Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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This script generates plots for Supplementary Figure 9.
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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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 code/helper_functions/read_Data.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(ComplexHeatmap)
library(rstatix)
library(dendextend)
library(parallel)
library(neighbouRhood)
library(unix)# SCE object
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"]
# 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)$chemokines_morethan600_withcontrol# prepare data
dat_relation$cellID_first <- paste("RNA", paste(dat_relation$`First Image Number`, dat_relation$`First Object Number`, sep = "_"), sep = "_")
dat_relation$cellID_second <- paste("RNA", paste(dat_relation$`Second Image Number`, dat_relation$`Second Object Number`, sep = "_"), sep = "_")targets <- metadata(sce_rna)$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$expressor)
ggplot(frac, aes(x=expressor, y = fraction)) +
geom_boxplot(alpha=.75, outlier.size = 0.5, aes(fill = Tcell_density_score_image)) +
stat_pvalue_manual(x = "group1", y.position = 0.055, stat.test, size = 4) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size = 15),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="T cell Score")) +
xlab("") +
ylab("Fractions") +
coord_cartesian(ylim = c(0,0.06))Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
use `guide = "none"` instead.# Create table with celltype fractions
cur_df <- data.frame(celltype = sce_prot$celltype,
Description = sce_prot$Description,
Location = sce_prot$Location)
# remove control samples
cur_df <- cur_df %>%
filter(Location != "CTRL") %>%
group_by(Description, celltype) %>%
summarise(n=n()) %>%
group_by(Description) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill=0)
matrixrownames <- cur_df$Description
# now we create a matrix from the data and cluster the data based on the cell fractions
hm_dat = as.matrix(cur_df[,-1])
rownames(hm_dat) <- as.character(matrixrownames)
# calculate distance and then cluster images based on cluster fraction
dd <- dist((hm_dat), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
row_sorted <- hc$labels
dend <- as.dendrogram(hc)
clusters <- data.frame(cutree(dend, k=4)) #### order_clusters_as_data = FALSE??
clusters_1E <- color_branches(dend, k = 4, col = c("gray50", "blue", "green", "red"), groupLabels = TRUE)
# get labels from dend
dend_labels <- clusters_1E %>%
labels()
# change colnames
colnames(clusters) <- "dend_cluster"
clusters$Description <- rownames(clusters)
# same orientation as in 1E
clusters <- clusters[match(dend_labels, clusters$Description),]
# change cluster names
clusters$cluster_name <- ""
clusters[clusters$dend_cluster == 3,]$cluster_name <- "Grey Branch"
clusters[clusters$dend_cluster == 4,]$cluster_name <- "Blue Branch"
clusters[clusters$dend_cluster == 2,]$cluster_name <- "Green Branch"
clusters[clusters$dend_cluster == 1,]$cluster_name <- "Red Branch"
# add cluster to sce_rna object
all_dat <- data.frame(colData(sce_rna))[,c("Description", "ImageNumber")]
all_dat <- left_join(all_dat, clusters)
sce_rna$cluster_name <- as.character(all_dat$cluster_name)
sce_rna$cluster_name <- factor(sce_rna$cluster_name, levels = c("Red Branch", "Green Branch", "Blue Branch", "Grey Branch"))
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
frac <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(Description, cluster_name, expressor) %>%
summarise(n=n()) %>%
mutate(fraction = n / sum(n)) %>%
