Last updated: 2021-02-05
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Knit directory: melanoma_publication_old_data/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 20a1458 | toobiwankenobi | 2021-02-04 | adapt figure order |
| Rmd | f9bb33a | toobiwankenobi | 2021-02-04 | new Figure 5 and minor changes in figure order |
| Rmd | 9442cb9 | toobiwankenobi | 2020-12-22 | add all new files |
| Rmd | d8c7699 | Tobias Hoch | 2020-10-23 | adapt figure 1 and supp fig 6 |
| Rmd | 1af3353 | toobiwankenobi | 2020-10-16 | add stuff |
| Rmd | d8819f2 | toobiwankenobi | 2020-10-08 | read new data (nuclei expansion) and adapt scripts |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
This script generates plots for Figure 1.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())library(SingleCellExperiment)
library(data.table)
library(scater)
library(ggplot2)
library(dittoSeq)
library(cba)
library(ComplexHeatmap)
library(corrplot)
library(dplyr)
library(reshape2)
library(tidyverse)
library(rms)
library(cytomapper)
library(ggrepel)
library(circlize)
library(ggbeeswarm)
library(dendextend)sce_rna <- readRDS("data/sce_RNA.rds")
sce_prot <- readRDS("data/sce_protein.rds")# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_rna[rowData(sce_rna)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)
dat <- data.frame(colData(sce_rna))[,c("cellID", "celltype")]
dat <- left_join(dat, marker_expression)
dat$cellID <- NULL
# aggregate the groups
dat_aggr <- dat %>%
group_by(celltype) %>%
summarise_all(funs(mean))Warning: `funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# number of cells per group
dat_sum <- dat %>%
group_by(celltype) %>%
summarise(n=n())
dat_sum <- data.frame(t(dat_sum))
# scale and center expression
dat_aggr[,-c(1)] <- scale(dat_aggr[,-c(1)])
# create matrix
m <- as.matrix(t(dat_aggr[,-c(1)]))
colnames(m) <- dat_aggr$celltype
# top annotation with number of cells
ha <- HeatmapAnnotation("Number of Cells" = anno_barplot(ifelse(as.numeric(dat_sum[2,])>100000, 101000, as.numeric(dat_sum[2,])),
height = unit(3,"cm"),
ylim = range(0,100000),
gp = gpar(fill = ifelse(as.numeric(dat_sum[2,]) > 100000, "white", "black"),
col="white"),
axis_param = list(gp = gpar(fontsize=14))),
"Numbers" = anno_text(round(as.numeric(dat_sum[2,])),
which = "column",
rot = 0,
just = "center",
location = 0.5,
gp = gpar(fontsize=10,col = ifelse(as.numeric(dat_sum[2,]) > 100000, "red", "black"))),
annotation_name_gp = gpar(fontsize = 16))
# row_split for markers
rowData(sce_rna)$heatmap_relevance <- ""
rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance <- "lineage"
rowData(sce_rna[grepl("CXCL|CCL|DapB", rownames(sce_rna)),])$heatmap_relevance <- "chemokine"
rowData(sce_rna[grepl("B2M|GLUT1|CD134|Lag3|CD163|cleavedPARP|pRB", rownames(sce_rna)),])$heatmap_relevance <- "other"
# plot heatmap
h <- Heatmap(m, name = "Scaled Expression",
row_split = rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance,
cluster_columns = TRUE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=18),
column_names_rot = 45,
column_names_centered = TRUE,
show_column_names = TRUE,
top_annotation = ha,
row_names_gp = gpar(fontsize = 16),
row_title_gp = gpar(fontsize = 23),
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-2:2), legend_width = unit(6,"cm"),
direction="horizontal", title_gp = gpar(fontsize=16),
labels_gp = gpar(fontsize=12)),
column_names_side = "top",
height = unit(20, "cm"),
width = unit(17,"cm"))
draw(h, heatmap_legend_side = "bottom")# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_prot[rowData(sce_prot)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)
dat <- data.frame(colData(sce_prot))[,c("cellID", "celltype")]
dat <- left_join(dat, marker_expression)
dat$cellID <- NULL
# aggregate the groups
dat_aggr <- dat %>%
group_by(celltype) %>%
summarise_all(funs(mean))
# number of cells per group
dat_sum <- dat %>%
group_by(celltype) %>%
summarise(n=n())
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$celltype
# top annotation with number of cells
ha <- HeatmapAnnotation("Number of Cells" = anno_barplot(ifelse(as.numeric(dat_sum[2,])>70000, 70000, as.numeric(dat_sum[2,])),
height = unit(3,"cm"),
ylim = range(0,70000),
gp = gpar(fill = ifelse(as.numeric(dat_sum[2,]) > 70000, "white", "black"),
col = "white"),
axis_param = list(gp = gpar(fontsize=14))),
