scRNAseq_complete_04-2_celltype_markers_subcelltypes

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Last updated: 2024-02-12

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Knit directory: synovialscrnaseq/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/scRNAseq_complete_04-2_celltype_markers_subcelltypes.Rmd) and HTML (public/scRNAseq_complete_04-2_celltype_markers_subcelltypes.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	58eeb06	Reto Gerber	2023-05-30	add new version
html	58eeb06	Reto Gerber	2023-05-30	add new version
Rmd	4575ba6	Reto Gerber	2022-12-21	Update analyis
Rmd	e786402	Reto Gerber	2022-05-28	update figures to final version of paper
html	e786402	Reto Gerber	2022-05-28	update figures to final version of paper
Rmd	0f8368f	Reto Gerber	2022-05-20	update figures
html	0f8368f	Reto Gerber	2022-05-20	update figures
Rmd	5a6aa2a	Reto Gerber	2022-04-25	rerun with small resolution
html	5a6aa2a	Reto Gerber	2022-04-25	rerun with small resolution
Rmd	3443cc6	Reto Gerber	2022-04-25	Update
html	3443cc6	Reto Gerber	2022-04-25	Update
Rmd	b5b139f	Reto Gerber	2022-03-29	Update analysis
html	b5b139f	Reto Gerber	2022-03-29	Update analysis
Rmd	7d99571	Reto Gerber	2022-03-21	update analysis
html	7d99571	Reto Gerber	2022-03-21	update analysis

---

Set up

suppressPackageStartupMessages({
  library(magrittr)
  library(SingleCellExperiment)
  library(scater)
  library(scran)
  library(scuttle)
  library(magrittr)
})
n_workers <- 10
RhpcBLASctl::blas_set_num_threads(n_workers)

remove_low_quality_samples <- TRUE
analysis_version <- 7
source(here::here("code","utilities_plots.R"))

here::here()

[1] "/home/retger/Synovial/synovialscrnaseq"

set.seed(100)

hm_ls <- list()

get_celltype_centers <- function(sce, dimred, column){
    as.data.frame(reducedDim(sce,"UMAP_corrected")) %>% 
    dplyr::mutate(V3=sce[[column]]) %>% 
    dplyr::group_by(V3) %>% 
    dplyr::summarise(V1=mean(as.numeric(V1)),
                     V2=mean(as.numeric(V2)))
}

# move center of column
move_label <- function(celltype_centers,val=0, ax="X", what=NULL){
  if(is.null(what)){
    if(ax=="X"){
      celltype_centers$V1 <- celltype_centers$V1 + val
    } else if(ax=="Y"){
      celltype_centers$V2 <- celltype_centers$V2 + val
    }
  } else {
    stopifnot(what %in% celltype_centers$V3)
    if(ax=="X"){
      celltype_centers$V1[celltype_centers$V3 == what] <- celltype_centers$V1[celltype_centers$V3 == what] + val
    } else if(ax=="Y"){
      celltype_centers$V2[celltype_centers$V3 == what] <- celltype_centers$V2[celltype_centers$V3 == what] + val
    }
  }
  celltype_centers
}

findMarkers - SF

celltype_name_pre <- "sf"
# order_celltypes <- c(5,4,2,6,8,1,7,3)
used_clustering <- "sf_clusters_final"
# order_celltypes <- NULL
order_celltypes <- c(3,7,5,4,6,1,2)

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

if(is.null(sce_sub[[paste0(celltype_name_pre,"_celltype")]])){
  sce_sub[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce_sub[[used_clustering]])
  sce_sub[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]])
}

Loading required package: tidySingleCellExperiment

Attaching package: 'tidySingleCellExperiment'

The following object is masked from 'package:IRanges':

    slice

The following object is masked from 'package:S4Vectors':

    rename

The following object is masked from 'package:matrixStats':

    count

The following object is masked from 'package:magrittr':

    extract

The following object is masked from 'package:stats':

    filter

if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce_sub[[paste0(celltype_name_pre,"_celltype")]]))
}

format(object.size(sce_sub),units="Mb")

[1] "2523.9 Mb"

colLabels(sce_sub) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
sce_sub_red <- sce_sub
sce_sub_red <- sce_sub_red[rowData(sce_sub_red)$is_hvg,]
colData(sce_sub_red) <- NULL
rowData(sce_sub_red) <- NULL
counts(sce_sub_red) <- NULL
assay(sce_sub_red,"reconstructed") <- NULL
metadata(sce_sub_red) <- list()
reducedDims(sce_sub_red) <- list()
colLabels(sce_sub_red) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
as(sce_sub_red, "SingleCellExperiment")

class: SingleCellExperiment 
dim: 2614 30432 
metadata(0):
assays(1): logcounts
rownames(2614): SAMD11 HES4 ... MT-ND3 MAFIP
rowData names(0):
colnames(30432): Syn_Bio_079.AAACCCATCAAAGCCT
  Syn_Bio_079.AAACGAAAGAAGTCCG ... Syn_Bio_050.TTTGGAGTCATCCTGC-1
  Syn_Bio_050.TTTGGTTAGGTAACTA-1
colData names(1): label
reducedDimNames(0):
altExpNames(0):

format(object.size(sce_sub_red),units="Mb")

[1] "171.8 Mb"

markers <- findMarkers(sce_sub_red, test.type = "wilcox", pval.type = "all", direction="up",
                       lfc=0.5, block=sce_sub$Sample) 

saveRDS(markers,here::here("output",paste0("findMarkers_results_v",analysis_version,"_",celltype_name_pre,".rds")))

Heatmap - SF

markers <- readRDS(here::here("output",paste0("findMarkers_results_v",analysis_version,"_",celltype_name_pre,".rds")))

topmarkers <- purrr::map(seq_along(markers), function(i){
  markers[[i]] %>% 
  as.data.frame() %>% 
  dplyr::arrange(FDR) %>% 
  dplyr::select(FDR) %>% 
  head(n=10)
})
names(topmarkers) <- names(markers)
topmarkers

$`CADM1high ACAN+ DKK3+`
                 FDR
COL12A1 1.777646e-69
FNDC1   3.038790e-50
TPM1    1.838642e-35
ACAN    5.475484e-26
COMP    2.565001e-25
ASPN    4.216189e-25
TAGLN   2.658597e-19
POSTN   2.826811e-17
SFRP4   1.061789e-09
COL1A2  2.864558e-07

$`GGT5high CXCL12 high FGF7+`
                 FDR
CXCL12  3.986291e-53
IGFBP4  4.401633e-25
IER3    9.221636e-14
CFH     8.178939e-12
PTGFR   1.112774e-02
EGR1    1.283421e-01
GGT5    8.528055e-01
JUNB    1.000000e+00
KLF9    1.000000e+00
ZFP36L1 1.000000e+00

$`HLA-DRAhigh CD74+`
                  FDR
CHI3L2  1.706008e-175
CHI3L1   1.000000e+00
FTH1     1.000000e+00
PLTP     1.000000e+00
SOD2     1.000000e+00
ENAH     1.000000e+00
LUM      1.000000e+00
ID2      1.000000e+00
TNC      1.000000e+00
SLC38A2  1.000000e+00

$`MMP13+`
        FDR
SOX4      1
KLF6      1
JUN       1
CCN5      1
BNC2      1
MAP4K4    1
MAFB      1
RORA      1
EMILIN1   1
MDK       1

$`PRG4+ CD55+ TWISTNB+ lining SF`
                 FDR
PRG4   1.018388e-178
TIMP3  1.785080e-127
CD55   8.158007e-125
ITGB8  7.615618e-100
HTRA1  8.230421e-100
GFPT2   1.055650e-96
UAP1    2.753885e-78
HBEGF   1.150714e-75
HAS1    2.375721e-72
CRTAC1  4.528893e-68

$`SERPINE1+ COL5A3+ LOXL2high`
                   FDR
TPM2      3.352091e-26
TGFBI     2.054128e-15
LOXL2     1.328124e-05
COL5A3    8.969596e-04
TNFRSF12A 9.575967e-01
LTBP2     1.000000e+00
ITGA5     1.000000e+00
TUBA1B    1.000000e+00
S100A16   1.000000e+00
SLC20A1   1.000000e+00

$`TNXBhigh IGFBP6+ FGFBP2+`
                FDR
MFAP5  2.001058e-15
FBN1   1.563059e-09
TNXB   4.454351e-04
IGFBP6 7.697037e-04
LTBP4  1.393670e-02
FSTL1  1.000000e+00
ADIRF  1.000000e+00
CD248  1.000000e+00
AQP1   1.000000e+00
HTRA3  1.000000e+00

column <- paste0(celltype_name_pre,"_clusters_final")
column_labels <- paste0(celltype_name_pre,"_celltype")
sce_sub[[column]] <- factor(sce_sub[[column]],levels = order_celltypes)
dic <- unique(as.data.frame(colData(sce_sub)[,c(column,column_labels)])) %>% 
  dplyr::arrange(!!dplyr::sym(column))

# subcelltype_colors <- viridis::viridis(length(dic[[column]]))
subcelltype_colors <- rainbow(length(dic[[column]]))[order(as.character(dic[[column]]))]
names(subcelltype_colors) <- dic[[column]]
column_names <- paste0(dic[[column]], " - ",dic[[column_labels]])
dic$colors <- subcelltype_colors
dic$column_names <- column_names


markergenes_ls <- purrr::map(topmarkers[dic[[paste0(celltype_name_pre,"_celltype")]]],~rownames(.x))
markergenes_ls <- markergenes_ls
names(markergenes_ls) <- names(markergenes_ls) #%>% stringr::str_replace_all("/","\n")
markergenes_ls[[1]] <- c("THY1",markergenes_ls[[1]])
markergenes_n <- purrr::map(markergenes_ls, ~length(.x))
markergenes <- unlist(markergenes_ls)

clusters <- sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]
clucol <- dic$colors
names(clucol) <- dic$column_names 

samples <- sce_sub$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)

diagnosis <- sce_sub$Diagnosis_main
diagcol <- get_colors("diagnosis") 
diagcol <- diagcol[names(diagcol) %in% unique(sce_sub$Diagnosis_main)]


clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)

mataggr <- summarizeAssayByGroup(logcounts(sce_sub),clusters_samples, subset.row=markergenes)

tmpcoldat <- colData(sce_sub) %>%
  as.data.frame() %>%
  dplyr::mutate(tmp_clusters_final=!!dplyr::sym(paste0(celltype_name_pre,"_clusters_final"))) %>% 
  dplyr::left_join(data.frame(tmp_clusters_final=factor(order_celltypes),
           tmp_celltype_order=seq_along(unique(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]))),
           by="tmp_clusters_final") %>%
  dplyr::arrange(tmp_celltype_order, Diagnosis_main, Sample)

order_samples <- tmpcoldat %>%
  dplyr::select(tmp_clusters_final, Diagnosis_main, Sample) %>%
  unique() %>%
  dplyr::mutate(tmpname=paste0(stringr::str_replace_all(tmp_clusters_final,"/","\n"),"-",Diagnosis_main,"-", Sample)) %>%
  dplyr::pull()

mataggr <- mataggr[,order_samples]

colnamessplit <- stringr::str_split(colnames(mataggr),"-")
clustersaggr <- purrr::map_chr(colnamessplit,~.x[1]) 
diagcolsaggr <- purrr::map_chr(colnamessplit,~.x[2])
samplesaggr <- purrr::map_chr(colnamessplit,~.x[3])
tmpass <-  t(apply(assay(mataggr,"mean"),1,scale))
colnames(tmpass) <- colnames(assay(mataggr,"mean"))
assay(mataggr,"mean_scale") <- tmpass


  
order_samples <- colnames(mataggr)[order(colnames(mataggr))]

library(ComplexHeatmap)

