Figure 4
Last updated: 2024-12-24
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Knit directory: proj_distal/analysis/
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# scRNA-seq
library(Seurat)
packageVersion("Seurat")[1] '4.1.0'# Plotting
library("ggplot2")
library("ggpubr")
library("cowplot")
# Presentation
library("knitr")
# Others
library("readxl")
library("DT")
library("dplyr")
library('stringr')source(here::here("R/00_generalDeps.R"))
source(here::here("R/output.R"))filt_path <- here::here("data/processed/figure4_input_seurat.rds")Introduction
In this document we are going to perform plotting figure4 panelC&D on the high-quality filtered dataset using Seurat.
Loading seurat object processed
if (file.exists(filt_path)) {
patient.integrated <- readRDS(filt_path)
} else {
stop("Figure4 dataset is missing. ",
"Please check Input first.",
call. = FALSE)
}Integration
Integrate data from the six samples, so that cells from the same cell type/subpopulation will cluster together.
visualization
plot metadata
#DimPlot(patient.integrated, reduction = "umap", group.by = "SortPheno") + ggtitle(paste0("SortPheno: ", length(patient.integrated$SortPheno)))
clusterCount = as.data.frame( table( ClusterNumber = patient.integrated[[ "SortPheno" ]]), responseName = "CellCount");
# Create datatable
datatable( clusterCount,
class = "compact",
rownames = FALSE,
colnames = c("SortPheno", "Nb. Cells"),
options = list(dom = "<'row'rt>", # Set elements for CSS formatting ('<Blf><rt><ip>')
autoWidth = FALSE,
columnDefs = list( # Center all columns
list( targets = 0:(ncol(clusterCount)-1),
className = 'dt-center')),
orderClasses = FALSE, # Disable flag for CSS to highlight columns used for ordering (for performance)
paging = FALSE, # Disable pagination (show all)
processing = TRUE,
scrollCollapse = TRUE,
scroller = TRUE, # Only load visible data
scrollX = TRUE,
scrollY = "525px",
stateSave = TRUE));#DimPlot(patient.integrated, reduction = "umap", group.by = "Sample") + ggtitle(paste0("Sample: ", length(patient.integrated$Sample)))
clusterCount = as.data.frame( table( ClusterNumber = patient.integrated[[ "Sample" ]]), responseName = "CellCount");
# Create datatable
datatable( clusterCount,
class = "compact",
rownames = FALSE,
colnames = c("Sample", "Nb. Cells"),
options = list(dom = "<'row'rt>", # Set elements for CSS formatting ('<Blf><rt><ip>')
autoWidth = FALSE,
columnDefs = list( # Center all columns
list( targets = 0:(ncol(clusterCount)-1),
className = 'dt-center')),
orderClasses = FALSE, # Disable flag for CSS to highlight columns used for ordering (for performance)
paging = FALSE, # Disable pagination (show all)
processing = TRUE,
scrollCollapse = TRUE,
scroller = TRUE, # Only load visible data
scrollX = TRUE,
scrollY = "525px",
stateSave = TRUE));Plate4 01-018 issue
Visual plate4 sample 01-018 then filter out
DefaultAssay(patient.integrated) <- 'RNA'
metadata_df = patient.integrated@meta.data
ids = metadata_df[metadata_df$Plate == 'p4' & metadata_df$Sample == '01-018',]$UniqueCellID
metadata_df$p4018 = 'no'
