Last updated: 2022-02-10
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Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 588dbb1 | toobiwankenobi | 2022-02-06 | Figure Order |
| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
| Rmd | c4e2793 | toobiwankenobi | 2021-08-04 | rearrange figure order to match pre-print |
| html | 4109ff1 | toobiwankenobi | 2021-07-07 | delete html files and adapt gitignore |
| Rmd | fc55711 | toobiwankenobi | 2021-07-07 | figure changes |
| html | fc55711 | toobiwankenobi | 2021-07-07 | figure changes |
| Rmd | 0f72ef1 | toobiwankenobi | 2021-05-11 | figure adaptations |
| html | 0f72ef1 | toobiwankenobi | 2021-05-11 | figure adaptations |
| Rmd | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| html | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| Rmd | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| html | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| Rmd | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
| 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. Panel A and B were created manually.
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(ComplexHeatmap)
library(dplyr)
library(reshape2)
library(tidyverse)
library(rms)
library(cytomapper)
library(ggrepel)
library(circlize)
library(ggbeeswarm)
library(dendextend)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"]# 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$cellID <- NULL
# aggregate the groups
dat_aggr <- dat %>%
group_by(celltype) %>%
summarise_all(funs(mean))Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.# number of cells per group
dat_sum <- dat %>%
group_by(celltype) %>%
summarise(n=n())
dat_sum <- data.frame(t(dat_sum))
# scale and center expression
dat_aggr[,-c(1)] <- scale(dat_aggr[,-c(1)])
# create matrix
m <- as.matrix(t(dat_aggr[,-c(1)]))
colnames(m) <- dat_aggr$celltype
# number annotation
ha <- HeatmapAnnotation("Numbers" = anno_text(format(round(as.numeric(dat_sum[2,])), nsmall = 0, big.mark = "'"),
which = "column",
rot = 90,
just = "center",
height = unit(2.3,"cm"),
location = 0.5,
gp = gpar(fontsize=16,col = "black", border = "black")))
# 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,])$heatmap_relevance,
cluster_columns = TRUE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=18),
column_names_rot = 45,
column_names_centered = FALSE,
show_column_names = TRUE,
top_annotation = ha,
row_names_gp = gpar(fontsize = 14),
row_title_gp = gpar(fontsize = 23),
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-2:2), legend_width = unit(6,"cm"),
direction="horizontal", title_gp = gpar(fontsize=16),
labels_gp = gpar(fontsize=12), title_position = "topcenter"),
column_names_side = "bottom",
height = unit(17, "cm"),
width = unit(15,"cm"))
draw(h, heatmap_legend_side = "bottom")# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_prot[rowData(sce_prot)$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$celltype
# number annotation
ha <- HeatmapAnnotation("Numbers" = anno_text(format(round(as.numeric(dat_sum[2,])), nsmall = 0, big.mark = "'"),
which = "column",
rot = 90,
just = "center",
height = unit(2.3,"cm"),
location = 0.5,
gp = gpar(fontsize=16,col = "black", border = "black")))
# row_split for markers
rowData(sce_prot)$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,])$heatmap_relevance,
cluster_columns = TRUE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=18),
column_names_rot = 45,
column_names_centered = FALSE,
show_column_names = TRUE,
row_names_gp = gpar(fontsize = 14),
row_title_gp = gpar(fontsize = 23),
top_annotation = ha,
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-2:2), legend_width = unit(6,"cm"),
direction="horizontal", title_gp = gpar(fontsize=16),title_position = "topcenter",
labels_gp = gpar(fontsize=12)),
column_names_side = "bottom",
height = unit(17, "cm"),
width = unit(15,"cm"))
draw(h, heatmap_legend_side = "bottom")all_mask <- loadImages(x = "data/full_data/protein/cpout/",
pattern = "ilastik_s2_Probabilities_equalized_cellmask.tiff")# we load the metadata for the images.
image_mat <- as.data.frame(read.csv(file = "data/data_for_analysis/protein/Image.csv",stringsAsFactors = FALSE))
# we extract only the FileNames of the masks as they are in the all_masks object
cur_df <- data.frame(cellmask = image_mat$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$celltype %in% c("Tumor", "CD8+ T cell"), sce_prot_sub$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$Description
# now we create a matrix from the data and cluster the data based on the cell fractions
hm_dat = as.matrix(cur_df[,-1])
rownames(hm_dat) <- as.character(matrixrownames)
# calculate distance and then cluster images based on cluster fraction
dd <- dist((hm_dat), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
row_sorted <- hc$labels
# 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$Description
patient_meta <- as.matrix(patient_meta)
rownames(patient_meta) <- mrownames
patient_meta <- data.frame(patient_meta[row_sorted,])