Loading required package: grid

========================================
ComplexHeatmap version 2.6.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================

ha <- ComplexHeatmap::HeatmapAnnotation(
    `Cell type` = factor(
      clustersaggr,
      levels = unique(clustersaggr),
      labels = paste0(unique(clustersaggr)," - ",purrr::map_chr(unique(clustersaggr),                                                        ~dic[[paste0(celltype_name_pre,"_celltype")]][dic[[paste0(celltype_name_pre,"_clusters_final")]] == .x]))),
    # Diagnosis=diagcolsaggr,
    Sample = samplesaggr,
    col=list(`Cell type`=clucol,Sample=samcol,Diagnosis=diagcol),
    annotation_legend_param=list(labels_gp = gpar(fontsize=16),
                                 title_gp = gpar(fontsize=20,fontface="bold")))



ht_opt$TITLE_PADDING = unit(c(4, 20), "points")
ht <- ComplexHeatmap::Heatmap(
  assay(mataggr,"mean_scale"), 
  heatmap_legend_param = list(title = "Scaled expression",fontsize=20,gap=unit(20, "mm"),
                              labels_gp = gpar(fontsize=16),
                              title_gp = gpar(fontsize=20,fontface="bold")),  
  cluster_rows = FALSE, 
  cluster_columns=FALSE,
  column_order=order_samples,
  show_column_names=FALSE,
  column_split=factor(clustersaggr,levels = unique(clustersaggr)),
  column_title = NULL,
  column_gap = unit(0.2, "mm"),
  row_split=factor(unlist(purrr::map(seq_along(markergenes_n),~rep(names(markergenes_n)[.x],markergenes_n[[.x]]))),
                   levels = unique(clustersaggr)),
  row_title = NULL
)
hm_ls[[celltype_name_pre]] <- ha %v% ht

hm_ls[[celltype_name_pre]] 

Past versions of heatmap_sf-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28
0f8368f	Reto Gerber	2022-05-20
5a6aa2a	Reto Gerber	2022-04-25
b5b139f	Reto Gerber	2022-03-29
7d99571	Reto Gerber	2022-03-21

# order_celltypes <- c(5,4,2,8,1,7,3,6)
# order_celltypes <- NULL
order_celltypes <- c(3,7,5,4,6,1,2)

if(is.null(sce_sub[[paste0(celltype_name_pre,"_celltype")]])){
  sce_sub[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce_sub[[used_clustering]])
  sce_sub[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce_sub[[paste0(celltype_name_pre,"_celltype")]]))
}
column <- paste0(celltype_name_pre,"_clusters_final")
column_labels <- paste0(celltype_name_pre,"_celltype")
sce_sub[[column]] <- factor(sce_sub[[column]],levels = order_celltypes)
dic <- unique(as.data.frame(colData(sce_sub)[,c(column,column_labels)])) %>% 
  dplyr::arrange(!!dplyr::sym(column))

# subcelltype_colors <- viridis::viridis(length(dic[[column]]))
subcelltype_colors <- rainbow(length(dic[[column]]))[order(as.character(dic[[column]]))]
names(subcelltype_colors) <- dic[[column]]
column_names <- paste0(dic[[column]], " - ",dic[[column_labels]])
dic$colors <- subcelltype_colors
dic$column_names <- column_names


markergenes_ls <- list(
  `3` = c("COL1A1", "COL1A2", "PDPN"),
  `7` = c("PRG4", "CD55", "ITGB8", "MMP1", "HBEGF", "TWISTNB", "ITGBL1", 
"CLIC5", "TMEM196", "PGF", "ZNF385B", "GABRA4", "GJB2", "FAM49A", 
"SEMA3A", "SEMA5A", "SLC2A12", "FGF10", "GPR1", "CXCL6", "CITED2", 
"PCSK6", "VEGFC", "CHST2", "RELL1", "NTN4", "ANK3", "C10orf105", 
"HAS1", "SMIM14", "CXCL1", "INHBA", "GPX3", "ERRFI1", "MMP3", 
"CLU", "TIMP3", "CRTAC1", "HTRA1", "SEMA3C", "DEFB1", "IGFBP5", 
"UAP1", "SOX5", "SLC7A2", "RCAN1", "ADAMTS5"),
  `4` = c("CD74", "HLA-DRA", "HLA-DRB1", "HLA-DPB1", "PLTP", "CHI3L1", 
"LUM", "PHLDA1"),
  `6` = c("TNXB", "IGFBP6", "FGFBP2", "CD34", "DPP4", "MFAP5", "ADIRF", 
"FSTL1", "ELN"),
  `1` = c("SERPINE1", "LOXL2", "ANPEP", "COL4A1", "TGFBI", "TGFB1", "COL5A3", 
"PHLDA2", "SLC16A3", "IGFBP3", "TPM2", "S100A16", "SLC20A1", 
"LTBP2"),
  `2` = c("SFRP1", "APOE", "IL6", "CXCL2", "GGT5", "FGF7", "CXCL12", 
"NOTCH3", "CXCL14", "C3", "GAS6", "ID4", "AHR", "RASD1", "TMEM176A", 
"TMEM176B", "IGFBP4", "VCAN"),
  `8` = c("MMP13", "CADM1", "ACAN", "DKK3", "POSTN", "COL12A1", "COMP", "FNDC1", 
"SFRP4", "SCX", "PTN", "LRRC17", "GXYLT2", "EDIL3", "TPM1", "TAGLN", 
"ASPN", "LHFPL6")
)

for (i in seq_along(markergenes_ls)) {
  markergenes_ls[[i]] <- markergenes_ls[[i]][markergenes_ls[[i]] %in% rownames(sce_sub)]
}


markergenes_n <- purrr::map(markergenes_ls, ~length(.x))
markergenes <- unlist(markergenes_ls)

tmp_figure_sf <- hm_ls[["sf"]]

clusters <- sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]
clucol <- dic$colors
names(clucol) <- dic$column_names 

samples <- sce_sub$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)

diagnosis <- sce_sub$Diagnosis_main
diagcol <- get_colors("diagnosis") 
diagcol <- diagcol[names(diagcol) %in% unique(sce_sub$Diagnosis_main)]


clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)

mataggr <- summarizeAssayByGroup(logcounts(sce_sub),clusters_samples, subset.row=markergenes)

tmpcoldat <- colData(sce_sub) %>%
  as.data.frame() %>%
  dplyr::mutate(tmp_clusters_final=!!dplyr::sym(paste0(celltype_name_pre,"_clusters_final"))) %>% 
  dplyr::left_join(data.frame(tmp_clusters_final=factor(order_celltypes),
           tmp_celltype_order=seq_along(unique(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]))),
           by="tmp_clusters_final") %>%
  dplyr::arrange(tmp_celltype_order, Diagnosis_main, Sample)

order_samples <- tmpcoldat %>%
  dplyr::select(tmp_clusters_final, Diagnosis_main, Sample) %>%
  unique() %>%
  dplyr::mutate(tmpname=paste0(stringr::str_replace_all(tmp_clusters_final,"/","\n"),"-",Diagnosis_main,"-", Sample)) %>%
  dplyr::pull()

mataggr <- mataggr[,order_samples]

colnamessplit <- stringr::str_split(colnames(mataggr),"-")
clustersaggr <- purrr::map_chr(colnamessplit,~.x[1]) 
diagcolsaggr <- purrr::map_chr(colnamessplit,~.x[2])
samplesaggr <- purrr::map_chr(colnamessplit,~.x[3])
tmpass <-  t(apply(assay(mataggr,"mean"),1,scale))
colnames(tmpass) <- colnames(assay(mataggr,"mean"))
assay(mataggr,"mean_scale") <- tmpass


  
order_samples <- colnames(mataggr)[order(colnames(mataggr))]

library(ComplexHeatmap)

ha <- ComplexHeatmap::HeatmapAnnotation(
    `Cell type` = factor(
      clustersaggr,
      levels = unique(clustersaggr),
      labels = paste0(unique(clustersaggr)," - ",purrr::map_chr(unique(clustersaggr),                                                        ~dic[[paste0(celltype_name_pre,"_celltype")]][dic[[paste0(celltype_name_pre,"_clusters_final")]] == .x]))),
    # Diagnosis=diagcolsaggr,
    Sample = samplesaggr,
    col=list(`Cell type`=clucol,Sample=samcol,Diagnosis=diagcol),
    annotation_legend_param=list(labels_gp = gpar(fontsize=16),
                                 title_gp = gpar(fontsize=20,fontface="bold")))



ht_opt$TITLE_PADDING = unit(c(4, 20), "points")
ht <- ComplexHeatmap::Heatmap(
  assay(mataggr,"mean_scale"), 
  heatmap_legend_param = list(title = "Scaled expression",fontsize=20,gap=unit(20, "mm"),
                              labels_gp = gpar(fontsize=16),
                              title_gp = gpar(fontsize=20,fontface="bold")),  
  cluster_rows = FALSE, 
  cluster_columns=FALSE,
  column_order=order_samples,
  show_column_names=FALSE,
  column_split=factor(clustersaggr,levels = unique(clustersaggr)),
  column_title = NULL,
  column_gap = unit(0.2, "mm"),
  row_split=factor(unlist(purrr::map(seq_along(markergenes_n),~rep(names(markergenes_n)[.x],markergenes_n[[.x]]))),
                   levels = unique(clustersaggr)),
  row_title = NULL
)
hm_ls[[celltype_name_pre]] <- ha %v% ht

hm_ls[[celltype_name_pre]] 

