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Knit directory: synovialscrnaseq/
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Unstaged changes:
Modified: analysis/scRNAseq_combined_06_Figures.Rmd
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| 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 |
| Rmd | 27d00c3 | Reto Gerber | 2021-10-20 | Add untracked files, update |
suppressPackageStartupMessages({
library(ggplot2)
library(SingleCellExperiment)
library(scater)
library(scran)
library(scuttle)
library(magrittr)
})
n_workers <- 10
RhpcBLASctl::blas_set_num_threads(n_workers)
source(here::here("code","utilities_plots.R"))
analysis_version <- 7
here::here()[1] "/home/retger/Synovial/synovialscrnaseq"set.seed(100)sce_main <- readRDS(file =here::here("output",paste0("syn_v",analysis_version,"_sce_hvg_cms_doublet_subcluster_invivo.rds")))
if(is.null(sce_main$main_celltype)){
sce_main$main_celltype <- sce_main$main_celltype_cellid
}Loading required package: tidySingleCellExperiment
Attaching package: 'tidySingleCellExperiment'The following object is masked from 'package:magrittr':
extractThe following object is masked from 'package:IRanges':
sliceThe following object is masked from 'package:S4Vectors':
renameThe following object is masked from 'package:matrixStats':
countThe following object is masked from 'package:stats':
filterformat(object.size(sce_main),units="Mb")[1] "6883.6 Mb"colLabels(sce_main) <- colData(sce_main)$main_celltype
sce_main_red <- sce_main
sce_main_red <- sce_main_red[rowData(sce_main_red)$is_hvg,]
colData(sce_main_red) <- NULL
colLabels(sce_main_red) <- colData(sce_main)$main_celltype
rowData(sce_main_red) <- NULL
counts(sce_main_red) <- NULL
assay(sce_main_red,"reconstructed") <- NULL
metadata(sce_main_red) <- list()
reducedDims(sce_main_red) <- list()
as(sce_main_red, "SingleCellExperiment")class: SingleCellExperiment
dim: 3291 102758
metadata(0):
assays(1): logcounts
rownames(3291): PERM1 HES4 ... RPS4Y1 MT-TR
rowData names(0):
colnames(102758): Syn_Bio_079.AAACCCAAGAGGCTGT
Syn_Bio_079.AAACCCACACATCCCT ... Syn_Bio_050.TTTGTTGGTCTGTAAC-1
Syn_Bio_050.TTTGTTGTCGCCCAGA-1
colData names(1): label
reducedDimNames(0):
altExpNames(0):format(object.size(sce_main_red),units="Mb")[1] "534.8 Mb"markers <- findMarkers(sce_main_red, test.type = "wilcox", pval.type = "all", direction="up",
lfc=0.5, block=sce_main$Sample)
saveRDS(markers,here::here("output",paste0("findMarkers_results_v",analysis_version,"_main.rds")))markers <- readRDS(here::here("output",paste0("findMarkers_results_v",analysis_version,"_main.rds")))topmarkers <- purrr::map(seq_along(markers), function(i){
markers[[i]] %>%
as.data.frame() %>%
dplyr::arrange(FDR) %>%
dplyr::select(FDR) %>%
head(n=10)
})
names(topmarkers) <- names(markers)
topmarkers$`B cells`
FDR
MS4A1 3.303241e-30
BANK1 2.858290e-24
TNFRSF13C 2.787433e-14
CD37 1.256120e-07
RALGPS2 3.068297e-03
CD79A 1.000000e+00
LTB 1.000000e+00
POU2F2 1.000000e+00
CD83 1.000000e+00
BIRC3 1.000000e+00
$`Dendritic cells`
FDR
HLA-DPB1 1.370881e-56
HLA-DPA1 1.370881e-56
HLA-DRA 1.639417e-56
HLA-DRB1 2.363214e-56
HLA-DQA1 4.509545e-56
HLA-DQB1 7.764549e-53
CST3 4.665384e-38
LGALS2 2.951278e-34
CPVL 1.100839e-31
