Last updated: 2022-02-23
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Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | fe331cb | toobiwankenobi | 2022-02-22 | re-run whole analysis |
| html | d246c15 | toobiwankenobi | 2022-02-22 | update Supp Fig 9 |
| Rmd | 64e5fde | toobiwankenobi | 2022-02-16 | change order and naming of supp fig files |
| Rmd | b20b6fb | toobiwankenobi | 2022-02-02 | update code for Supp Figures |
| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
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)
library(cytomapper)# 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$`Second Object Number`, sep = "_"), sep = "_")all_mask <- loadImages(x = "data/full_data/rna/cpout/",
pattern = "ilastik_s2_Probabilities_equalized_cellmask.tiff")# we load the metadata for the images.
image_mat_rna <- as.data.frame(read.csv(file = "data/data_for_analysis/rna/Image.csv",stringsAsFactors = FALSE))
# we extract only the FileNames of the masks as they are in the all_masks object
cur_df <- data.frame(cellmask = image_mat_rna$FileName_cellmask,
ImageNumber = image_mat_rna$ImageNumber,
Description = image_mat_rna$Metadata_Description)
# we set the rownames of the extracted data to be equal to the names of all_masks
rownames(cur_df) <- gsub(pattern = ".tiff",replacement = "",image_mat_rna$FileName_cellmask)
# we add the extracted information via mcols in the order of the all_masks object
mcols(all_mask) <- cur_df[names(all_mask),]all_mask <- scaleImages(all_mask,2^16-1)# select the images K10 (absent), K3 (low), A11 (med), N3 (high) as representative images for the T cell grouping
# subset masks
mask_sub <- all_mask[mcols(all_mask)$Description %in% c("K10", "N3", "K3", "A11")]
sce_prot_sub <- sce_prot[,sce_prot$Description %in% c("K10", "N3", "K3", "A11")]
# rename all cells that are not CD8+ T cell
sce_prot_sub$celltype <- ifelse(sce_prot_sub$celltype %in% c("CD8+ T cell"), sce_prot_sub$celltype, "Other")
# create color vector
col_list <- list()
col_list$`Cell Type` <- metadata(sce_prot)$colour_vectors$celltype[c("Tumor", "CD8+ T cell")]
names(col_list$`Cell Type`) <- c("Other", "CD8+ T cell")
col_list$`Cell Type`["CD8+ T cell"] <- "green"
sce_prot_sub$`Cell Type` <- sce_prot_sub$celltype
plotCells(mask = mask_sub,
object = sce_prot_sub,
cell_id = "CellNumber", img_id = "Description",
colour_by = "Cell Type",
colour = col_list)targets <- metadata(sce_rna)$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 11795507 630 18778532 1002.9 18778532 1002.9
Vcells 907534761 6924 1309232800 9988.7 1090431471 8319.4rlimit_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 27.07411 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] cytomapper_1.6.0 EBImage_4.36.0
[3] unix_1.5.4 neighbouRhood_0.4
[5] magrittr_2.0.2 dtplyr_1.2.1
[7] dendextend_1.15.2 rstatix_0.7.0
[9] ComplexHeatmap_2.10.0 ggpubr_0.4.0
[11] ggpmisc_0.4.5 ggpp_0.4.3
[13] data.table_1.14.2 cowplot_1.1.1
[15] gridExtra_2.3 dittoSeq_1.6.0
[17] scater_1.22.0 scuttle_1.4.0
[19] tidyr_1.2.0 ggbeeswarm_0.6.0
[21] ggplot2_3.3.5 SingleCellExperiment_1.16.0
[23] SummarizedExperiment_1.24.0 Biobase_2.54.0
[25] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[27] IRanges_2.28.0 S4Vectors_0.32.3
[29] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[31] matrixStats_0.61.0 dplyr_1.0.7
[33] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] backports_1.4.1 circlize_0.4.13
[3] systemfonts_1.0.3 plyr_1.8.6
[5] sp_1.4-6 shinydashboard_0.7.2
[7] BiocParallel_1.28.3 digest_0.6.29
[9] foreach_1.5.2 htmltools_0.5.2
[11] magick_2.7.3 viridis_0.6.2
[13] tiff_0.1-11 fansi_1.0.2
[15] ScaledMatrix_1.2.0 cluster_2.1.2
[17] doParallel_1.0.16 svgPanZoom_0.3.4
[19] svglite_2.0.0 jpeg_0.1-9
[21] colorspace_2.0-2 ggrepel_0.9.1
[23] xfun_0.29 callr_3.7.0
[25] crayon_1.4.2 RCurl_1.98-1.5
[27] jsonlite_1.7.3 iterators_1.0.13
[29] glue_1.6.1 gtable_0.3.0
[31] zlibbioc_1.40.0 XVector_0.34.0
[33] MatrixModels_0.5-0 GetoptLong_1.0.5
[35] DelayedArray_0.20.0 car_3.0-12
[37] BiocSingular_1.10.0 Rhdf5lib_1.16.0
[39] shape_1.4.6 HDF5Array_1.22.1
[41] abind_1.4-5 SparseM_1.81
[43] scales_1.1.1 pheatmap_1.0.12
[45] DBI_1.1.2 Rcpp_1.0.8
[47] xtable_1.8-4 viridisLite_0.4.0
[49] clue_0.3-60 rsvd_1.0.5
[51] htmlwidgets_1.5.4 httr_1.4.2
[53] RColorBrewer_1.1-2 ellipsis_0.3.2
[55] farver_2.1.0 pkgconfig_2.0.3
[57] sass_0.4.0 locfit_1.5-9.4
[59] utf8_1.2.2 labeling_0.4.2
[61] reshape2_1.4.4 tidyselect_1.1.1
[63] rlang_1.0.0 later_1.3.0
[65] munsell_0.5.0 tools_4.1.2
[67] cli_3.1.1 generics_0.1.2
[69] broom_0.7.12 ggridges_0.5.3
[71] fftwtools_0.9-11 evaluate_0.14
[73] stringr_1.4.0 fastmap_1.1.0
[75] yaml_2.2.2 processx_3.5.2
[77] knitr_1.37 fs_1.5.2
[79] purrr_0.3.4 sparseMatrixStats_1.6.0
[81] mime_0.12 whisker_0.4
[83] quantreg_5.87 compiler_4.1.2
[85] rstudioapi_0.13 beeswarm_0.4.0
[87] png_0.1-7 ggsignif_0.6.3
[89] tibble_3.1.6 bslib_0.3.1
[91] stringi_1.7.6 highr_0.9
[93] ps_1.6.0 lattice_0.20-45
[95] Matrix_1.4-0 vctrs_0.3.8
[97] rhdf5filters_1.6.0 pillar_1.7.0
[99] lifecycle_1.0.1 jquerylib_0.1.4
[101] GlobalOptions_0.1.2 BiocNeighbors_1.12.0
[103] bitops_1.0-7 irlba_2.3.5
[105] raster_3.5-15 httpuv_1.6.5
[107] R6_2.5.1 promises_1.2.0.1
[109] vipor_0.4.5 codetools_0.2-18
[111] assertthat_0.2.1 rhdf5_2.38.0
[113] rprojroot_2.0.2 rjson_0.2.21
[115] withr_2.4.3 GenomeInfoDbData_1.2.7
[117] terra_1.5-17 beachmat_2.10.0
[119] rmarkdown_2.11 DelayedMatrixStats_1.16.0
[121] carData_3.0-5 git2r_0.29.0
[123] getPass_0.2-2 shiny_1.7.1