Past versions of heatmap_sf_new-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28

hm_ls[["sf_new"]] <- hm_ls[["sf"]] 
hm_ls[["sf"]] <- tmp_figure_sf
# png(here::here("..","synovialscrnaseq","output","Figures_Paper","Suppl_Figure_9_new.png"),width=26,height=24,res = 144, units = "in")
# hm_ls[["sf_new"]] 
# dev.off()

findMarkers - MP

celltype_name_pre <- "mp"
# order_celltypes <- c(11,1,8,5,4,9,10,7,3,12,13,2,6)
order_celltypes <- c(9,6,5,1,4,8,2,10,3,11,12,7)
used_clustering <- "mp_clusters_k10"
# order_celltypes <- NULL

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

if(is.null(sce_sub[[paste0(celltype_name_pre,"_celltype")]])){
  sce_sub[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce_sub[[used_clustering]])
  sce_sub[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce_sub[[paste0(celltype_name_pre,"_celltype")]]))
}
format(object.size(sce_sub),units="Mb")

[1] "2577 Mb"

colLabels(sce_sub) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
sce_sub_red <- sce_sub
sce_sub_red <- sce_sub_red[rowData(sce_sub_red)$is_hvg,]
colData(sce_sub_red) <- NULL
rowData(sce_sub_red) <- NULL
counts(sce_sub_red) <- NULL
assay(sce_sub_red,"reconstructed") <- NULL
metadata(sce_sub_red) <- list()
reducedDims(sce_sub_red) <- list()
colLabels(sce_sub_red) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
as(sce_sub_red, "SingleCellExperiment")

class: SingleCellExperiment 
dim: 1908 35659 
metadata(0):
assays(1): logcounts
rownames(1908): HES4 ISG15 ... MT-ND5 AC145212.1
rowData names(0):
colnames(35659): Syn_Bio_079.AAACCCAGTTTAGTCG
  Syn_Bio_079.AAACGAAAGACATAGT ... Syn_Bio_050.TTTGGAGGTAGAATGT-1
  Syn_Bio_050.TTTGTTGGTCTGTAAC-1
colData names(1): label
reducedDimNames(0):
altExpNames(0):

format(object.size(sce_sub_red),units="Mb")

[1] "138.2 Mb"

markers <- findMarkers(sce_sub_red, test.type = "wilcox", pval.type = "all", direction="up",
                       lfc=0.5, block=sce_sub$Sample) 

saveRDS(markers,here::here("output",paste0("findMarkers_results_v",analysis_version,"_",celltype_name_pre,".rds")))
topmarkers <- purrr::map(seq_along(markers), function(i){
  markers[[i]] %>% 
  as.data.frame() %>% 
  dplyr::arrange(FDR) %>% 
  dplyr::select(FDR) %>% 
  head(n=10)
})
names(topmarkers) <- names(markers)
topmarkers

$`C1QA/B/C+ FOLR2low CCR2+ CD48+ CLEC10A`
          FDR
HBEGF       1
OTUD1       1
IER3        1
TNFAIP3     1
CXCL8       1
IRF1        1
CHMP1B      1
ATF3        1
ARL4A       1
GABARAPL1   1

$`CD1C+ CLEC10A+`
                 FDR
CLEC10A 5.192886e-17
FCER1A  5.349127e-13
AREG    2.291077e-01
JAML    1.000000e+00
CREM    1.000000e+00
PKIB    1.000000e+00
IFITM2  1.000000e+00
CD1C    1.000000e+00
GPAT3   1.000000e+00
EZR     1.000000e+00

$`CD48+ CLEC10A+`
       FDR
SORL1    1
CHMP1B   1
OLR1     1
RGS2     1
ARL4A    1
HCST     1
BTG1     1
FGL2     1
YPEL5    1
EREG     1

$`CD48high S100A12+ IL1B+`
                 FDR
S100A9  1.361772e-47
S100A8  6.329126e-46
VCAN    3.004319e-30
FCN1    1.785182e-12
S100A12 5.272929e-12
FCAR    3.798016e-11
FPR1    2.433728e-08
SLC2A3  1.774679e-03
SLC11A1 3.164546e-03
CD93    6.112539e-03

$`CD48low SPP1+`
         FDR
FBP1       1
LDHA       1
C15orf48   1
PHLDA1     1
TIMP1      1
CD9        1
ANPEP      1
ALCAM      1
S100A10    1
IL1RN      1

$`CLEC9A+ CADM1+ CLNK+`
                  FDR
CPVL     6.064449e-32
CLEC9A   1.737663e-29
C1orf54  5.861233e-29
CST3     9.017387e-27
S100B    5.989549e-19
CCND1    3.324747e-18
CADM1    2.227152e-17
TACSTD2  6.216290e-16
BATF3    1.411153e-15
DNASE1L3 1.724319e-13

$`FOLR2+ MERTK+ TIMD4+ & FOLR2low MERTKlow SPP1+ subsets`
        FDR
FN1       1
SLC39A8   1
MT2A      1
TGFBI     1
FABP5     1
MT1X      1
EMP1      1
SOD2      1
ATF5      1
HSPA5     1

$`FOLR2high MERTK+ SELENOPhigh CD48med`
      FDR
CXCL3   1
CXCL8   1
THBD    1
CXCL2   1
ELL2    1
MMP19   1
TFRC    1
HIF1A   1
ID2     1
HMOX1   1

$`FOLR2high MERTK+ SELENOPhigh COLEC12high LYVE1+ CD209+ SLC40A1+`
                 FDR
SELENOP 4.533771e-55
FOLR2   5.018420e-27
PLTP    4.329616e-19
SLC40A1 5.858257e-11
DAB2    8.735748e-09
F13A1   9.683780e-09
JUN     4.050644e-07
FCGRT   2.549959e-04
EGR1    8.635116e-03
CCL3    8.353835e-01

$`FOLR2high MERTK+ SELENOPhigh COLEC12high TIMD4+`
                FDR
NUPR1  9.598052e-12
IFI27  1.000000e+00
GPNMB  1.000000e+00
TXNIP  1.000000e+00
TIMD4  1.000000e+00
MT-ND5 1.000000e+00
MARCO  1.000000e+00
CTSD   1.000000e+00
KCNMA1 1.000000e+00
DST    1.000000e+00

$`FOLR2low MERTKlow TOP2A+ CENPF+ proliferating`
               FDR
MKI67 6.571993e-30
CENPF 1.599883e-26
CEP55 1.807124e-26
BIRC5 3.970799e-26
STMN1 3.758186e-25
TPX2  9.172587e-21
ASPM  7.046794e-20
TOP2A 3.436303e-19
ANLN  4.578177e-19
TK1   2.384672e-18

$`IDO1+ LAMP3+`
                  FDR
LAMP3    1.780848e-33
CCR7     1.536981e-22
TNFAIP2  1.150758e-18
BIRC3    3.836952e-17
CD83     2.239287e-15
FSCN1    7.009093e-15
PNRC1    8.813582e-15
REL      1.257373e-12
DUSP5    6.962994e-11
MARCKSL1 1.812204e-09

markergenes <- c("PTPRC","ITGAM","FCGR1A","CD14","CD63","CD68","MARCO","TREM2","RNASE1","LGMN","DAB2","APOE","CD163","FOLR2","MERTK","MRC1","CMKLR1","STAB1","PLTP","MAF","SLCO2B1","PMP22","C1QA","C1QB","C1QC","COLEC12","NUPR1","IFI27","MAMDC2","CCL18","CCL13","TIMD4","GPR34","SELENOP","NRP1","ICAM1","F13A1","EGR1","LYVE1","CD209","SLC40A1","IGF1","LYZ","CEBPD","ZNF331","RGS2","CREM","NAMPT","NR4A2","BTG1","INSIG1","IER3","VEGFA","LGALS2","AREG","CXCR4","NR4A3","CCR2","CX3CR1","GPR183","ARL4C","CLEC10A","SPP1","EREG","IL1RN","PPIF","FCN1","CD52","ITGAX","CD300E","FCAR","APOBEC3A","SLC11A1","FPR2","S100A9","S100A8","S100A12","PLAC8","SELL","CD93","CSF3R","CD48","IL1B","RILPL2","SAMSN1","SLC7A5","CLEC4A","THBS1","CSTA","G0S2","CD1C","FCER1A","LAMP3","CD83","IDO1","CLEC9A","CLNK","CADM1","IRF8")

column <- paste0(celltype_name_pre,"_clusters_final")
column_labels <- paste0(celltype_name_pre,"_celltype")
sce_sub[[column]] <- factor(sce_sub[[column]],levels = order_celltypes)
dic <- unique(as.data.frame(colData(sce_sub)[,c(column,column_labels)])) %>%
  dplyr::arrange(!!dplyr::sym(column))

# subcelltype_colors <- viridis::viridis(length(dic[[column]]))
subcelltype_colors <- rainbow(length(dic[[column]]))[order(as.character(dic[[column]]))]
names(subcelltype_colors) <- dic[[column]]
tmp <- subcelltype_colors[names(subcelltype_colors)=="1"]
subcelltype_colors[names(subcelltype_colors)=="1"] <- subcelltype_colors[names(subcelltype_colors)=="7"]
subcelltype_colors[names(subcelltype_colors)=="7"] <- tmp
tmp <- subcelltype_colors[names(subcelltype_colors)=="3"]
subcelltype_colors[names(subcelltype_colors)=="3"] <- subcelltype_colors[names(subcelltype_colors)=="10"]
subcelltype_colors[names(subcelltype_colors)=="10"] <- tmp
column_names <- paste0(dic[[column]], " - ",dic[[column_labels]])
dic$colors <- subcelltype_colors
dic$column_names <- column_names

clusters <- sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]
clucol <- dic$colors
names(clucol) <- dic$column_names

samples <- sce_sub$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)

diagnosis <- sce_sub$Diagnosis_main
diagcol <- get_colors("diagnosis")
diagcol <- diagcol[names(diagcol) %in% unique(sce_sub$Diagnosis_main)]


clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)

mataggr <- summarizeAssayByGroup(logcounts(sce_sub),clusters_samples, subset.row=markergenes)