"Numbers" = anno_text(round(as.numeric(dat_sum[2,])),
which = "column",
rot = 0,
just = "center",
location = 0.5,
gp = gpar(fontsize=10,col = ifelse(as.numeric(dat_sum[2,]) > 70000, "red", "black"))),
annotation_name_gp = gpar(fontsize = 16))
# row_split for markers
rowData(sce_prot)$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,])$heatmap_relevance,
cluster_columns = TRUE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=18),
column_names_rot = 45,
column_names_centered = TRUE,
show_column_names = TRUE,
row_names_gp = gpar(fontsize = 16),
row_title_gp = gpar(fontsize = 23),
top_annotation = ha,
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-2:2), legend_width = unit(6,"cm"),
direction="horizontal", title_gp = gpar(fontsize=16),
labels_gp = gpar(fontsize=12)),
column_names_side = "top",
height = unit(20, "cm"),
width = unit(17,"cm"))
draw(h, heatmap_legend_side = "bottom")cur_rna <- data.frame(colData(sce_rna))
cur_prot <- data.frame(colData(sce_prot))
rna_sum <- cur_rna %>%
group_by(Description, celltype) %>%
summarise(n = n()) %>%
mutate(fraction = n/sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)
prot_sum <- cur_prot %>%
group_by(Description, celltype) %>%
summarise(n = n()) %>%
mutate(fraction = n/sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)
# Correlation Plot - shared celltypes
shared <- c("Macrophage", "Neutrophil", "Tcytotoxic", "Tumor")
corrplot(cor(rna_sum[,-1], prot_sum[,-1], method = "pearson"),
addCoef.col = "white",
method = "circle",
tl.col="black",
tl.cex = 1.5)# Mean Correlation between shared celltypes
mean(c(cor(rna_sum$Macrophage, prot_sum$Macrophage, method = "pearson"),
cor(rna_sum$Neutrophil, prot_sum$Neutrophil, method = "pearson"),
cor(rna_sum$Tcytotoxic, prot_sum$Tcytotoxic, method = "pearson"),
cor(rna_sum$Tumor, prot_sum$Tumor, method = "pearson")))[1] 0.9389364all_mask <- loadImages(x = "/home/ubuntu/bbvolume/Data/Analysis/melanoma_cohort_new_cp_pipeline/protein/cpout/",
pattern = "ilastik_s2_Probabilities_equalized_cellmask.tiff")# we load the metadata for the images.
image_mat <- as.data.frame(read.csv(file = "data/protein/Image.csv",stringsAsFactors = FALSE))
# we extract only the FileNames of the masks as they are in the all_masks object
cur_df <- data.frame(cellmask = image_mat$FileName_cellmask,
ImageNumber = image_mat$ImageNumber,
Description = image_mat$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$FileName_cellmask)
# we add the extracted information via mcols in the order of the all_masks object
mcols(all_mask) <- cur_df[names(all_mask),]all_mask <- scaleImages(all_mask,2^16-1)# subset masks
mask_sub <- all_mask[mcols(all_mask)$Description %in% c("H9", "L7", "H2")]
sce_prot_sub <- sce_prot[,sce_prot$Description %in% c("H9", "L7", "H2")]
# rename all cells that are not tumor and not tcytotoxic
#sce_prot_sub$celltype <- ifelse(sce_prot_sub$celltype %in% c("Tumor", "Tcytotoxic"), sce_prot_sub$celltype, "other")
col_list <- list()
col_list$`Cell Type` <- metadata(sce_prot)$colour_vectors$celltype
sce_prot_sub$`Cell Type` <- sce_prot_sub$celltype
plotCells(mask = mask_sub,
object = sce_prot_sub,
cell_id = "CellNumber", img_id = "Description",
colour_by = "Cell Type",
colour = col_list,
display = "single")# Create table with celltype fractions
cur_df <- data.frame(celltype = sce_prot$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$Description
# now we create a matrix from the data and cluster the data based on the cell fractions
hm_dat = as.matrix(cur_df[,-1])
rownames(hm_dat) <- as.character(matrixrownames)
# calculate distance and then cluster images based on cluster fraction
dd <- dist((hm_dat), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
# order optimal orders the "leafs" of the cluster tree optimally to achieve smooth transitions
hc$order = order.optimal(dd, hc$merge)$order
row_sorted <- hc$labels[hc$order]
# now we generate the clinical metadata and order it in the same way as the celltype data
patient_meta <- metadata(sce_prot)
patient_meta <- data.frame(patient_meta[c("Description", "MM_location_simplified", "Location")])
patient_meta <- patient_meta %>%
filter(Location != "CTRL")
mrownames <- patient_meta$Description
patient_meta <- as.matrix(patient_meta)
rownames(patient_meta) <- mrownames
patient_meta <- data.frame(patient_meta[row_sorted,])