CD74 5.035648e-24
$`Endothelial cells`
FDR
HSPG2 1.160748e-71
GNG11 1.160748e-71
ENG 6.552932e-71
TM4SF1 3.109673e-70
IFI27 2.034223e-67
AQP1 6.191734e-66
NPDC1 1.438575e-63
CLEC14A 5.437018e-63
RAMP2 1.421321e-61
COL15A1 8.094352e-61
$Fibroblasts
FDR
LUM 1.734588e-72
DCN 2.079124e-69
FN1 2.079124e-69
C1S 2.837773e-65
CCDC80 4.030590e-65
MMP2 1.383558e-64
COL1A2 1.394713e-64
COL3A1 2.623164e-63
LRP1 1.468709e-58
HTRA1 1.468709e-58
$Macrophages
FDR
CTSB 5.422159e-47
CD68 2.465772e-35
CTSL 1.084050e-26
CTSZ 5.945712e-23
MAFB 6.219787e-23
CD14 6.503482e-17
CYBB 1.955266e-12
PSAP 6.179828e-09
MS4A7 7.091365e-07
KCTD12 2.761744e-02
$`Mast cells`
FDR
CPA3 7.654907e-33
TPSB2 8.078864e-32
TPSAB1 8.078864e-32
MS4A2 1.763697e-27
HPGDS 1.019243e-23
HPGD 3.151552e-22
CTSG 8.504468e-22
ANXA1 4.688402e-16
LTC4S 2.945854e-14
HDC 1.743379e-13
$Neutrophils
FDR
S100A9 0.001104826
NAMPT 0.001104826
IFITM2 0.001104826
S100A8 0.001104826
FTH1 0.001104826
ITM2B 0.001104826
SRGN 0.001104826
SAT1 0.001446963
G0S2 0.001694698
SOD2 0.002518363
$`Pericytes/Mural cells`
FDR
CALD1 1.593488e-39
MYL9 5.735033e-37
TAGLN 1.950801e-35
IGFBP7 1.219012e-33
C11orf96 1.392688e-28
NOTCH3 2.579085e-28
TPM1 2.579085e-28
COL18A1 5.341102e-27
ADIRF 7.552298e-27
NR2F2 4.858776e-26
$Plasmablasts
FDR
IGHG1 5.278828e-07
PRDM1 5.278828e-07
IGHG3 5.278828e-07
MZB1 5.278828e-07
POU2AF1 1.941103e-06
PRDX4 6.310224e-06
CCND2 1.439282e-03
ZBP1 2.725350e-03
TENT5C 1.054154e-02
HIST1H1C 1.878322e-02
$`Plasmacytoid DCs`
FDR
GZMB 3.111760e-23
PLD4 2.665760e-20
CLIC3 2.665760e-20
PLAC8 5.130436e-18
SOX4 2.604757e-14
SEMA7A 3.479245e-14
IRF8 1.356418e-12
LILRA4 4.308873e-12
SCT 6.845314e-12
GPR183 1.354720e-11
$`T cells/NK cells`
FDR
IL32 3.981844e-61
CD3D 1.655958e-41
CD3E 1.897720e-33
TRBC2 1.000297e-20
BCL11B 4.064669e-18
ZFP36L2 5.341862e-16
DUSP2 1.412035e-15
CD3G 3.667718e-11
HCST 3.590314e-10
SPOCK2 1.498644e-07markergenes_ls <- purrr::map(topmarkers,~rownames(.x))
markergenes_ls <- markergenes_ls[celltype_order()]
names(markergenes_ls) <- names(markergenes_ls) %>% stringr::str_replace_all("/","\n")
markergenes_n <- purrr::map(markergenes_ls, ~length(.x))
markergenes <- unlist(markergenes_ls)
fdr_topmarkers <- unlist(topmarkers[celltype_order()])
names(fdr_topmarkers) <- markergenes
clusters <- sce_main$main_celltype %>% stringr::str_replace_all("/","\n")
clucol <- get_colors("main_celltype")
clucol <- clucol[celltype_order()]
names(clucol) <- names(clucol) %>% stringr::str_replace_all("/","\n")
samples <- sce_main$Sample
samcol <- sample_cols(unique(samples))
names(samcol) <- unique(samples)
diagnosis <- sce_main$Diagnosis_main
diagcol <- get_colors("diagnosis")
diagcol <- diagcol[names(diagcol) %in% unique(sce_main$Diagnosis_main)]
clusters_samples <- paste0(clusters,"-",diagnosis,"-", samples)
mataggr <- summarizeAssayByGroup(logcounts(sce_main),clusters_samples, subset.row=markergenes)
# order
tmpcoldat <- colData(sce_main) %>%
as.data.frame() %>%
dplyr::left_join(data.frame(main_celltype=celltype_order(),
main_celltype_order=seq_along(celltype_order())),
by="main_celltype") %>%
dplyr::arrange(main_celltype_order, Diagnosis_main, Sample)
order_samples <- tmpcoldat %>%