# order
tmpcoldat <- colData(sce_sub) %>%
  as.data.frame() %>%
  dplyr::mutate(tmp_clusters_final=!!dplyr::sym(paste0(celltype_name_pre,"_clusters_final"))) %>%
  dplyr::left_join(data.frame(tmp_clusters_final=factor(order_celltypes),
           tmp_celltype_order=seq_along(unique(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]))),
           by="tmp_clusters_final") %>%
  dplyr::arrange(tmp_celltype_order, Diagnosis_main, Sample)

order_samples <- tmpcoldat %>%
  dplyr::select(tmp_clusters_final, Diagnosis_main, Sample) %>%
  unique() %>%
  dplyr::mutate(tmpname=paste0(stringr::str_replace_all(tmp_clusters_final,"/","\n"),"-",Diagnosis_main,"-", Sample)) %>%
  dplyr::pull()

mataggr <- mataggr[,order_samples]

colnamessplit <- stringr::str_split(colnames(mataggr),"-")
clustersaggr <- purrr::map_chr(colnamessplit,~.x[1])
diagcolsaggr <- purrr::map_chr(colnamessplit,~.x[2])
samplesaggr <- purrr::map_chr(colnamessplit,~.x[3])
tmpass <-  t(apply(assay(mataggr,"mean"),1,scale))
colnames(tmpass) <- colnames(assay(mataggr,"mean"))
assay(mataggr,"mean_scale") <- tmpass


library(ComplexHeatmap)
ht_opt$TITLE_PADDING = unit(c(4, 20), "points")

tmpct <- factor(clustersaggr,levels = unique(clustersaggr),labels = paste0(unique(clustersaggr)," - ",purrr::map_chr(unique(clustersaggr),~dic$mp_celltype[dic$mp_clusters_final==.x])))
cluolord <- purrr::map_int(seq_along(levels(tmpct)),~ which(levels(tmpct)[.x] == names(clucol)))
clucol <- clucol[cluolord]




ha <- ComplexHeatmap::HeatmapAnnotation(show_legend = c(FALSE, TRUE, TRUE),
    `Cell type` = tmpct,
    # Diagnosis=diagcolsaggr,
    Sample = samplesaggr,
    col=list(`Cell type`=clucol,Sample=samcol,Diagnosis=diagcol),
    annotation_legend_param=list(labels_gp = gpar(fontsize=16),
                                 title_gp = gpar(fontsize=20,fontface="bold")))



hm <- ComplexHeatmap::Heatmap(
  assay(mataggr,"mean_scale"),
  heatmap_legend_param = list(title = "Scaled expression",fontsize=20,gap=unit(20, "mm"),
                              labels_gp = gpar(fontsize=16),
                              title_gp = gpar(fontsize=20,fontface="bold")),    cluster_rows = FALSE,
  cluster_columns=FALSE,
  column_order=order_samples,
  show_column_names=FALSE,
  column_split=factor(clustersaggr,levels = unique(clustersaggr)),
  column_gap = unit(0.2, "mm"),
  column_title = NULL
)

hm_ls[[celltype_name_pre]] <- ha %v% hm

lgd_list <- list(
  Legend(at = levels(tmpct), title = "\nCell type",
         legend_gp = gpar(fill = clucol),
         labels_gp = gpar(fontsize=16),
         title_gp = gpar(fontsize=20,fontface="bold"))
)
# lgd_list <- list(
#   Legend(at = levels(tmpct)[1], title = "\nCell type",
#          legend_gp = gpar(fill = clucol[1]),
#          labels_gp = gpar(fontsize=16),
#          title_gp = gpar(fontsize=20,fontface="bold")),
#   Legend(at = levels(tmpct)[2:6], title = "FOL2R+ MERTK+ CD206+ macrophages",
#          legend_gp = gpar(fill = clucol[2:6]),
#          labels_gp = gpar(fontsize=16),
#          title_gp = gpar(fontsize=18,fontface="bold")),
#   Legend(at = levels(tmpct)[7:9], title = "CCR2+ macrophages",
#          legend_gp = gpar(fill = clucol[7:9]),
#          labels_gp = gpar(fontsize=16),
#          title_gp = gpar(fontsize=18,fontface="bold")),
#   Legend(at = levels(tmpct)[10:13], title = "Dendritic cells (DSs)",
#          legend_gp = gpar(fill = clucol[10:13]),
#          labels_gp = gpar(fontsize=16),
#          title_gp = gpar(fontsize=18,fontface="bold"))
#   )

hm_ls[[paste0(celltype_name_pre,"_draw")]] <- draw(hm_ls[[celltype_name_pre]], ht_gap = unit(0, "mm"), merge_legends=TRUE, annotation_legend_list = lgd_list,align_annotation_legend="heatmap_center")

Past versions of mp_heatmap-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28

findMarkers - TC

celltype_name_pre <- "tc"
order_celltypes <- c(4,5,1,6,7,8,2,3,9)
used_clustering <- "tc_clusters_final"
# order_celltypes <- NULL

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

if(is.null(sce_sub[[paste0(celltype_name_pre,"_celltype")]])){
  sce_sub[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce_sub[[used_clustering]])
  sce_sub[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce_sub[[paste0(celltype_name_pre,"_celltype")]]))
}
format(object.size(sce_sub),units="Mb")

[1] "931.7 Mb"

colLabels(sce_sub) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
sce_sub_red <- sce_sub
sce_sub_red <- sce_sub_red[rowData(sce_sub_red)$is_hvg,]
colData(sce_sub_red) <- NULL
rowData(sce_sub_red) <- NULL
counts(sce_sub_red) <- NULL
assay(sce_sub_red,"reconstructed") <- NULL
metadata(sce_sub_red) <- list()
reducedDims(sce_sub_red) <- list()
colLabels(sce_sub_red) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
as(sce_sub_red, "SingleCellExperiment")

class: SingleCellExperiment 
dim: 2814 23169 
metadata(0):
assays(1): logcounts
rownames(2814): PERM1 HES4 ... MT-TL2 AL354822.1
rowData names(0):
colnames(23169): Syn_Bio_079.AAACCCATCCTGGTCT
  Syn_Bio_079.AAACCCATCGAACCAT ... Syn_Bio_050.TTTGGTTTCACATTGG-1
  Syn_Bio_050.TTTGTTGTCGCCCAGA-1
colData names(1): label
reducedDimNames(0):
altExpNames(0):

format(object.size(sce_sub_red),units="Mb")

[1] "47.3 Mb"

markers <- findMarkers(sce_sub_red, test.type = "wilcox", pval.type = "all", direction="up",
                       lfc=0.5, block=sce_sub$Sample) 

saveRDS(markers,here::here("output",paste0("findMarkers_results_v",analysis_version,"_",celltype_name_pre,".rds")))
topmarkers <- purrr::map(seq_along(markers), function(i){
  markers[[i]] %>% 
  as.data.frame() %>% 
  dplyr::arrange(FDR) %>% 
  dplyr::select(FDR) %>% 
  head(n=10)
})
names(topmarkers) <- names(markers)
topmarkers

$`CCR7high LEF1+ SELL+`
                FDR
RPS8   3.241573e-09
RPL21  1.346744e-04
RPS6   9.389380e-03
RPS13  2.518856e-02
RPL23  2.519568e-02
RPS3A  1.383866e-01
RPL13  1.795839e-01
EEF1B2 1.795839e-01
RPS18  2.208074e-01
RPS12  4.694525e-01

$`CCR7med LEF1low SELLlow`
         FDR
RGCC       1
ANXA1      1
KLF6       1
FOS        1
TSC22D3    1
DUSP1      1
TNFAIP3    1
LMNA       1
MCL1       1
PPP1R15A   1

$`CD3- NKG7- KLRB1+ IL7R+`
         FDR
FOSB       1
KLRB1      1
NFKBIA     1
AREG       1
JUND       1
TNFRSF18   1
TIMP1      1
BTG2       1
XCL1       1
LMNA       1

$`CD3- NKG7+ GNLY+`
       FDR
GNLY     1
TYROBP   1
FCER1G   1
GZMB     1
CD7      1
KLRD1    1
IER2     1
REL      1
PRF1     1
IFITM2   1

$`GZMB- GZMH- GZMK+`
       FDR
DUSP2    1
TUBA4A   1
PIK3R1   1
NR4A2    1
CXCR4    1
CREM     1
RGS1     1
GZMM     1
RUNX3    1
GZMK     1

$`GZMB+ GZMH+ GZMK- GNLY+`
       FDR
GZMH     1
CD52     1
RUNX3    1
LGALS1   1
ID2      1
ARL4C    1
LITAF    1
CST7     1
SYNE2    1
METRNL   1

$`GZMB+ GZMH+ GZMK+`
         FDR
GZMA       1
CCL4       1
CD8A       1
CCL5       1
HLA-DPB1   1
CD74       1
HLA-DPA1   1
CLEC2B     1
HCST       1
HLA-DRB1   1

$`TIGIT+ CTLA4+`
                 FDR
MAF     8.773069e-10
IL32    1.000000e+00
DUSP4   1.000000e+00
ARID5B  1.000000e+00
CTLA4   1.000000e+00
CD2     1.000000e+00
NR3C1   1.000000e+00
RBPJ    1.000000e+00
S100A4  1.000000e+00
TNFRSF4 1.000000e+00

$`TOP2A+ CENPF+`
                  FDR
HMGB2    1.380647e-35
MKI67    1.380647e-35
STMN1    3.582537e-33
TOP2A    2.201522e-31
TUBA1B   4.271050e-31
CENPF    7.993601e-31
TUBB     3.977797e-29
HIST1H4C 5.383778e-25
HIST1H1B 1.749932e-23
H2AFZ    4.214657e-23

column <- paste0(celltype_name_pre,"_clusters_final")
column_labels <- paste0(celltype_name_pre,"_celltype")
sce_sub[[column]] <- factor(sce_sub[[column]],levels = order_celltypes)
dic <- unique(as.data.frame(colData(sce_sub)[,c(column,column_labels)])) %>% 
  dplyr::arrange(!!dplyr::sym(column))