metadata_df[ids, ]$p4018 = 'yes'
patient.integrated$p4018 = metadata_df$p4018
#DimPlot(patient.integrated, reduction = "umap", group.by = "p4018") + ggtitle("Plate 4 018 issue")
Idents(patient.integrated) <- 'p4018'
patient.integrated = subset(patient.integrated, idents = 'yes',invert = TRUE)visualization 2
plot metadata after filter out
#DimPlot(patient.integrated, reduction = "umap", group.by = "SortPheno") + ggtitle(paste0("After filtering Plate 4 018 issue SortPheno: ", length(patient.integrated$SortPheno)))
clusterCount = as.data.frame( table( ClusterNumber = patient.integrated[[ "SortPheno" ]]), responseName = "CellCount");
# Create datatable
datatable( clusterCount,
class = "compact",
rownames = FALSE,
colnames = c("SortPheno", "Nb. Cells"),
options = list(dom = "<'row'rt>", # Set elements for CSS formatting ('<Blf><rt><ip>')
autoWidth = FALSE,
columnDefs = list( # Center all columns
list( targets = 0:(ncol(clusterCount)-1),
className = 'dt-center')),
orderClasses = FALSE, # Disable flag for CSS to highlight columns used for ordering (for performance)
paging = FALSE, # Disable pagination (show all)
processing = TRUE,
scrollCollapse = TRUE,
scroller = TRUE, # Only load visible data
scrollX = TRUE,
scrollY = "525px",
stateSave = TRUE));#DimPlot(patient.integrated, reduction = "umap", group.by = "Sample") + ggtitle(paste0("After filtering Plate 4 018 issue Sample: ", length(patient.integrated$Sample)))
clusterCount = as.data.frame( table( ClusterNumber = patient.integrated[[ "Sample" ]]), responseName = "CellCount");
# Create datatable
datatable( clusterCount,
class = "compact",
rownames = FALSE,
colnames = c("Sample", "Nb. Cells"),
options = list(dom = "<'row'rt>", # Set elements for CSS formatting ('<Blf><rt><ip>')
autoWidth = FALSE,
columnDefs = list( # Center all columns
list( targets = 0:(ncol(clusterCount)-1),
className = 'dt-center')),
orderClasses = FALSE, # Disable flag for CSS to highlight columns used for ordering (for performance)
paging = FALSE, # Disable pagination (show all)
processing = TRUE,
scrollCollapse = TRUE,
scroller = TRUE, # Only load visible data
scrollX = TRUE,
scrollY = "525px",
stateSave = TRUE));Box plots of gene set score
loading markers table
markers table 1 : figure 2, marker table 2 : figure 3
# DEA by Anno_1
dea_f = read.csv(file = here::here("data/processed/figure2_group_gene_markers.csv"), header = TRUE)
Top50_dea_f = dea_f %>% group_by(cluster) %>% top_n(n = 50, wt = avg_log2FC)
selfAg_df <- Top50_dea_f[Top50_dea_f$cluster == "SLA",]
flu_df <- Top50_dea_f[Top50_dea_f$cluster == "FLU",]
#marker2
amr053_df = read_excel(here::here("data/processed/supp_Table7_Rev1.xlsx"), sheet = "Feuil1")
amr053_df = na.omit(amr053_df)Box plots
Gene set score Auto-reactivity & H1N1
for(name in c('selfAg', 'flu')){
df = eval(parse(text = paste0(name, '_df')))
patient.integrated <- AddModuleScore(patient.integrated, features = list(df$gene) ,name = name)
}
metadata_df = patient.integrated@meta.data
title = c("Auto-reactivity", "H1N1")
i = 1
for(name in c('selfAg1','flu1')){
