EC_trajectory_analysis
retogerber
2024-01-22
Last updated: 2024-01-22
Checks: 6 1
Knit directory: synovialscrnaseq/
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Set up
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(slingshot)
library(tradeSeq)
library(ggplot2)
})
n_workers <- 20
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)tmpfilename <- paste0("syn_v",analysis_version,"_sce_ec",dplyr::if_else(remove_low_quality_samples, "_invivo",""),"_trajectory.rds")
sce_full <- readRDS(file = here::here("output",tmpfilename))
sce_full <- sce_full[!duplicated(row.names(sce_full)),]Loading required package: tidySingleCellExperiment
Attaching package: 'tidySingleCellExperiment'The 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':
filterLoading required package: BiocSingularsce_full <- sce_full[rowData(sce_full)$is_hvg,]tmpfilename <- paste0("syn_v",analysis_version,"_sce_ec",dplyr::if_else(remove_low_quality_samples, "_invivo",""),"_trajectory2.rds")
sce <- readRDS(file = here::here("output",tmpfilename))table(rowData(sce)$tradeSeq$converged)
FALSE TRUE
379 3426 Association of gene expression with pseudotime
Sort by FDR and filter by meanLogFC>1
tmpfilename <- paste0("syn_v",analysis_version,"_sce_ec",dplyr::if_else(remove_low_quality_samples, "_invivo",""),"_trajectory2_ATres.rds")
ATres <- readRDS(file = here::here("output",tmpfilename))
ATres <- ATres[rowData(sce)$tradeSeq$converged,]
sce_full <- sce_full[rowData(sce)$tradeSeq$converged,]
sce <- sce[rowData(sce)$tradeSeq$converged,]ATres$FDR <- p.adjust(ATres$pvalue)
aStart <- order(ATres$waldStat, decreasing = TRUE)
lcm <- rowMeans(logcounts(sce_full))
stopifnot(all(rownames(sce_full) == rownames(ATres)))
aStart_filt <- aStart[lcm[aStart]>1]
ATres_filt <- ATres[aStart_filt,]
ATres_filt <- ATres_filt[ATres_filt$FDR<0.01,]
ATres_filt <- ATres_filt[!stringr::str_detect(rownames(ATres_filt),"^NA(.){0,1}[0-9]{0,2}$"),]genenames <- rownames(ATres_filt)js <- seq(1,length(genenames),by=16)
js <- js[1:5]
for(j in seq_along(js)[-length(js)]){
pltls <- lapply((js[j]):(js[j+1]-1),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
print(ggpubr::ggarrange(plotlist=pltls, common.legend = TRUE, align="hv"))
}Suppl Figure 16
sce_full$ec_celltype_simplified[sce_full$ec_celltype_simplified=="ec venous"] <- "ACKR1+ venous"
tmpplt <- plotSmoothers(sce, counts(sce), gene = "FLT1", pointCol=sce_full$ec_celltype_simplified, size=10) + labs(color="Endothelial cell labels")
leg <- ggpubr::get_legend(tmpplt)
tm <- ggpubr::as_ggplot(leg)
genenames <- c("PODXL")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
plta <- ggpubr::ggarrange(plotlist=c(pltls,list(ggplot()+theme_void()),list(tm)), legend = "none", align="hv", nrow = 1,ncol=5)
genenames <- c("SPARC", "COL4A1", "COL4A2", "COL15A1", "PLVAP")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
pltb <- ggpubr::ggarrange(plotlist=pltls, legend = "none", align="hv", nrow = 1,ncol=5)
genenames <- c("VWF", "ACKR1", "CLU")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
pltc <- ggpubr::ggarrange(plotlist=pltls, legend = "none", align="hv", nrow = 1,ncol=5)
genenames <- c("SELE", "SELP", "ICAM1")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
pltd <- ggpubr::ggarrange(plotlist=pltls, legend = "none", align="hv", nrow = 1,ncol=5)
genenames <- c("HLA-DRA", "HLA-DPA1", "CD74")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
plte <- ggpubr::ggarrange(plotlist=pltls, legend = "none", align="hv", nrow = 1,ncol=5)
genenames <- c("JUN", "JUNB", "FOS", "FOSB")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
pltf <- ggpubr::ggarrange(plotlist=pltls, legend = "none", align="hv", nrow = 1,ncol=5)
genenames <- c("FLT1","NFKBIA", "IRF1", "SOCS3")
pltls <- lapply(seq_along(genenames),function(i) plotSmoothers(sce, counts(sce), gene = genenames[i], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(genenames[i]))
pltg <- ggpubr::ggarrange(plotlist=pltls, legend = "none", align="hv", nrow = 1,ncol=5)plt <- ggpubr::ggarrange(
plta + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
pltb + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
pltc + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
pltd + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