# subcelltype_colors <- viridis::viridis(length(dic[[column]]))
subcelltype_colors <- rainbow(length(dic[[column]]))[order(as.character(dic[[column]]))]
names(subcelltype_colors) <- dic[[column]]
column_names <- paste0(dic[[column]], " - ",dic[[column_labels]])
dic$colors <- subcelltype_colors
dic$column_names <- column_names


markergenes_ls <- 
  list(`1`=c("PTPRC","CD3D","CD3E","CD3G","CD4","TIGIT","PDCD1","CXCL13","CTLA4","FOXP3","CCR7","LEF1","SELL","NKG7","GZMA","GZMM","CCL5","CD8A","CD8B","GZMK","GZMB","GZMH","GNLY","KLRD1","KLRC1","KLRF1","KLRB1", "IL7R")
  )

markergenes_n <- purrr::map(markergenes_ls, ~length(.x))
markergenes <- unlist(markergenes_ls)

clusters <- sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]
clucol <- dic$colors
names(clucol) <- dic$column_names 

samples <- sce_sub$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)

diagnosis <- sce_sub$Diagnosis_main
diagcol <- get_colors("diagnosis") 
diagcol <- diagcol[names(diagcol) %in% unique(sce_sub$Diagnosis_main)]


clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)

mataggr <- summarizeAssayByGroup(logcounts(sce_sub),clusters_samples, subset.row=markergenes)

tmpcoldat <- colData(sce_sub) %>%
  as.data.frame() %>%
  dplyr::mutate(tmp_clusters_final=!!dplyr::sym(paste0(celltype_name_pre,"_clusters_final"))) %>% 
  dplyr::left_join(data.frame(tmp_clusters_final=factor(order_celltypes),
           tmp_celltype_order=seq_along(unique(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]))),
           by="tmp_clusters_final") %>%
  dplyr::arrange(tmp_celltype_order, Diagnosis_main, Sample)

order_samples <- tmpcoldat %>%
  dplyr::select(tmp_clusters_final, Diagnosis_main, Sample) %>%
  unique() %>%
  dplyr::mutate(tmpname=paste0(stringr::str_replace_all(tmp_clusters_final,"/","\n"),"-",Diagnosis_main,"-", Sample)) %>%
  dplyr::pull()

mataggr <- mataggr[,order_samples]

colnamessplit <- stringr::str_split(colnames(mataggr),"-")
clustersaggr <- purrr::map_chr(colnamessplit,~.x[1]) 
diagcolsaggr <- purrr::map_chr(colnamessplit,~.x[2])
samplesaggr <- purrr::map_chr(colnamessplit,~.x[3])
tmpass <-  t(apply(assay(mataggr,"mean"),1,scale))
colnames(tmpass) <- colnames(assay(mataggr,"mean"))
assay(mataggr,"mean_scale") <- tmpass


  
order_samples <- colnames(mataggr)[order(colnames(mataggr))]

library(ComplexHeatmap)

ha <- ComplexHeatmap::HeatmapAnnotation(
    `Cell type` = factor(
      clustersaggr,
      levels = unique(clustersaggr),
      labels = paste0(unique(clustersaggr)," - ",purrr::map_chr(unique(clustersaggr),                                                        ~dic[[paste0(celltype_name_pre,"_celltype")]][dic[[paste0(celltype_name_pre,"_clusters_final")]] == .x]))),
    # Diagnosis=diagcolsaggr,
    Sample = samplesaggr,
    col=list(`Cell type`=clucol,Sample=samcol,Diagnosis=diagcol),
    annotation_legend_param=list(labels_gp = gpar(fontsize=16),
                                 title_gp = gpar(fontsize=20,fontface="bold")))



ht_opt$TITLE_PADDING = unit(c(4, 20), "points")
ht <- ComplexHeatmap::Heatmap(
  assay(mataggr,"mean_scale"), 
  heatmap_legend_param = list(title = "Scaled expression",fontsize=20,gap=unit(20, "mm"),
                              labels_gp = gpar(fontsize=16),
                              title_gp = gpar(fontsize=20,fontface="bold")),  
  cluster_rows = FALSE, 
  cluster_columns=FALSE,
  column_order=order_samples,
  show_column_names=FALSE,
  column_split=factor(clustersaggr,levels = unique(clustersaggr)),
  column_title = NULL,
  column_gap = unit(0.2, "mm"),
  row_split=factor(unlist(purrr::map(seq_along(markergenes_n),~rep(names(markergenes_n)[.x],markergenes_n[[.x]]))),
                   levels = unique(clustersaggr)),
  row_title = NULL
)
hm_ls[[celltype_name_pre]] <- ha %v% ht

hm_ls[[celltype_name_pre]] 

Past versions of heatmap_tc_new-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28

hm_ls[["tc_new"]] <- hm_ls[["tc"]] 
saveRDS(hm_ls[["tc_new"]],here::here("..","synovialscrnaseq","output","Suppl_Figure_4d.rds"))
png(here::here("..","synovialscrnaseq","output","Figures_Paper","Suppl_Figure_11_new.png"),width=26,height=18,res = 144, units = "in")
hm_ls[["tc_new"]] 
dev.off()

pdf 
  2 

celltype_name_pre <- "tc"
order_celltypes <- c(4,5,1,6,7,8,2,3,9)
used_clustering <- "tc_clusters_final"
# order_celltypes <- NULL

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

if(is.null(sce_sub[[paste0(celltype_name_pre,"_celltype")]])){
  sce_sub[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce_sub[[used_clustering]])
  sce_sub[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce_sub[[paste0(celltype_name_pre,"_celltype")]]))
}
format(object.size(sce_sub),units="Mb")

[1] "931.7 Mb"

colLabels(sce_sub) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
sce_sub_red <- sce_sub
sce_sub_red <- sce_sub_red[rowData(sce_sub_red)$is_hvg,]
colData(sce_sub_red) <- NULL
rowData(sce_sub_red) <- NULL
counts(sce_sub_red) <- NULL
assay(sce_sub_red,"reconstructed") <- NULL
metadata(sce_sub_red) <- list()
reducedDims(sce_sub_red) <- list()
colLabels(sce_sub_red) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
as(sce_sub_red, "SingleCellExperiment")

class: SingleCellExperiment 
dim: 2814 23169 
metadata(0):
assays(1): logcounts
rownames(2814): PERM1 HES4 ... MT-TL2 AL354822.1
rowData names(0):
colnames(23169): Syn_Bio_079.AAACCCATCCTGGTCT
  Syn_Bio_079.AAACCCATCGAACCAT ... Syn_Bio_050.TTTGGTTTCACATTGG-1
  Syn_Bio_050.TTTGTTGTCGCCCAGA-1
colData names(1): label
reducedDimNames(0):
altExpNames(0):

format(object.size(sce_sub_red),units="Mb")

[1] "47.3 Mb"

markers <- findMarkers(sce_sub_red, test.type = "wilcox", pval.type = "all", direction="up",
                       lfc=0.5, block=sce_sub$Sample) 

saveRDS(markers,here::here("output",paste0("findMarkers_results_v",analysis_version,"_",celltype_name_pre,".rds")))
topmarkers <- purrr::map(seq_along(markers), function(i){
  markers[[i]] %>% 
  as.data.frame() %>% 
  dplyr::arrange(FDR) %>% 
  dplyr::select(FDR) %>% 
  head(n=10)
})
names(topmarkers) <- names(markers)
topmarkers

$`CCR7high LEF1+ SELL+`
                FDR
RPS8   3.241573e-09
RPL21  1.346744e-04
RPS6   9.389380e-03
RPS13  2.518856e-02
RPL23  2.519568e-02
RPS3A  1.383866e-01
RPL13  1.795839e-01
EEF1B2 1.795839e-01
RPS18  2.208074e-01
RPS12  4.694525e-01

$`CCR7med LEF1low SELLlow`
         FDR
RGCC       1
ANXA1      1
KLF6       1
FOS        1
TSC22D3    1
DUSP1      1
TNFAIP3    1
LMNA       1
MCL1       1
PPP1R15A   1

$`CD3- NKG7- KLRB1+ IL7R+`
         FDR
FOSB       1
KLRB1      1
NFKBIA     1
AREG       1
JUND       1
TNFRSF18   1
TIMP1      1
BTG2       1
XCL1       1
LMNA       1

$`CD3- NKG7+ GNLY+`
       FDR
GNLY     1
TYROBP   1
FCER1G   1
GZMB     1
CD7      1
KLRD1    1
IER2     1
REL      1
PRF1     1
IFITM2   1

$`GZMB- GZMH- GZMK+`
       FDR
DUSP2    1
TUBA4A   1
PIK3R1   1
NR4A2    1
CXCR4    1
CREM     1
RGS1     1
GZMM     1
RUNX3    1
GZMK     1

$`GZMB+ GZMH+ GZMK- GNLY+`
       FDR
GZMH     1
CD52     1
RUNX3    1
LGALS1   1
ID2      1
ARL4C    1
LITAF    1
CST7     1
SYNE2    1
METRNL   1

$`GZMB+ GZMH+ GZMK+`
         FDR
GZMA       1
CCL4       1
CD8A       1
CCL5       1
HLA-DPB1   1
CD74       1
HLA-DPA1   1
CLEC2B     1
HCST       1
HLA-DRB1   1

$`TIGIT+ CTLA4+`
                 FDR
MAF     8.773069e-10
IL32    1.000000e+00
DUSP4   1.000000e+00
ARID5B  1.000000e+00
CTLA4   1.000000e+00
CD2     1.000000e+00
NR3C1   1.000000e+00
RBPJ    1.000000e+00
S100A4  1.000000e+00
TNFRSF4 1.000000e+00

$`TOP2A+ CENPF+`
                  FDR
HMGB2    1.380647e-35
MKI67    1.380647e-35
STMN1    3.582537e-33
TOP2A    2.201522e-31
TUBA1B   4.271050e-31
CENPF    7.993601e-31
TUBB     3.977797e-29
HIST1H4C 5.383778e-25
HIST1H1B 1.749932e-23
H2AFZ    4.214657e-23

column <- paste0(celltype_name_pre,"_clusters_final")
column_labels <- paste0(celltype_name_pre,"_celltype")
sce_sub[[column]] <- factor(sce_sub[[column]],levels = order_celltypes)
dic <- unique(as.data.frame(colData(sce_sub)[,c(column,column_labels)])) %>% 
  dplyr::arrange(!!dplyr::sym(column))