p<-ggplot(metadata_df, aes(x=SortPheno, y= eval(parse(text = name)), color=Sample)) +
geom_boxplot()+
#scale_color_manual(values=cols) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
my_comparisons <- list(c("TTneg" , "TTpos"))
p <- p + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
if ( i == 1 ){
stat = compare_means(selfAg1 ~ SortPheno, data = metadata_df)
}else{
stat = compare_means(flu1 ~ SortPheno, data = metadata_df)
}
print(p + ggtitle(paste0(title[i],": p.format is ", stat$p.format)))
i = i + 1
}
Cluster markers
for(name in c(1:3,5,6)){
genes = amr053_df[amr053_df$cluster == name,]$gene
if(length(genes) > 50){
genes = genes[1:50]
}
patient.integrated <- AddModuleScore(patient.integrated, features = list(genes) ,name = paste0('cl', name))
}
metadata_df = patient.integrated@meta.data
title = c("Cluster 1 (TEM GZMK+)", "Cluster 2 (TCM HLA-DR+)", "Cluster 3 (TEM NKG7+)", "Cluster 5 (CTL)", "Cluster 6 (TREG)")
i = 1
for(name in paste0('cl', c(1:3,5,6), '1')){
p<-ggplot(metadata_df, aes(x=SortPheno, y= eval(parse(text = name)), color=SortPheno)) +
geom_boxplot()+
#scale_color_manual(values=cols) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
my_comparisons <- list(c("TTneg" , "TTpos"))
p <- p + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
if ( i == 1 ){
stat = compare_means(cl11 ~ SortPheno, data = metadata_df)
}else if (i == 2){
stat = compare_means(cl21 ~ SortPheno, data = metadata_df)
}else if (i == 3){
stat = compare_means(cl31 ~ SortPheno, data = metadata_df)
}else if (i == 4){
stat = compare_means(cl51 ~ SortPheno, data = metadata_df)
}else if (i == 5){
stat = compare_means(cl61 ~ SortPheno, data = metadata_df)
}
print(p + ggtitle(paste0(title[i],": p.format is ", stat$p.format)))
i = i + 1
}
# save scores
df_scores = metadata_df[,c("UniqueCellID", "Sample","selfAg1","flu1","cl11","cl21","cl31","cl51","cl61")]
colnames(df_scores) = c("UniqueCellID", "Sample","Auto-reactivity","H1N1","Cluster 1 (TEM GZMK+)", "Cluster 2 (TCM HLA-DR+)", "Cluster 3 (TEM NKG7+)", "Cluster 5 (CTL)", "Cluster 6 (TREG)")Figure 4
Panel C
my_comparisons <- list(c("TTneg" , "TTpos"))
p1 <-ggplot(metadata_df, aes(x=SortPheno, y= selfAg1, color=Sample)) +
geom_boxplot()+
#scale_color_manual(values=cols) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p1 <- p1 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p1 <- p1 + labs(x = "sorting phenotype", y = "Gene set score", title = "Auto-reactivity")
p2 <-ggplot(metadata_df, aes(x=SortPheno, y= flu1, color=Sample)) +
geom_boxplot()+
#scale_color_manual(values=cols) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p2 <- p2 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p2 <- p2 + labs(x = "sorting phenotype", y = "Gene set score", title = "H1N1")
fig <- plot_grid(p1, p2, nrow = 1, labels = "AUTO")
ggsave(here::here("output", DOCNAME, "figure4-panelC.pdf"), fig,
width = 14, height = 8, scale = 1.2)
ggsave(here::here("output", DOCNAME, "figure4-panelC.png"), fig,
width = 14, height = 8, scale = 1.2)
fig
Panel D