# generate the barplot. this is generated as the annotation for the heatmap of the Patient_ID that is generated below.
hm_dat <- hm_dat[row_sorted,]
# bring cell types in order (column order)
col_order <- c("Tumor", "Macrophage", "Neutrophil", "Tcytotoxic", "Thelper", "Tregulatory", "Bcell", "BnTcell", "pDC", "Stroma", "unknown")
hm_dat <- hm_dat[, col_order]
col_vector <- metadata(sce_prot)$colour_vector$celltype[colnames(hm_dat)]
col_vector_location <- structure(c("blue", "red", "green"),
names = c("LN", "other", "skin"))
# rename punch locations
patient_meta[patient_meta$Location == "M", ]$Location <- "margin"
patient_meta[patient_meta$Location == "C", ]$Location <- "core"
col_vector_punch <- structure(c("black", "grey"),
names = c("core", "margin"))
# create annotation with cell type proportions
ha <- rowAnnotation(`Punch Location` = patient_meta[,c("Location")],
`Cell Type Proportion` =
anno_barplot(hm_dat,
gp=gpar(fill=col_vector),
bar_width = 1,
height = unit(30,"cm"),
width = unit(15,"cm"),
show_row_names = FALSE),
col = list(`Punch Location` = col_vector_punch),
show_legend = FALSE)
dend <- as.dendrogram(hclust(dist(hm_dat, method = "euclidean"), method = "ward.D2"), ylim = c(0,10))
dend <- color_branches(dend, k = 4, groupLabels = TRUE) # `color_branches()` returns a dendrogram object
#plot(dend)
# heatmap consisting of the patient_IDs. one color per patient
h1 = Heatmap(patient_meta[,c("MM_location_simplified")],
col = col_vector_location,
width = unit(0.5, "cm"),
cluster_rows = dend,
row_dend_width = unit(3, "cm"),
height = unit(30, "cm"),
show_heatmap_legend = FALSE,
heatmap_legend_param = list(title = "Met Location"),
row_names_gp = gpar(cex=0.5),
show_row_names = TRUE,
right_annotation = ha,
column_labels = "Met Location")
# plot the data
ht = grid.grabExpr(draw(h1))
grid.newpage()
pushViewport(viewport(angle = 270))
grid.draw(ht)lgd1 <- Legend(labels = names(col_vector),
title = "Cell Type",
legend_gp = gpar(fill = col_vector),
ncol = 1)
lgd2 <- Legend(labels = names(col_vector_location),
legend_gp = gpar(fill = unname(col_vector_location)),
title = "Met Location",
ncol = 1)
lgd3 <- Legend(labels = names(col_vector_punch),
legend_gp = gpar(fill = unname(col_vector_punch)),
title = "Punch Location",
ncol = 1)
pd = packLegend(lgd1, lgd2, lgd3, direction = "vertical",
column_gap = unit(0.5, "cm"))
draw(pd)
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] dendextend_1.14.0 ggbeeswarm_0.6.0
[3] circlize_0.4.12 ggrepel_0.9.0
[5] cytomapper_1.3.1 EBImage_4.32.0
[7] rms_6.1-0 SparseM_1.78
[9] Hmisc_4.4-2 Formula_1.2-4
[11] survival_3.2-7 lattice_0.20-41