# subcelltype_colors <- viridis::viridis(length(dic[[column]]))
subcelltype_colors <- rainbow(length(dic[[column]]))[order(as.character(dic[[column]]))]
names(subcelltype_colors) <- dic[[column]]
column_names <- paste0(dic[[column]], " - ",dic[[column_labels]])
dic$colors <- subcelltype_colors
dic$column_names <- column_names


markergenes_ls <- purrr::map(topmarkers[dic[[paste0(celltype_name_pre,"_celltype")]]],~rownames(.x))
markergenes_ls <- markergenes_ls
names(markergenes_ls) <- names(markergenes_ls) #%>% stringr::str_replace_all("/","\n")
markergenes_n <- purrr::map(markergenes_ls, ~length(.x))
markergenes <- unlist(markergenes_ls)

clusters <- sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]
clucol <- dic$colors
names(clucol) <- dic$column_names 

samples <- sce_sub$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)

diagnosis <- sce_sub$Diagnosis_main
diagcol <- get_colors("diagnosis") 
diagcol <- diagcol[names(diagcol) %in% unique(sce_sub$Diagnosis_main)]


clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)

mataggr <- summarizeAssayByGroup(logcounts(sce_sub),clusters_samples, subset.row=markergenes)

tmpcoldat <- colData(sce_sub) %>%
  as.data.frame() %>%
  dplyr::mutate(tmp_clusters_final=!!dplyr::sym(paste0(celltype_name_pre,"_clusters_final"))) %>% 
  dplyr::left_join(data.frame(tmp_clusters_final=factor(order_celltypes),
           tmp_celltype_order=seq_along(unique(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]))),
           by="tmp_clusters_final") %>%
  dplyr::arrange(tmp_celltype_order, Diagnosis_main, Sample)

order_samples <- tmpcoldat %>%
  dplyr::select(tmp_clusters_final, Diagnosis_main, Sample) %>%
  unique() %>%
  dplyr::mutate(tmpname=paste0(stringr::str_replace_all(tmp_clusters_final,"/","\n"),"-",Diagnosis_main,"-", Sample)) %>%
  dplyr::pull()

mataggr <- mataggr[,order_samples]

colnamessplit <- stringr::str_split(colnames(mataggr),"-")
clustersaggr <- purrr::map_chr(colnamessplit,~.x[1]) 
diagcolsaggr <- purrr::map_chr(colnamessplit,~.x[2])
samplesaggr <- purrr::map_chr(colnamessplit,~.x[3])
tmpass <-  t(apply(assay(mataggr,"mean"),1,scale))
colnames(tmpass) <- colnames(assay(mataggr,"mean"))
assay(mataggr,"mean_scale") <- tmpass


  
order_samples <- colnames(mataggr)[order(colnames(mataggr))]

library(ComplexHeatmap)

ha <- ComplexHeatmap::HeatmapAnnotation(
    `Cell type` = factor(
      clustersaggr,
      levels = unique(clustersaggr),
      labels = paste0(unique(clustersaggr)," - ",purrr::map_chr(unique(clustersaggr),                                                        ~dic[[paste0(celltype_name_pre,"_celltype")]][dic[[paste0(celltype_name_pre,"_clusters_final")]] == .x]))),
    # Diagnosis=diagcolsaggr,
    Sample = samplesaggr,
    col=list(`Cell type`=clucol,Sample=samcol,Diagnosis=diagcol),
    annotation_legend_param=list(labels_gp = gpar(fontsize=16),
                                 title_gp = gpar(fontsize=20,fontface="bold")))



ht_opt$TITLE_PADDING = unit(c(4, 20), "points")
ht <- ComplexHeatmap::Heatmap(
  assay(mataggr,"mean_scale"), 
  heatmap_legend_param = list(title = "Scaled expression",fontsize=20,gap=unit(20, "mm"),
                              labels_gp = gpar(fontsize=16),
                              title_gp = gpar(fontsize=20,fontface="bold")),  
  cluster_rows = FALSE, 
  cluster_columns=FALSE,
  column_order=order_samples,
  show_column_names=FALSE,
  column_split=factor(clustersaggr,levels = unique(clustersaggr)),
  column_title = NULL,
  column_gap = unit(0.2, "mm"),
  row_split=factor(unlist(purrr::map(seq_along(markergenes_n),~rep(names(markergenes_n)[.x],markergenes_n[[.x]]))),
                   levels = unique(clustersaggr)),
  row_title = NULL
)
hm_ls[[celltype_name_pre]] <- ha %v% ht

hm_ls[[celltype_name_pre]] 

Past versions of heatmap_tc-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28
0f8368f	Reto Gerber	2022-05-20
5a6aa2a	Reto Gerber	2022-04-25
b5b139f	Reto Gerber	2022-03-29
7d99571	Reto Gerber	2022-03-21

findMarkers - EC

celltype_name_pre <- "ec"
order_celltypes <- c(2,6,3,5,8,1,4,7)
used_clustering <- "ec_clusters_final"
# order_celltypes <- NULL

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

if(is.null(sce_sub[[paste0(celltype_name_pre,"_celltype")]])){
  sce_sub[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce_sub[[used_clustering]])
  sce_sub[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce_sub[[paste0(celltype_name_pre,"_celltype")]]))
}
format(object.size(sce_sub),units="Mb")

[1] "720.2 Mb"

colLabels(sce_sub) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
sce_sub_red <- sce_sub
sce_sub_red <- sce_sub_red[rowData(sce_sub_red)$is_hvg,]
colData(sce_sub_red) <- NULL
rowData(sce_sub_red) <- NULL
counts(sce_sub_red) <- NULL
assay(sce_sub_red,"reconstructed") <- NULL
metadata(sce_sub_red) <- list()
reducedDims(sce_sub_red) <- list()
colLabels(sce_sub_red) <- colData(sce_sub)[[paste0(celltype_name_pre,"_celltype")]]
as(sce_sub_red, "SingleCellExperiment")

class: SingleCellExperiment 
dim: 3809 9378 
metadata(0):
assays(1): logcounts
rownames(3809): SAMD11 HES4 ... BX004987.1 AL354822.1
rowData names(0):
colnames(9378): Syn_Bio_079.AAACCCACACATCCCT
  Syn_Bio_079.AAACGCTGTCGGCCTA ... Syn_Bio_050.TTTCGATAGGGACCAT-1
  Syn_Bio_050.TTTGATCTCCCTCTCC-1
colData names(1): label
reducedDimNames(0):
altExpNames(0):

format(object.size(sce_sub_red),units="Mb")

[1] "45.4 Mb"

markers <- findMarkers(sce_sub_red, test.type = "wilcox", pval.type = "all", direction="up",
                       lfc=0.5, block=sce_sub$Sample) 

saveRDS(markers,here::here("output",paste0("findMarkers_results_v",analysis_version,"_",celltype_name_pre,".rds")))
topmarkers <- purrr::map(seq_along(markers), function(i){
  markers[[i]] %>% 
  as.data.frame() %>% 
  dplyr::arrange(FDR) %>% 
  dplyr::select(FDR) %>% 
  head(n=10)
})
names(topmarkers) <- names(markers)
topmarkers

$`ACKRhigh IL1R1+ CLU+ SELE+ venous`
        FDR
CTSC      1
CCL14     1
NPC2      1
FABP5     1
MGP       1
CCN2      1
POSTN     1
DUSP23    1
PIM3      1
TNFSF10   1

$`ACKRhigh IL1R1+ CLU+ SELEhigh TNFAIP3+ venous`
                 FDR
SELE    1.129818e-06
ICAM1   1.000000e+00
CCL2    1.000000e+00
C2CD4B  1.000000e+00
NFKBIA  1.000000e+00
IRF1    1.000000e+00
KLF4    1.000000e+00
IER3    1.000000e+00
BHLHE40 1.000000e+00
MT2A    1.000000e+00

$`ACKRhigh IL1R1+ CLU+ VCAN+ venous`
        FDR
TXNIP     1
CLU       1
ID3       1
GIMAP7    1
NKTR      1
RAMP3     1
TSC22D3   1
IGFBP4    1
DAAM1     1
ADIRF     1

$`ACKRmed CLU- SPARChigh`
       FDR
ITM2A    1
COTL1    1
CCND1    1
PRSS23   1
GASK1B   1
CTHRC1   1
LGALS1   1
MCAM     1
ITGA6    1
HSPA5    1

$`GJA4+ CLDN5+ arterial`
                  FDR
CXCL12   5.932051e-18
SRP14    3.805272e-16
SRGN     1.279765e-13
PODXL    2.956143e-12
SLC9A3R2 3.188914e-12
IFI27    4.897143e-11
PPA1     5.478173e-11
EFNB2    2.368980e-09
ICAM2    2.611863e-08
GJA4     4.971547e-08

$`KDR+ SPP1+ SPARChigh capillary`
        FDR
FLT1      1
PLVAP     1
COL4A1    1
KDR       1
MLEC      1
MEF2C     1
RGCC      1
GSN       1
ETS1      1
C1orf54   1

$`LYVE1+ PROX1+ CCL21+ lymphatic`
                 FDR
MMRN1   4.333056e-06
TFPI    4.333056e-06
TFF3    4.333056e-06
EFEMP1  5.559318e-06
CCL21   5.559318e-06
LYVE1   5.559318e-06
PDPN    5.220037e-04
COLEC12 7.220345e-04
LAPTM5  9.048423e-04
GYPC    9.949943e-04

$`TOP2A+ CENPF+ proliferating`
                  FDR
CDK1     1.315584e-08
MKI67    1.785725e-08
TPX2     1.866667e-08
CENPF    3.977464e-08
TOP2A    4.828200e-07
HMGB2    7.187750e-07
PBK      3.169123e-06
PTTG1    9.201280e-06
HMGN2    1.278876e-05
HIST1H4C 4.326646e-05

column <- paste0(celltype_name_pre,"_clusters_final")
column_labels <- paste0(celltype_name_pre,"_celltype")
sce_sub[[column]] <- factor(sce_sub[[column]],levels = order_celltypes)
dic <- unique(as.data.frame(colData(sce_sub)[,c(column,column_labels)])) %>%
  dplyr::arrange(!!dplyr::sym(column))