p1<-ggplot(metadata_df, aes(x=SortPheno, y= cl11, color=SortPheno)) +
geom_boxplot()+
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p1 <- p1 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p1 <- p1 + labs(x = "sorting phenotype", y = "Gene set score", title = "Cluster 1 (TEM GZMK+)")
p2<-ggplot(metadata_df, aes(x=SortPheno, y= cl21, color=SortPheno)) +
geom_boxplot()+
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p2 <- p2 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p2 <- p2 + labs(x = "sorting phenotype", y = "Gene set score", title = "Cluster 2 (TCM HLA-DR+)")
p3<-ggplot(metadata_df, aes(x=SortPheno, y= cl31, color=SortPheno)) +
geom_boxplot()+
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p3 <- p3 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p3 <- p3 + labs(x = "sorting phenotype", y = "Gene set score", title = "Cluster 3 (TEM NKG7+)")
p4<-ggplot(metadata_df, aes(x=SortPheno, y= cl51, color=SortPheno)) +
geom_boxplot()+
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p4 <- p4 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p4 <- p4 + labs(x = "sorting phenotype", y = "Gene set score", title = "Cluster 5 (CTL)")
p5<-ggplot(metadata_df, aes(x=SortPheno, y= cl61, color=SortPheno)) +
geom_boxplot()+
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=1))
p5 <- p5 + stat_compare_means(comparisons = my_comparisons, label = "p.signif")
p5 <- p5 + labs(x = "sorting phenotype", y = "Gene set score", title = "Cluster 6 (Treg)")
fig <- plot_grid(p1, p2, p3, p4, p5, nrow = 2, labels = "AUTO")
ggsave(here::here("output", DOCNAME, "figure4-panelD.pdf"), fig,
width = 14, height = 8, scale = 1.3)
ggsave(here::here("output", DOCNAME, "figure4-panelD.png"), fig,
width = 14, height = 8, scale = 1.3)
fig
Output files
write.table(df_scores, here::here("output", DOCNAME,"figure4-raw_data.csv"), row.names=FALSE,col.names=TRUE, sep =',')
saveRDS(patient.integrated, here::here("data/processed/figure4_output_seurat.rds"))
save(metadata_df, file = here::here("data/processed/figure4_output_metadata.RData"))Session information
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.2 (2021-11-01)
os Ubuntu 20.04.3 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Etc/UTC
date 2024-12-24
pandoc 2.14.0.3 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
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scales 1.2.1 2022-08-20 [1] RSPM (R 4.1.0)
scattermore 0.7 2020-11-24 [1] RSPM (R 4.1.0)
sctransform 0.3.3 2022-01-13 [1] RSPM (R 4.1.0)
sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.1.0)
Seurat * 4.1.0 2022-01-14 [1] RSPM (R 4.1.0)
SeuratObject * 4.0.4 2021-11-23 [1] RSPM (R 4.1.0)
shiny 1.7.1 2021-10-02 [1] RSPM (R 4.1.0)
spatstat.core 2.3-2 2021-11-26 [1] RSPM (R 4.1.0)
spatstat.data 2.1-2 2021-12-17 [1] RSPM (R 4.1.0)
spatstat.geom 2.4-0 2022-03-29 [1] RSPM (R 4.1.0)
spatstat.sparse 2.1-0 2021-12-17 [1] RSPM (R 4.1.0)
spatstat.utils 2.3-0 2021-12-12 [1] RSPM (R 4.1.0)
stringi 1.7.6 2021-11-29 [1] RSPM (R 4.1.0)
stringr * 1.4.0 2019-02-10 [1] RSPM (R 4.1.0)