filter(expressor %in% targets) %>%
reshape2::dcast(Description + cluster_name ~ expressor, value.var = "fraction", fill = 0) %>%
reshape2::melt(id.vars = c("Description", "cluster_name"), variable.name = "expressor", value.name = "fraction")
stat.test <- frac %>%
group_by(expressor) %>%
kruskal_test(data = ., fraction ~ cluster_name) %>%
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$expressor)
ggplot(frac, aes(x=expressor, y = fraction)) +
geom_boxplot(alpha=.75, outlier.size = 0.5, aes(fill = cluster_name)) +
stat_pvalue_manual(x = "group1", y.position = 0.055, stat.test, size = 4) +
scale_color_discrete(guide = FALSE) +
theme_bw() +
theme(text = element_text(size = 15),
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
guides(fill=guide_legend(title="Dendrogram Cluster")) +
xlab("") +
scale_fill_manual(values=c("red", "green", "blue", "grey")) +
ylab("Fractions") +
coord_cartesian(ylim = c(0,0.06))Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
use `guide = "none"` instead.# Load and prepare
dat_cells = fread(file = "data/data_for_analysis/rna/cell.csv",stringsAsFactors = FALSE)
dat_relation = fread(file = "data/data_for_analysis/rna/Object relationships.csv",stringsAsFactors = FALSE)
# Number of cores used for multicore:
if(detectCores() >= 12){
ncores = round(detectCores()/1.25,0)
}
if(detectCores() > 1 & detectCores() < 12){
ncores = round(detectCores()/2,0)
}
if(detectCores() == 1){
ncores = 1
}
n_perm = 100 start = Sys.time()
cur_sce <- as.data.frame(colData(sce_rna))
# add same cellID to dat_cells as in sce object
dat_cells$cellID <- paste("RNA_", paste(dat_cells$ImageNumber, dat_cells$ObjectNumber, sep = "_"), sep = "")
image_df <- data.frame()
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 9388034 501.4 14603417 780.0 14603417 780.0
Vcells 747115554 5700.1 1086952781 8292.8 905727318 6910.2rlimit_as(Inf)$cur
[1] Inf
$max
[1] Inffor(size in c(2,3,4,5,6)) {
images <- data.frame()
for(i in colnames(cur_sce[,grepl("CCL|CXCL",colnames(cur_sce))])){
# add chemokine info to celltype
sce_info <- cur_sce[,c("cellID", i , "Description")]
# add celltype information
dat_cells_tmp <- left_join(as.data.frame(dat_cells), sce_info, by = "cellID")
#assign labels and groups
dat_cells_tmp$label <- dat_cells_tmp[,i]
dat_cells_tmp$group <- dat_cells_tmp$Description
dat_cells_tmp <- as.data.table(dat_cells_tmp)
# subset dat_relation and dat_cells
dat_cells_sub <- dat_cells_tmp
dat_relation_sub <- dat_relation
# Prepare the data
d = neighbouRhood::prepare_tables(dat_cells_sub, dat_relation_sub)
# Calculate the baseline statistics
dat_baseline = neighbouRhood::apply_labels(d[[1]], d[[2]]) %>%
neighbouRhood::aggregate_classic_patch(., patch_size = size)
# Calculate the permutation statistics
# This will run the test using parallel computing. The name of the idcol does actually not matter.
set.seed(12312)
dat_perm = rbindlist(mclapply(1:n_perm, function(x){
dat_labels = neighbouRhood::shuffle_labels(d[[1]])
neighbouRhood::apply_labels(dat_labels, d[[2]]) %>%
neighbouRhood::aggregate_classic_patch(., patch_size = size)
},mc.cores = ncores
), idcol = 'run')
# calc p values
dat_p <- neighbouRhood::calc_p_vals(dat_baseline, dat_perm, n_perm = n_perm, p_tresh = 0.01)
# select interactions between chemokine+ cells
dat_p$interaction <- paste(dat_p$FirstLabel, dat_p$SecondLabel, sep = "_")
dat_p_wide <- dat_p %>%
reshape2::dcast(group ~ interaction, value.var = "sigval", fill = 0) %>%
select(group, `1_1`)
summary <- as.data.frame(dat_p_wide) %>%
group_by(`1_1`) %>%
summarise(n=n(),.groups = 'drop') %>%
ungroup() %>%
mutate(percentage_sig = (n/sum(n)) * 100)
images <- rbind(images, cbind(summary[1,], i))