plte + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
pltf + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
pltg + theme_bw() + main_plot_theme() + theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"), panel.border = element_blank()),
common.legend=TRUE,
align="hv",
ncol=1,
font.label=list(size=26),
labels="auto"
)
plt
figname <- "Suppl_Figure_16"
width <- 20
height <- 24
res = 300
maxwidth <- 8.5
maxheight <- 11
downscale <- max(c(3,height/maxheight, width/maxwidth))
# ideally multiply by 'downscale' but imagemagick throws error for too large images
initres <- res*1.2
magick_geometry <- paste0(width/downscale*res,"x",height/downscale*res)
plt <- plt +
labs(title=paste0("Figure S",stringr::str_extract(figname,"[[:digit:]]+$"))) +
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 startRes <- startVsEndTest(sce)oStart <- order(startRes$waldStat, decreasing = TRUE)
startRes[oStart,]
lcm <- rowMeans(logcounts(sce_full))
stopifnot(all(rownames(sce_full) == rownames(startRes)))
oStart_filt <- oStart[lcm[oStart]>1]
genenames <- rownames(startRes[oStart_filt,])js <- seq(1,length(oStart_filt),by=16)
for(j in js){
pltls <- lapply((j):(j+15),function(i) plotSmoothers(sce, counts(sce), gene = names(sce)[oStart_filt[i]], pointCol=sce_full$ec_celltype_simplified) + ggplot2::ggtitle(names(sce)[oStart_filt[i]]))
print(ggpubr::ggarrange(plotlist=pltls, common.legend = TRUE, align="hv"))
}pltls <- lapply(1:16,function(i) plotSmoothers(sce, counts(sce), gene = names(sce)[oStart_filt[i]]) + ggplot2::ggtitle(names(sce)[oStart_filt[i]]))
ggpubr::ggarrange(plotlist=pltls, common.legend = TRUE, align="hv")library(clusterExperiment)
nPointsClus <- 20
clusPat <- clusterExpressionPatterns(sce, nPoints = nPointsClus,
genes = names(sce)[oStart_filt], ncores=n_workers,
minSizes=12)clusterLabels <- primaryCluster(clusPat$rsec)tg <- names(sce)[oStart_filt][clusterLabels==1]
pltls <- lapply(1:16,function(i) plotSmoothers(sce, counts(sce), gene = tg[i]) + ggplot2::ggtitle(tg[i]))
ggpubr::ggarrange(plotlist=pltls, common.legend = TRUE, align="hv")
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] gdtools_0.2.3 BiocSingular_1.6.0
[3] tidySingleCellExperiment_1.0.0 ggplot2_3.3.3
[5] tradeSeq_1.4.0 slingshot_1.8.0
[7] princurve_2.1.6 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 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 VGAM_1.1-5
[4] systemfonts_1.0.1 plyr_1.8.6 igraph_1.2.6
[7] lazyeval_0.2.2 splines_4.0.3 BiocParallel_1.24.1
[10] densityClust_0.3 fastICA_1.2-2 digest_0.6.27
[13] htmltools_0.5.1.1 viridis_0.5.1 fansi_0.4.2
[16] magrittr_2.0.1 cluster_2.1.1 openxlsx_4.2.3
[19] limma_3.46.0 docopt_0.7.1 svglite_1.2.3.2
[22] colorspace_2.0-0 ggrepel_0.9.1 haven_2.3.1
[25] xfun_0.21 dplyr_1.0.4 sparsesvd_0.2
[28] crayon_1.4.1 RCurl_1.98-1.2 jsonlite_1.7.2
[31] ape_5.4-1 glue_1.4.2 gtable_0.3.0
[34] zlibbioc_1.36.0 XVector_0.30.0 DelayedArray_0.16.3
[37] car_3.0-10 abind_1.4-5 scales_1.1.1
[40] pheatmap_1.0.12 DBI_1.1.1 edgeR_3.32.1
[43] rstatix_0.7.0 Rcpp_1.0.6 viridisLite_0.3.0
[46] foreign_0.8-81 rsvd_1.0.3 htmlwidgets_1.5.3
[49] httr_1.4.2 FNN_1.1.3 RColorBrewer_1.1-2
[52] ellipsis_0.3.1 pkgconfig_2.0.3 farver_2.0.3
[55] locfit_1.5-9.4 here_1.0.1 tidyselect_1.1.0
[58] labeling_0.4.2 rlang_0.4.10 reshape2_1.4.4
[61] later_1.1.0.1 munsell_0.5.0 cellranger_1.1.0
[64] tools_4.0.3 cli_2.3.0 generics_0.1.0
[67] broom_0.7.4 evaluate_0.14 stringr_1.4.0
[70] yaml_2.2.1 RhpcBLASctl_0.20-137 knitr_1.31
[73] fs_1.5.0 zip_2.1.1 DDRTree_0.1.5
[76] purrr_0.3.4 RANN_2.6.1 pbapply_1.4-3
[79] nlme_3.1-152 monocle_2.18.0 slam_0.1-48
[82] compiler_4.0.3 plotly_4.9.3 curl_4.3
[85] ggsignif_0.6.0 tibble_3.0.6 stringi_1.5.3
[88] highr_0.8 forcats_0.5.1 lattice_0.20-41
[91] Matrix_1.3-2 HSMMSingleCell_1.10.0 vctrs_0.3.6
[94] pillar_1.4.7 lifecycle_1.0.0 combinat_0.0-8
[97] cowplot_1.1.1 data.table_1.13.6 bitops_1.0-6
[100] irlba_2.3.3 httpuv_1.5.5 R6_2.5.0
[103] promises_1.2.0.1 gridExtra_2.3 rio_0.5.16
[106] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.1
[109] qlcMatrix_0.9.7 GenomeInfoDbData_1.2.4 mgcv_1.8-34
[112] hms_1.0.0 grid_4.0.3 beachmat_2.6.4
[115] tidyr_1.1.2 rmarkdown_2.6 carData_3.0-4
[118] Rtsne_0.15 git2r_0.28.0 ggpubr_0.4.0