dplyr::select(main_celltype, Diagnosis_main, Sample) %>%
unique() %>%
dplyr::mutate(tmpname=paste0(stringr::str_replace_all(main_celltype,"/","\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)),
# 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_opt(RESET = TRUE)
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"),
right_annotation = ComplexHeatmap::rowAnnotation(`log10(FDR)`=ComplexHeatmap::anno_points(log10(fdr_topmarkers)),width=unit(20, "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 <- ha %v% ht
figname <- "Suppl_Figure_10"
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$y, : font
family 'Arial' not found in PostScript font databasedev.off()pdf
2 system(paste0("convert -geometry ",magick_geometry," ", here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff")), " ",here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".tiff"))))
jpeg(here::here("..","synovialscrnaseq","output","Figures_Paper",paste0(figname,".jpeg")),width=width,height=height,res = initres, units = "in")
draw(hm,
column_title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$")),
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 databasedev.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
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] ComplexHeatmap_2.6.2 tidySingleCellExperiment_1.0.0
[3] magrittr_2.0.1 scuttle_1.0.4
[5] scran_1.18.7 scater_1.18.6
[7] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[9] Biobase_2.50.0 GenomicRanges_1.42.0
[11] GenomeInfoDb_1.26.7 IRanges_2.24.1
[13] S4Vectors_0.28.1 BiocGenerics_0.36.1
[15] MatrixGenerics_1.2.1 matrixStats_0.58.0
[17] ggplot2_3.3.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.5.0
[3] RColorBrewer_1.1-2 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.0.3
[7] R6_2.5.0 irlba_2.3.3
[9] vipor_0.4.5 lazyeval_0.2.2
[11] DBI_1.1.1 colorspace_2.0-0
[13] GetoptLong_1.0.5 withr_2.4.1
[15] tidyselect_1.1.0 gridExtra_2.3
[17] compiler_4.0.3 git2r_0.28.0
[19] cli_2.3.0 BiocNeighbors_1.8.2
[21] Cairo_1.5-12.2 DelayedArray_0.16.3
[23] plotly_4.9.3 scales_1.1.1
[25] stringr_1.4.0 digest_0.6.27
[27] rmarkdown_2.6 XVector_0.30.0
[29] RhpcBLASctl_0.20-137 pkgconfig_2.0.3
[31] htmltools_0.5.1.1 sparseMatrixStats_1.2.1
[33] limma_3.46.0 GlobalOptions_0.1.2
[35] htmlwidgets_1.5.3 rlang_0.4.10
[37] DelayedMatrixStats_1.12.3 shape_1.4.5
[39] generics_0.1.0 jsonlite_1.7.2
[41] BiocParallel_1.24.1 dplyr_1.0.4
[43] RCurl_1.98-1.2 BiocSingular_1.6.0
[45] GenomeInfoDbData_1.2.4 Matrix_1.3-2
[47] Rcpp_1.0.6 ggbeeswarm_0.6.0
[49] munsell_0.5.0 fansi_0.4.2
[51] viridis_0.5.1 lifecycle_1.0.0
[53] stringi_1.5.3 whisker_0.4
[55] yaml_2.2.1 edgeR_3.32.1
[57] zlibbioc_1.36.0 promises_1.2.0.1
[59] dqrng_0.2.1 crayon_1.4.1
[61] lattice_0.20-41 beachmat_2.6.4
[63] circlize_0.4.12 magick_2.6.0
[65] locfit_1.5-9.4 knitr_1.31
[67] pillar_1.4.7 igraph_1.2.6
[69] rjson_0.2.20 glue_1.4.2
[71] evaluate_0.14 data.table_1.13.6
[73] png_0.1-7 vctrs_0.3.6
[75] httpuv_1.5.5 tidyr_1.1.2
[77] gtable_0.3.0 purrr_0.3.4
[79] clue_0.3-58 assertthat_0.2.1
[81] xfun_0.21 rsvd_1.0.3
[83] later_1.1.0.1 viridisLite_0.3.0
[85] tibble_3.0.6 beeswarm_0.2.3
[87] cluster_2.1.1 bluster_1.0.0
[89] statmod_1.4.35 ellipsis_0.3.1
[91] here_1.0.1