# generate the barplot. this is generated as the annotation for the heatmap of the Patient_ID that is generated below.
hm_dat <- hm_dat[row_sorted,]
# bring cell types in order (column order)
col_order <- c("Tumor", "Macrophage", "Neutrophil", "CD8+ T cell", "CD4+ T cell", "FOXP3+ T cell", "B cell", "BnT cell", "pDC", "Stroma", "unknown")
hm_dat <- hm_dat[, col_order]
col_vector <- metadata(sce_prot)$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(25,"cm"),
width = unit(11,"cm"),
show_row_names = FALSE),
col = list(`Punch Location` = col_vector_punch),
show_legend = FALSE)
dend <- as.dendrogram(hc)
dend <- color_branches(dend, k = 4, col = c("gray50", "blue", "green", "red"), groupLabels = TRUE) # `color_branches()` returns a dendrogram object
# 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(1, "cm"),
height = unit(25, "cm"),
show_heatmap_legend = FALSE,
heatmap_legend_param = list(title = "Met Location"),
row_names_gp = gpar(cex=3),
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 4.1.2 (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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dendextend_1.15.2 ggbeeswarm_0.6.0
[3] circlize_0.4.13 ggrepel_0.9.1
[5] cytomapper_1.6.0 EBImage_4.36.0
[7] rms_6.2-0 SparseM_1.81
[9] Hmisc_4.6-0 Formula_1.2-4
[11] survival_3.2-13 lattice_0.20-45
[13] forcats_0.5.1 stringr_1.4.0
[15] purrr_0.3.4 readr_2.1.2
[17] tidyr_1.2.0 tibble_3.1.6
[19] tidyverse_1.3.1 reshape2_1.4.4
[21] dplyr_1.0.7 ComplexHeatmap_2.10.0
[23] dittoSeq_1.6.0 scater_1.22.0
[25] ggplot2_3.3.5 scuttle_1.4.0
[27] data.table_1.14.2 SingleCellExperiment_1.16.0
[29] SummarizedExperiment_1.24.0 Biobase_2.54.0
[31] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[33] IRanges_2.28.0 S4Vectors_0.32.3
[35] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[37] matrixStats_0.61.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] shinydashboard_0.7.2 utf8_1.2.2
[3] tidyselect_1.1.1 htmlwidgets_1.5.4
[5] BiocParallel_1.28.3 munsell_0.5.0
[7] ScaledMatrix_1.2.0 codetools_0.2-18
[9] withr_2.4.3 colorspace_2.0-2
[11] highr_0.9 knitr_1.37
[13] rstudioapi_0.13 git2r_0.29.0
[15] GenomeInfoDbData_1.2.7 pheatmap_1.0.12
[17] rhdf5_2.38.0 rprojroot_2.0.2
[19] vctrs_0.3.8 generics_0.1.2
[21] TH.data_1.1-0 xfun_0.29
[23] R6_2.5.1 doParallel_1.0.16
[25] clue_0.3-60 rsvd_1.0.5
[27] locfit_1.5-9.4 rhdf5filters_1.6.0
[29] bitops_1.0-7 DelayedArray_0.20.0
[31] assertthat_0.2.1 promises_1.2.0.1
[33] scales_1.1.1 multcomp_1.4-18
[35] nnet_7.3-17 beeswarm_0.4.0
[37] gtable_0.3.0 beachmat_2.10.0
[39] processx_3.5.2 sandwich_3.0-1
[41] rlang_1.0.0 MatrixModels_0.5-0
[43] systemfonts_1.0.3 GlobalOptions_0.1.2
[45] splines_4.1.2 broom_0.7.12
[47] checkmate_2.0.0 yaml_2.2.2
[49] abind_1.4-5 modelr_0.1.8
[51] backports_1.4.1 httpuv_1.6.5
[53] tools_4.1.2 ellipsis_0.3.2
[55] raster_3.5-15 jquerylib_0.1.4
[57] RColorBrewer_1.1-2 ggridges_0.5.3
[59] Rcpp_1.0.8 plyr_1.8.6
[61] base64enc_0.1-3 sparseMatrixStats_1.6.0
[63] zlibbioc_1.40.0 RCurl_1.98-1.5
[65] ps_1.6.0 rpart_4.1.16
[67] GetoptLong_1.0.5 viridis_0.6.2
[69] cowplot_1.1.1 zoo_1.8-9
[71] haven_2.4.3 cluster_2.1.2
[73] fs_1.5.2 magrittr_2.0.2
[75] magick_2.7.3 reprex_2.0.1
[77] mvtnorm_1.1-3 whisker_0.4
[79] xtable_1.8-4 mime_0.12
[81] hms_1.1.1 evaluate_0.14
[83] fftwtools_0.9-11 jpeg_0.1-9
[85] readxl_1.3.1 gridExtra_2.3
[87] shape_1.4.6 compiler_4.1.2
[89] crayon_1.4.2 htmltools_0.5.2
[91] later_1.3.0 tzdb_0.2.0
[93] tiff_0.1-11 lubridate_1.8.0
[95] DBI_1.1.2 dbplyr_2.1.1
[97] MASS_7.3-55 Matrix_1.4-0
[99] cli_3.1.1 parallel_4.1.2
[101] pkgconfig_2.0.3 getPass_0.2-2
[103] sp_1.4-6 foreign_0.8-82
[105] terra_1.5-17 xml2_1.3.3
[107] foreach_1.5.2 svglite_2.0.0
[109] vipor_0.4.5 bslib_0.3.1
[111] XVector_0.34.0 rvest_1.0.2
[113] callr_3.7.0 digest_0.6.29
[115] rmarkdown_2.11 cellranger_1.1.0
[117] htmlTable_2.4.0 DelayedMatrixStats_1.16.0
[119] shiny_1.7.1 quantreg_5.87
[121] rjson_0.2.21 lifecycle_1.0.1
[123] nlme_3.1-155 jsonlite_1.7.3
[125] Rhdf5lib_1.16.0 BiocNeighbors_1.12.0
[127] viridisLite_0.4.0 fansi_1.0.2
[129] pillar_1.7.0 fastmap_1.1.0
[131] httr_1.4.2 glue_1.6.1
[133] svgPanZoom_0.3.4 png_0.1-7
[135] iterators_1.0.13 HDF5Array_1.22.1
[137] stringi_1.7.6 sass_0.4.0
[139] polspline_1.1.19 BiocSingular_1.10.0
[141] latticeExtra_0.6-29 irlba_2.3.5