# subcelltype_colors <- viridis::viridis(length(dic[[column]]))
subcelltype_colors <- rainbow(length(dic[[column]]))[order(as.character(dic[[column]]))]
names(subcelltype_colors) <- dic[[column]]
column_names <- paste0(dic[[column]], " - ",dic[[column_labels]])
dic$colors <- subcelltype_colors
dic$column_names <- column_names

markergenes_ls <-
  list(
       `1`=c("PECAM1"),
       `2`=c("GJA4","SEMA3G","IGFBP3","CXCL12","CLDN5","SRGN","LTBP4","RHOB","PLPP1","EFNB2","ARL15","CRYBG3","MECOM","SLC9A3R2","ICAM2","PPA1"),
       `6`=c("SPP1","RFLNB","PLPP3","RGCC","EXOC3L2","ITIH5","MLEC","COL13A1","KDR","PLVAP","COL15A1","COL4A1","COL4A2","SPARC","FLT1","RGCC"),
       `3`=c("VWF","HLA-DRB1","HLA-DRA","HLA-DPA1","HLA-DPB1","SELP","NR2F2","ACKR1","DAAM1","FBLN2","CLU","ADIRF","SNCG","LIFR","IL33","IL1R1","IRF1","CCL2","SELE","SOCS3","NFKBIA","ICAM1","SOD2","IL6","VCAN","C2CD4B","CCL2","CDKN1A","MT2A","TNFAIP3"),
       `5`=c("IGF1","LAPTM5","MAF","PROX1","PDPN","AKAP12","LYVE1","EFEMP1","TFF3","CCL21","MARCKS","COLEC12","TFPI"),
       `8`=c("CENPF","MKI67","TOP2A")

)

markergenes_n <- purrr::map(markergenes_ls, ~length(.x))
markergenes <- unlist(markergenes_ls)

clusters <- sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]
clucol <- dic$colors
names(clucol) <- dic$column_names 

samples <- sce_sub$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)

diagnosis <- sce_sub$Diagnosis_main
diagcol <- get_colors("diagnosis") 
diagcol <- diagcol[names(diagcol) %in% unique(sce_sub$Diagnosis_main)]


clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)

mataggr <- summarizeAssayByGroup(logcounts(sce_sub),clusters_samples, subset.row=markergenes)

tmpcoldat <- colData(sce_sub) %>%
  as.data.frame() %>%
  dplyr::mutate(tmp_clusters_final=!!dplyr::sym(paste0(celltype_name_pre,"_clusters_final"))) %>% 
  dplyr::left_join(data.frame(tmp_clusters_final=factor(order_celltypes),
           tmp_celltype_order=seq_along(unique(sce_sub[[paste0(celltype_name_pre,"_clusters_final")]]))),
           by="tmp_clusters_final") %>%
  dplyr::arrange(tmp_celltype_order, Diagnosis_main, Sample)

order_samples <- tmpcoldat %>%
  dplyr::select(tmp_clusters_final, Diagnosis_main, Sample) %>%
  unique() %>%
  dplyr::mutate(tmpname=paste0(stringr::str_replace_all(tmp_clusters_final,"/","\n"),"-",Diagnosis_main,"-", Sample)) %>%
  dplyr::pull()

mataggr <- mataggr[,order_samples]

colnamessplit <- stringr::str_split(colnames(mataggr),"-")
clustersaggr <- purrr::map_chr(colnamessplit,~.x[1]) 
diagcolsaggr <- purrr::map_chr(colnamessplit,~.x[2])
samplesaggr <- purrr::map_chr(colnamessplit,~.x[3])
tmpass <-  t(apply(assay(mataggr,"mean"),1,scale))
colnames(tmpass) <- colnames(assay(mataggr,"mean"))
assay(mataggr,"mean_scale") <- tmpass


  
order_samples <- colnames(mataggr)[order(colnames(mataggr))]

library(ComplexHeatmap)

ha <- ComplexHeatmap::HeatmapAnnotation(
    `Cell type` = factor(
      clustersaggr,
      levels = unique(clustersaggr),
      labels = paste0(unique(clustersaggr)," - ",purrr::map_chr(unique(clustersaggr),                                                        ~dic[[paste0(celltype_name_pre,"_celltype")]][dic[[paste0(celltype_name_pre,"_clusters_final")]] == .x]))),
    # Diagnosis=diagcolsaggr,
    Sample = samplesaggr,
    col=list(`Cell type`=clucol,Sample=samcol,Diagnosis=diagcol),
    annotation_legend_param=list(labels_gp = gpar(fontsize=16),
                                 title_gp = gpar(fontsize=20,fontface="bold")))



ht_opt$TITLE_PADDING = unit(c(4, 20), "points")
ht <- ComplexHeatmap::Heatmap(
  assay(mataggr,"mean_scale"), 
  heatmap_legend_param = list(title = "Scaled expression",fontsize=20,gap=unit(20, "mm"),
                              labels_gp = gpar(fontsize=16),
                              title_gp = gpar(fontsize=20,fontface="bold")),  
  cluster_rows = FALSE, 
  cluster_columns=FALSE,
  column_order=order_samples,
  show_column_names=FALSE,
  column_split=factor(clustersaggr,levels = unique(clustersaggr)),
  column_title = NULL,
  column_gap = unit(0.2, "mm"),
  row_split=factor(unlist(purrr::map(seq_along(markergenes_n),~rep(names(markergenes_n)[.x],markergenes_n[[.x]]))),
                   levels = unique(clustersaggr)),
  row_title = NULL
)
hm_ls[[celltype_name_pre]] <- ha %v% ht

hm_ls[[celltype_name_pre]] 

Past versions of heatmap_ec-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28
0f8368f	Reto Gerber	2022-05-20
5a6aa2a	Reto Gerber	2022-04-25
b5b139f	Reto Gerber	2022-03-29
7d99571	Reto Gerber	2022-03-21

supplementary figure 11

celltype_name_pre <- "tc"
order_celltypes <- c(4,5,1,6,7,8,2,3,9)
used_clustering <- "tc_clusters_final"
tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,"_invivo",".rds")
sce_sub_tc <- readRDS(file = here::here("output",tmpfilename))
sce <- sce_sub_tc
if(is.null(sce[[paste0(celltype_name_pre,"_celltype")]])){
  sce[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce[[used_clustering]])
  sce[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce[[paste0(celltype_name_pre,"_celltype")]]))
}
rownames(sce)[stringr::str_detect(rownames(sce),"PDCD1")]

[1] "PDCD1"    "PDCD10"   "PDCD1LG2" "PDCD11"  

celltype_centers <- get_celltype_centers(sce, "UMAP_corrected", "tc_clusters_final")

celltype_centers %<>% move_label(0.5,"Y","2")
celltype_centers %<>% move_label(1,"X","3")
celltype_centers %<>% move_label(1,"X","8")
celltype_centers %<>% move_label(0.5,"Y","8")
celltype_centers %<>% move_label(1,"X","9")

genes_to_plot <- c("TIGIT","CTLA4","FOXP3", "PDCD1","CXCL13")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]

pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x)
  })
pl8b <- ggpubr::ggarrange(plotlist=pltls, ncol = 5, nrow = 1, font.label=list(size=26))

genes_to_plot <- c("TOP2A","CENPF")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]

pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x)
  })
pl8c <- ggpubr::ggarrange(plotlist=pltls, ncol = 5, nrow = 1, font.label=list(size=26))

genes_to_plot <- c("GZMK", "GZMB", "GZMH","GNLY")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]

pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x)
  })
pl8d <- ggpubr::ggarrange(plotlist=pltls, ncol = 5, nrow = 1, font.label=list(size=26))

plt <- ggpubr::ggarrange(
    pl8c,
    grid.grabExpr(draw(hm_ls[["tc"]],align_annotation_legend="heatmap_center",newpage = FALSE, merge_legends=TRUE,align_heatmap_legend="heatmap_center",padding = unit(c(10, 10, 10, 10), "mm")), width = 22, height = 20,wrap=TRUE),
  pl8b,
  pl8d,
  labels = "auto", ncol=1, nrow=4, font.label=list(size=26), heights = c(2,7,2,2)
)

figname <- "Suppl_Figure_11"
width <- 22
height <- 26
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)

plt <- plt +
  labs(title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$"))) +
  theme(plot.title = element_text(size=10*downscale))

tiff(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")),width=width,height=height,res = initres, units = "in", compression="zip")
plt + theme(plot.title = element_text(size=10*downscale, family="Arial"))
dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))

jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
plt + theme(plot.title = element_text(size=10*downscale, family="Arial"))
dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg"))))

pdf(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".pdf")),width=width,height=height)
plt
dev.off()

pdf 
  2 

Supplementary Figure 12

celltype_name_pre <- "sf"
# order_celltypes <- c(5,4,2,6,8,1,7,3)
used_clustering <- "sf_clusters_final"
# order_celltypes <- NULL
order_celltypes <- c(3,7,5,4,6,1,2)
tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,"_invivo",".rds")
sce_sub_sf <- readRDS(file = here::here("output",tmpfilename))
sce <- sce_sub_sf
if(is.null(sce[[paste0(celltype_name_pre,"_celltype")]])){
  sce[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce[[used_clustering]])
  sce[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce[[paste0(celltype_name_pre,"_celltype")]]))
}

column <- "sf_clusters_final"
column_labels <- "sf_celltype"
aggr_by="Sample"
facet_by="Sample_prep"
fill_lab = "Fibroblasts"
sce[[column]] <- factor(sce[[column]],levels = order_celltypes)
dic_sf <- unique(as.data.frame(colData(sce)[,c(column,column_labels)])) %>% 
  dplyr::arrange(!!dplyr::sym(column))
# subcelltype_colors <- viridis::viridis(length(dic_sf[[column]]))
subcelltype_colors <- rainbow(length(dic_sf[[column]]))[order(as.character(dic_sf[[column]]))]


pl9a <- scater::plotExpression(sce, c("PRG4","THY1"), 
                       x=column,colour_by=column, ncol=5) +
    scale_color_manual(values=subcelltype_colors, breaks=names(subcelltype_colors), labels=column_names) +
  main_plot_theme() +
  labs(x="")

Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.

celltype_centers <- get_celltype_centers(sce, "UMAP_corrected", column)
genes_to_plot <- c("TOP2A","CENPF")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]

pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x) +
    coord_fixed()
  })
pl9b <- ggpubr::ggarrange(plotlist=pltls, ncol = 2, nrow = 1, font.label=list(size=26))


plt <- ggpubr::ggarrange(pl9a,
                         pl9b, 
                         ncol = 1, nrow = 2,labels = "auto", heights = c(2,2),
                         font.label=list(size=26))
# plt

figname <- "Suppl_Figure_12"
width <- 16
height <- 16
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)

plt <- plt +
  labs(title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$"))) +
  theme(plot.title = element_text(size=10*downscale))

tiff(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")),width=width,height=height,res = initres, units = "in", compression="zip")
plt + theme(plot.title = element_text(size=10*downscale, family="Arial"))
dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))

jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
plt + theme(plot.title = element_text(size=10*downscale, family="Arial"))
dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg"))))

pdf(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".pdf")),width=width,height=height)
plt
dev.off()

pdf 
  2 

Supplementary Figure 13

hm <- hm_ls[["sf_new"]] 

figname <- "Suppl_Figure_13"
width <- 26 
height <- 22
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)


tiff(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")),width=width,height=height,res = initres, units = "in", compression="zip")
draw(hm,
   column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale,fontfamily="Arial"))

