
sce_rna_sub <- sce_rna[,sce_rna$Mutation %in% group_size$Mutation]
a <- plotCellCounts(sce = sce_rna_sub,
sce_sub = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets)],
cellID = "cellID",
colour_by = "expressor",
split_by = "Mutation",
imageID = "ImageNumber",
normalize = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$chemokine_combinations) +
guides(fill=guide_legend("Chemokine")) +
theme(text = element_text(size=16))
b <- plotCellCounts(sce = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets)],
cellID = "cellID",
colour_by = "celltype",
split_by = "Mutation",
imageID = "ImageNumber",
proportion = TRUE,
show_n = FALSE,
colour_vector = metadata(sce_rna)$colour_vectors$celltype) +
guides(fill=guide_legend("Cell Type")) +
theme(text = element_text(size=16))
# fraction of chemokine-expressing cells per image
t <- data.frame(colData(sce_rna_sub)) %>%
group_by(ImageNumber, Mutation, chemokine, MM_location_simplified) %>%
summarise(n=n()) %>%
group_by(ImageNumber) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(ImageNumber + Mutation + MM_location_simplified ~ chemokine, value.var = "fraction")
median_expression <- median(t$`TRUE`)
stat.test <- t %>%
group_by(Mutation) %>%
t_test(data = ., mu = median_expression, `TRUE` ~ 1, alternative = "greater") %>%
adjust_pvalue(method="BH") %>%
add_significance() %>%
add_x_position(x = "Mutation", dodge = 0.8)
c <- ggplot(t, aes(x=Mutation, y=`TRUE`)) +
geom_boxplot(alpha=0.2, lwd=1.5) +
geom_quasirandom(aes(col=MM_location_simplified), size=2, alpha=.8) +
geom_hline(yintercept = median_expression, linetype=2, size=2) +
stat_pvalue_manual(stat.test, x = "x", label = "p.adj.signif", size = 7, y.position = 0.3) +
xlab("") +
ylab("Fraction of Chemokine-Expressing Cells") +
theme_bw() +
theme(text = element_text(size=16),
axis.text.x = element_text(angle=45, hjust=1, vjust=1)) +
guides(col=guide_legend("Location")) +
coord_cartesian(ylim=c(0,0.3))
grid.arrange(a,b,c,nrow=1,
widths = c(1,1,1))# MM_location for Mutations
data.frame(colData(sce_rna_sub)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(Mutation, MM_location_simplified) %>%
summarise(n=n()) %>%
group_by(Mutation) %>%
mutate(percentage = n / sum(n) * 100)# A tibble: 9 x 4
# Groups: Mutation [3]
Mutation MM_location_simplified n percentage
<chr> <chr> <int> <dbl>
1 BRAF LN 30 42.3
2 BRAF other 10 14.1
3 BRAF skin 31 43.7
4 NRAS LN 16 32
5 NRAS other 10 20
6 NRAS skin 24 48
7 wt LN 6 21.4
8 wt other 5 17.9
9 wt skin 17 60.7
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] ggpubr_0.4.0 rstatix_0.6.0
[3] coxme_2.2-16 bdsmatrix_1.3-4
[5] circlize_0.4.12 ggrepel_0.9.0
[7] rms_6.1-0 SparseM_1.78
[9] Hmisc_4.4-2 Formula_1.2-4
[11] lattice_0.20-41 reshape2_1.4.4
[13] ComplexHeatmap_2.4.3 cba_0.2-21
[15] proxy_0.4-24 scater_1.16.2
[17] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[19] Biobase_2.50.0 GenomicRanges_1.42.0
[21] GenomeInfoDb_1.26.2 IRanges_2.24.1
[23] S4Vectors_0.28.1 BiocGenerics_0.36.0
[25] MatrixGenerics_1.2.0 matrixStats_0.57.0
[27] gridExtra_2.3 ggbeeswarm_0.6.0
[29] cowplot_1.1.1 forcats_0.5.0
[31] stringr_1.4.0 purrr_0.3.4
[33] readr_1.4.0 tidyr_1.1.2
[35] tibble_3.0.4 tidyverse_1.3.0
[37] ggalluvial_0.12.3 RColorBrewer_1.1-2
[39] dplyr_1.0.2 broom_0.7.3
[41] ggplot2_3.3.3 survival_3.2-7
[43] data.table_1.13.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1
[3] plyr_1.8.6 splines_4.0.3
[5] BiocParallel_1.22.0 TH.data_1.0-10
[7] digest_0.6.27 htmltools_0.5.0
[9] viridis_0.5.1 fansi_0.4.1
[11] magrittr_2.0.1 checkmate_2.0.0
[13] cluster_2.1.0 openxlsx_4.2.3
[15] modelr_0.1.8 sandwich_3.0-0
[17] jpeg_0.1-8.1 colorspace_2.0-0
[19] rvest_0.3.6 haven_2.3.1
[21] xfun_0.20 crayon_1.3.4
[23] RCurl_1.98-1.2 jsonlite_1.7.2
[25] zoo_1.8-8 glue_1.4.2
[27] gtable_0.3.0 zlibbioc_1.36.0
[29] XVector_0.30.0 MatrixModels_0.4-1
[31] GetoptLong_1.0.5 DelayedArray_0.16.0
[33] car_3.0-10 BiocSingular_1.4.0
[35] shape_1.4.5 abind_1.4-5
[37] scales_1.1.1 mvtnorm_1.1-1
[39] DBI_1.1.0 Rcpp_1.0.5
[41] viridisLite_0.3.0 htmlTable_2.1.0
[43] clue_0.3-58 foreign_0.8-81
[45] rsvd_1.0.3 htmlwidgets_1.5.3
[47] httr_1.4.2 ellipsis_0.3.1
[49] farver_2.0.3 pkgconfig_2.0.3
[51] nnet_7.3-14 dbplyr_2.0.0
[53] utf8_1.1.4 labeling_0.4.2
[55] tidyselect_1.1.0 rlang_0.4.10
[57] later_1.1.0.1 munsell_0.5.0
[59] cellranger_1.1.0 tools_4.0.3
[61] cli_2.2.0 generics_0.1.0
[63] evaluate_0.14 yaml_2.2.1
[65] knitr_1.30 fs_1.5.0
[67] zip_2.1.1 nlme_3.1-151
[69] whisker_0.4 quantreg_5.82
[71] xml2_1.3.2 compiler_4.0.3
[73] rstudioapi_0.13 curl_4.3
[75] beeswarm_0.2.3 png_0.1-7
[77] ggsignif_0.6.0 reprex_0.3.0
[79] stringi_1.5.3 Matrix_1.3-2
[81] vctrs_0.3.6 pillar_1.4.7
[83] lifecycle_0.2.0 GlobalOptions_0.1.2
[85] BiocNeighbors_1.6.0 bitops_1.0-6
[87] irlba_2.3.3 conquer_1.0.2
[89] httpuv_1.5.4 R6_2.5.0
[91] latticeExtra_0.6-29 promises_1.1.1
[93] rio_0.5.16 vipor_0.4.5
[95] codetools_0.2-18 polspline_1.1.19
[97] MASS_7.3-53 assertthat_0.2.1
[99] rprojroot_2.0.2 rjson_0.2.20
[101] withr_2.3.0 multcomp_1.4-15
[103] GenomeInfoDbData_1.2.4 hms_0.5.3
[105] rpart_4.1-15 rmarkdown_2.6
[107] DelayedMatrixStats_1.10.1 carData_3.0-4
[109] git2r_0.28.0 lubridate_1.7.9.2
[111] base64enc_0.1-3