[13] forcats_0.5.0 stringr_1.4.0
[15] purrr_0.3.4 readr_1.4.0
[17] tidyr_1.1.2 tibble_3.0.4
[19] tidyverse_1.3.0 reshape2_1.4.4
[21] dplyr_1.0.2 corrplot_0.84
[23] ComplexHeatmap_2.4.3 cba_0.2-21
[25] proxy_0.4-24 dittoSeq_1.0.2
[27] scater_1.16.2 ggplot2_3.3.3
[29] data.table_1.13.6 SingleCellExperiment_1.12.0
[31] SummarizedExperiment_1.20.0 Biobase_2.50.0
[33] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[35] IRanges_2.24.1 S4Vectors_0.28.1
[37] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[39] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1
[3] systemfonts_0.3.2 plyr_1.8.6
[5] sp_1.4-5 shinydashboard_0.7.1
[7] splines_4.0.3 BiocParallel_1.22.0
[9] TH.data_1.0-10 digest_0.6.27
[11] htmltools_0.5.0 tiff_0.1-6
[13] viridis_0.5.1 fansi_0.4.1
[15] magrittr_2.0.1 checkmate_2.0.0
[17] cluster_2.1.0 limma_3.44.3
[19] modelr_0.1.8 svgPanZoom_0.3.4
[21] svglite_1.2.3.2 sandwich_3.0-0
[23] jpeg_0.1-8.1 colorspace_2.0-0
[25] rvest_0.3.6 haven_2.3.1
[27] xfun_0.20 crayon_1.3.4
[29] RCurl_1.98-1.2 jsonlite_1.7.2
[31] zoo_1.8-8 glue_1.4.2
[33] gtable_0.3.0 zlibbioc_1.36.0
[35] XVector_0.30.0 MatrixModels_0.4-1
[37] GetoptLong_1.0.5 DelayedArray_0.16.0
[39] BiocSingular_1.4.0 shape_1.4.5
[41] abind_1.4-5 scales_1.1.1
[43] mvtnorm_1.1-1 pheatmap_1.0.12
[45] DBI_1.1.0 edgeR_3.30.3
[47] Rcpp_1.0.5 xtable_1.8-4
[49] viridisLite_0.3.0 htmlTable_2.1.0
[51] clue_0.3-58 foreign_0.8-81
[53] rsvd_1.0.3 htmlwidgets_1.5.3
[55] httr_1.4.2 RColorBrewer_1.1-2
[57] ellipsis_0.3.1 pkgconfig_2.0.3
[59] nnet_7.3-14 dbplyr_2.0.0
[61] locfit_1.5-9.4 tidyselect_1.1.0
[63] rlang_0.4.10 later_1.1.0.1
[65] munsell_0.5.0 cellranger_1.1.0
[67] tools_4.0.3 cli_2.2.0
[69] generics_0.1.0 broom_0.7.3
[71] ggridges_0.5.3 fastmap_1.0.1
[73] fftwtools_0.9-9 evaluate_0.14
[75] yaml_2.2.1 knitr_1.30
[77] fs_1.5.0 nlme_3.1-151
[79] mime_0.9 quantreg_5.82
[81] whisker_0.4 xml2_1.3.2
[83] compiler_4.0.3 rstudioapi_0.13
[85] beeswarm_0.2.3 png_0.1-7
[87] reprex_0.3.0 stringi_1.5.3
[89] gdtools_0.2.3 Matrix_1.3-2
[91] vctrs_0.3.6 pillar_1.4.7
[93] lifecycle_0.2.0 GlobalOptions_0.1.2
[95] BiocNeighbors_1.6.0 conquer_1.0.2
[97] cowplot_1.1.1 bitops_1.0-6
[99] irlba_2.3.3 raster_3.4-5
[101] httpuv_1.5.4 R6_2.5.0
[103] latticeExtra_0.6-29 promises_1.1.1
[105] gridExtra_2.3 vipor_0.4.5
[107] codetools_0.2-18 polspline_1.1.19
[109] MASS_7.3-53 assertthat_0.2.1
[111] rprojroot_2.0.2 rjson_0.2.20
[113] withr_2.3.0 multcomp_1.4-15
[115] GenomeInfoDbData_1.2.4 hms_0.5.3
[117] rpart_4.1-15 rmarkdown_2.6
[119] DelayedMatrixStats_1.10.1 git2r_0.28.0
[121] shiny_1.5.0 lubridate_1.7.9.2
[123] base64enc_0.1-3