gc()
}
# calculate percentage of images with significant patches
images$percentage_sig <- 100 - images$percentage_sig
images$patch_size <- size
images <- select(images, percentage_sig, i, patch_size)
colnames(images) <- c("significant_images", "chemokine", "patch_size")
# add to data.frame
image_df <- rbind(image_df, images)
}
end = Sys.time()
print(end-start)Time difference of 27.89348 minsdat <- image_df %>%
reshape2::dcast(chemokine ~ patch_size, value.var = "significant_images", fill = 0)
rownames(dat) <- dat$chemokine
dat$chemokine <- NULL
m <- t(as.matrix(dat))
col_fun = viridis::inferno(100)
Heatmap(m,
cluster_rows = FALSE,
col = col_fun,
column_title = "Self-Interaction",
column_title_side = "bottom",
show_row_names = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.1f", m[i, j]), x, y, gp = gpar(fontsize = 15, col = "grey"))
},
heatmap_legend_param = list(
title = "% Significant\nImages", at = c(0, 10, 20, 30, 40, 50),
labels = c("0%", "10%", "20%", "30%","40%", "50%")),
row_title = "Motif Size",
row_names_side = "left",
width = unit(15, "cm"),
height = unit(8, "cm"))
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] parallel grid stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] unix_1.5.4 neighbouRhood_0.4
[3] magrittr_2.0.2 dtplyr_1.2.1
[5] dendextend_1.15.2 rstatix_0.7.0
[7] ComplexHeatmap_2.10.0 ggpubr_0.4.0
[9] ggpmisc_0.4.5 ggpp_0.4.3
[11] data.table_1.14.2 cowplot_1.1.1
[13] gridExtra_2.3 dittoSeq_1.6.0
[15] scater_1.22.0 scuttle_1.4.0
[17] tidyr_1.2.0 ggbeeswarm_0.6.0
[19] ggplot2_3.3.5 SingleCellExperiment_1.16.0
[21] SummarizedExperiment_1.24.0 Biobase_2.54.0
[23] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[25] IRanges_2.28.0 S4Vectors_0.32.3
[27] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[29] matrixStats_0.61.0 dplyr_1.0.7
[31] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] backports_1.4.1 circlize_0.4.13
[3] plyr_1.8.6 BiocParallel_1.28.3
[5] digest_0.6.29 foreach_1.5.2
[7] htmltools_0.5.2 magick_2.7.3
[9] viridis_0.6.2 fansi_1.0.2
[11] ScaledMatrix_1.2.0 cluster_2.1.2
[13] doParallel_1.0.16 colorspace_2.0-2
[15] ggrepel_0.9.1 xfun_0.29
[17] callr_3.7.0 crayon_1.4.2
[19] RCurl_1.98-1.5 jsonlite_1.7.3
[21] iterators_1.0.13 glue_1.6.1
[23] gtable_0.3.0 zlibbioc_1.40.0
[25] XVector_0.34.0 MatrixModels_0.5-0
[27] GetoptLong_1.0.5 DelayedArray_0.20.0
[29] car_3.0-12 BiocSingular_1.10.0
[31] shape_1.4.6 abind_1.4-5
[33] SparseM_1.81 scales_1.1.1
[35] pheatmap_1.0.12 DBI_1.1.2
[37] Rcpp_1.0.8 viridisLite_0.4.0
[39] clue_0.3-60 rsvd_1.0.5
[41] httr_1.4.2 RColorBrewer_1.1-2
[43] ellipsis_0.3.2 pkgconfig_2.0.3
[45] farver_2.1.0 sass_0.4.0
[47] utf8_1.2.2 tidyselect_1.1.1
[49] labeling_0.4.2 rlang_1.0.0
[51] reshape2_1.4.4 later_1.3.0
[53] munsell_0.5.0 tools_4.1.2
[55] cli_3.1.1 generics_0.1.2
[57] broom_0.7.12 ggridges_0.5.3
[59] evaluate_0.14 stringr_1.4.0
[61] fastmap_1.1.0 yaml_2.2.2
[63] processx_3.5.2 knitr_1.37
[65] fs_1.5.2 purrr_0.3.4
[67] sparseMatrixStats_1.6.0 whisker_0.4
[69] quantreg_5.87 compiler_4.1.2
[71] rstudioapi_0.13 beeswarm_0.4.0
[73] png_0.1-7 ggsignif_0.6.3
[75] tibble_3.1.6 bslib_0.3.1
[77] stringi_1.7.6 highr_0.9
[79] ps_1.6.0 lattice_0.20-45
[81] Matrix_1.4-0 vctrs_0.3.8
[83] pillar_1.7.0 lifecycle_1.0.1
[85] jquerylib_0.1.4 GlobalOptions_0.1.2
[87] BiocNeighbors_1.12.0 bitops_1.0-7
[89] irlba_2.3.5 httpuv_1.6.5
[91] R6_2.5.1 promises_1.2.0.1
[93] vipor_0.4.5 codetools_0.2-18
[95] assertthat_0.2.1 rprojroot_2.0.2
[97] rjson_0.2.21 withr_2.4.3
[99] GenomeInfoDbData_1.2.7 beachmat_2.10.0
[101] rmarkdown_2.11 DelayedMatrixStats_1.16.0
[103] carData_3.0-5 git2r_0.29.0
[105] getPass_0.2-2