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))

jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
draw(hm,
   column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale,fontfamily="Arial"))

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg"))))

pdf(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".pdf")),width=width,height=height)
draw(hm,
     column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
     column_title_gp=grid::gpar(fontsize=10*downscale))
dev.off()

pdf 
  2 

Supplementary Figure 14

celltype_name_pre <- "sf"
# order_celltypes <- c(5,4,2,6,8,1,7,3)
used_clustering <- "sf_clusters_final"
# order_celltypes <- NULL
order_celltypes <- c(3,7,5,4,6,1,2)

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,"_invivo",".rds")
sce_sub_sf <- readRDS(file = here::here("output",tmpfilename))
sce <- sce_sub_sf
if(is.null(sce[[paste0(celltype_name_pre,"_celltype")]])){
  sce[[paste0(celltype_name_pre,"_clusters_final")]] <- as.integer(sce[[used_clustering]])
  sce[[paste0(celltype_name_pre,"_celltype")]] <- as.integer(sce[[paste0(celltype_name_pre,"_clusters_final")]])
}
if(is.null(order_celltypes)){
  order_celltypes <- sort(unique(sce[[paste0(celltype_name_pre,"_celltype")]]))
}

column <- "sf_clusters_final"
column_labels <- "sf_celltype"
aggr_by="Sample"
facet_by="Sample_prep"
fill_lab = "Fibroblasts"
sce[[column]] <- factor(sce[[column]],levels = order_celltypes)
dic_sf <- unique(as.data.frame(colData(sce)[,c(column,column_labels)])) %>% 
  dplyr::arrange(!!dplyr::sym(column))
# subcelltype_colors <- viridis::viridis(length(dic_sf[[column]]))
subcelltype_colors <- rainbow(length(dic_sf[[column]]))[order(as.character(dic_sf[[column]]))]

celltype_centers <- get_celltype_centers(sce, "UMAP_corrected", column)

genes_to_plot <- c("CLIC5","HBEGF")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]
pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x) +
    coord_fixed()
  })
pl11a <- ggpubr::ggarrange(plotlist=pltls, ncol = 2, nrow = 1, font.label=list(size=26))

genes_to_plot <- c("HLA-DRA","CD74","HLA-DRB1","HLA-DPB1")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]
pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x) +
    coord_fixed()
  })
pl11b <- ggpubr::ggarrange(plotlist=pltls, ncol = 2, nrow = 2, font.label=list(size=26))

genes_to_plot <- c("NOTCH3","IL6")
genes_to_plot <- genes_to_plot[genes_to_plot %in% rownames(sce)]
pltls <- purrr::map(genes_to_plot, ~{
  plotReducedDim(sce,"UMAP_corrected",colour_by = .x, point_size=0.1) +
    labs(x="UMAP 1", y="UMAP 2") +
    geom_label(aes(V1,V2,label=V3),data=celltype_centers) +
    main_plot_theme() +
    labs(title=.x) +
    coord_fixed()
  })
pl11c <- ggpubr::ggarrange(plotlist=pltls, ncol = 2, nrow = 1, font.label=list(size=26))


plt <- ggpubr::ggarrange(pl11a,
                         pl11b, 
                         pl11c, 
                         ncol = 1, nrow = 3,labels = "auto",
                         font.label=list(size=26), heights = c(1,2,1),align = "hv")
plt

Past versions of supple_fig_11-1.jpeg

Version	Author	Date
58eeb06	Reto Gerber	2023-05-30
e786402	Reto Gerber	2022-05-28

figname <- "Suppl_Figure_14"
width <- 16
height <- 20
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)

plt <- plt +
  labs(title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$"))) +
  theme(plot.title = element_text(size=10*downscale))

tiff(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")),width=width,height=height,res = initres, units = "in", compression="zip")
plt + theme(plot.title = element_text(size=10*downscale, family="Arial"))
dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))

jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
plt + theme(plot.title = element_text(size=10*downscale, family="Arial"))
dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg"))))

pdf(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".pdf")),width=width,height=height)
plt
dev.off()

pdf 
  2 

Supplementary Figure 15

figname <- "Suppl_Figure_15"
width <- 26 
height <- 22
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)


tiff(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")),width=width,height=height,res = initres, units = "in", compression="zip")
   draw(hm_ls[["mp"]], ht_gap = unit(0, "mm"), merge_legends=TRUE, annotation_legend_list = lgd_list,align_annotation_legend="heatmap_center",align_heatmap_legend="heatmap_center",column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale,fontfamily="Arial"))

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))

jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
draw(hm_ls[["mp"]], ht_gap = unit(0, "mm"), merge_legends=TRUE, annotation_legend_list = lgd_list,align_annotation_legend="heatmap_center",align_heatmap_legend="heatmap_center",column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale,fontfamily="Arial"))

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg"))))

pdf(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".pdf")),width=width,height=height)
draw(hm_ls[["mp"]], ht_gap = unit(0, "mm"), merge_legends=TRUE, annotation_legend_list = lgd_list,align_annotation_legend="heatmap_center",align_heatmap_legend="heatmap_center",column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale))
dev.off()

pdf 
  2 

Supplementary Figure 17

hm <- hm_ls[["ec"]] 

figname <- "Suppl_Figure_17"
width <- 26 
height <- 18
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)


tiff(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")),width=width,height=height,res = initres, units = "in", compression="zip")
draw(hm,
   column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale,fontfamily="Arial"))

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))

jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
draw(hm,
   column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
   column_title_gp=grid::gpar(fontsize=10*downscale,fontfamily="Arial"))

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family 'Arial' not found in PostScript font database

dev.off()

pdf 
  2 

system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg"))))

pdf(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".pdf")),width=width,height=height)
draw(hm,
     column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
     column_title_gp=grid::gpar(fontsize=10*downscale))
dev.off()

pdf 
  2 

Session information

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] gdtools_0.2.3                  ComplexHeatmap_2.6.2          
 [3] tidySingleCellExperiment_1.0.0 scuttle_1.0.4                 
 [5] scran_1.18.7                   scater_1.18.6                 
 [7] ggplot2_3.3.3                  SingleCellExperiment_1.12.0   
 [9] SummarizedExperiment_1.20.0    Biobase_2.50.0                
[11] GenomicRanges_1.42.0           GenomeInfoDb_1.26.7           
[13] IRanges_2.24.1                 S4Vectors_0.28.1              
[15] BiocGenerics_0.36.1            MatrixGenerics_1.2.1          
[17] matrixStats_0.58.0             magrittr_2.0.1                
[19] workflowr_1.6.2               

loaded via a namespace (and not attached):
  [1] readxl_1.3.1              backports_1.2.1          
  [3] circlize_0.4.12           systemfonts_1.0.1        
  [5] igraph_1.2.6              lazyeval_0.2.2           
  [7] BiocParallel_1.24.1       digest_0.6.27            
  [9] htmltools_0.5.1.1         viridis_0.5.1            
 [11] magick_2.6.0              fansi_0.4.2              
 [13] cluster_2.1.1             openxlsx_4.2.3           
 [15] limma_3.46.0              svglite_1.2.3.2          
 [17] colorspace_2.0-0          haven_2.3.1              
 [19] xfun_0.21                 dplyr_1.0.4              
 [21] crayon_1.4.1              RCurl_1.98-1.2           
 [23] jsonlite_1.7.2            glue_1.4.2               
 [25] gtable_0.3.0              zlibbioc_1.36.0          
 [27] XVector_0.30.0            GetoptLong_1.0.5         
 [29] DelayedArray_0.16.3       car_3.0-10               
 [31] BiocSingular_1.6.0        shape_1.4.5              
 [33] abind_1.4-5               scales_1.1.1             
 [35] DBI_1.1.1                 edgeR_3.32.1             
 [37] rstatix_0.7.0             Rcpp_1.0.6               
 [39] viridisLite_0.3.0         clue_0.3-58              
 [41] dqrng_0.2.1               foreign_0.8-81           
 [43] rsvd_1.0.3                htmlwidgets_1.5.3        
 [45] httr_1.4.2                RColorBrewer_1.1-2       
 [47] ellipsis_0.3.1            pkgconfig_2.0.3          
 [49] farver_2.0.3              locfit_1.5-9.4           
 [51] here_1.0.1                labeling_0.4.2           
 [53] tidyselect_1.1.0          rlang_0.4.10             
 [55] later_1.1.0.1             cellranger_1.1.0         
 [57] munsell_0.5.0             tools_4.0.3              
 [59] cli_2.3.0                 generics_0.1.0           
 [61] broom_0.7.4               evaluate_0.14            
 [63] stringr_1.4.0             yaml_2.2.1               
 [65] RhpcBLASctl_0.20-137      knitr_1.31               
 [67] fs_1.5.0                  zip_2.1.1                
 [69] purrr_0.3.4               sparseMatrixStats_1.2.1  
 [71] whisker_0.4               compiler_4.0.3           
 [73] beeswarm_0.2.3            plotly_4.9.3             
 [75] curl_4.3                  png_0.1-7                
 [77] ggsignif_0.6.0            tibble_3.0.6             
 [79] statmod_1.4.35            stringi_1.5.3            
 [81] highr_0.8                 forcats_0.5.1            
 [83] lattice_0.20-41           bluster_1.0.0            
 [85] Matrix_1.3-2              vctrs_0.3.6              
 [87] pillar_1.4.7              lifecycle_1.0.0          
 [89] GlobalOptions_0.1.2       BiocNeighbors_1.8.2      
 [91] data.table_1.13.6         cowplot_1.1.1            
 [93] bitops_1.0-6              irlba_2.3.3              
 [95] httpuv_1.5.5              R6_2.5.0                 
 [97] promises_1.2.0.1          gridExtra_2.3            
 [99] rio_0.5.16                vipor_0.4.5              
[101] assertthat_0.2.1          rprojroot_2.0.2          
[103] rjson_0.2.20              withr_2.4.1              
[105] GenomeInfoDbData_1.2.4    hms_1.0.0                
[107] beachmat_2.6.4            tidyr_1.1.2              
[109] rmarkdown_2.6             DelayedMatrixStats_1.12.3
[111] carData_3.0-4             Cairo_1.5-12.2           
[113] git2r_0.28.0              ggpubr_0.4.0             
[115] ggbeeswarm_0.6.0