survival 3.2-13 2021-08-24 [2] CRAN (R 4.1.2)
tensor 1.5 2012-05-05 [1] RSPM (R 4.1.0)
testthat 3.1.2 2022-01-20 [1] RSPM (R 4.1.0)
tibble 3.1.8 2022-07-22 [1] RSPM (R 4.1.0)
tidyr 1.2.0 2022-02-01 [1] RSPM (R 4.1.0)
tidyselect 1.1.2 2022-02-21 [1] RSPM (R 4.1.0)
usethis 2.1.5 2021-12-09 [1] RSPM (R 4.1.0)
utf8 1.2.2 2021-07-24 [1] RSPM (R 4.1.0)
uwot 0.1.11 2021-12-02 [1] RSPM (R 4.1.0)
vctrs 0.6.4 2023-10-12 [1] RSPM (R 4.1.0)
viridisLite 0.4.0 2021-04-13 [1] RSPM (R 4.1.0)
withr 2.5.0 2022-03-03 [1] RSPM (R 4.1.0)
workflowr 1.7.1 2023-08-23 [1] RSPM (R 4.1.0)
xfun 0.30 2022-03-02 [1] RSPM (R 4.1.0)
xtable 1.8-4 2019-04-21 [1] RSPM (R 4.1.0)
yaml 2.3.5 2022-02-21 [1] RSPM (R 4.1.0)
zoo 1.8-9 2021-03-09 [1] RSPM (R 4.1.0)
[1] /usr/local/lib/R/site-library
[2] /usr/local/lib/R/library
──────────────────────────────────────────────────────────────────────────────
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.0.8 DT_0.22 readxl_1.3.1 knitr_1.37 cowplot_1.1.1 ggpubr_0.4.0 ggplot2_3.4.4 SeuratObject_4.0.4 Seurat_4.1.0
loaded via a namespace (and not attached):
[1] backports_1.4.1 workflowr_1.7.1 systemfonts_1.0.4 plyr_1.8.6 igraph_1.5.1 lazyeval_0.2.2 splines_4.1.2 crosstalk_1.2.0 listenv_0.8.0 scattermore_0.7
[11] usethis_2.1.5 digest_0.6.29 htmltools_0.5.2 fansi_1.0.2 magrittr_2.0.2 memoise_2.0.1 tensor_1.5 cluster_2.1.2 ROCR_1.0-11 remotes_2.4.2
[21] globals_0.14.0 matrixStats_0.61.0 spatstat.sparse_2.1-0 prettyunits_1.1.1 colorspace_2.0-3 ggrepel_0.9.1 textshaping_0.3.6 xfun_0.30 callr_3.7.0 crayon_1.5.0
[31] jsonlite_1.8.0 spatstat.data_2.1-2 survival_3.2-13 zoo_1.8-9 glue_1.6.2 polyclip_1.10-0 gtable_0.3.0 leiden_0.3.9 car_3.0-12 pkgbuild_1.3.1
[41] future.apply_1.8.1 abind_1.4-5 scales_1.2.1 DBI_1.1.2 rstatix_0.7.0 miniUI_0.1.1.1 Rcpp_1.0.8 viridisLite_0.4.0 xtable_1.8-4 reticulate_1.23
[51] spatstat.core_2.3-2 htmlwidgets_1.5.4 httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0 uwot_0.1.11
[61] deldir_1.0-6 utf8_1.2.2 here_1.0.1 labeling_0.4.2 tidyselect_1.1.2 rlang_1.1.1 reshape2_1.4.4 later_1.3.0 cellranger_1.1.0 munsell_0.5.0
[71] tools_4.1.2 cachem_1.0.6 cli_3.6.1 generics_0.1.2 broom_0.7.12 devtools_2.4.3 ggridges_0.5.3 evaluate_0.15 stringr_1.4.0 fastmap_1.1.0
[81] ragg_1.2.2 yaml_2.3.5 goftest_1.2-3 processx_3.5.2 fs_1.5.2 fitdistrplus_1.1-6 purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0 future_1.23.0
[91] nlme_3.1-153 mime_0.12 brio_1.1.3 compiler_4.1.2 rstudioapi_0.13 plotly_4.10.0 png_0.1-7 ggsignif_0.6.3 testthat_3.1.2 spatstat.utils_2.3-0
[101] tibble_3.1.8 bslib_0.3.1 stringi_1.7.6 highr_0.9 ps_1.6.0 desc_1.4.1 lattice_0.20-45 Matrix_1.3-4 vctrs_0.6.4 pillar_1.7.0
[111] lifecycle_1.0.3 spatstat.geom_2.4-0 lmtest_0.9-39 jquerylib_0.1.4 RcppAnnoy_0.0.19 data.table_1.14.2 irlba_2.3.5 httpuv_1.6.5 patchwork_1.1.1 R6_2.5.1
[121] promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.30.0 sessioninfo_1.2.2 codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1 pkgload_1.2.4 rprojroot_2.0.2
[131] withr_2.5.0 sctransform_0.3.3 mgcv_1.8-38 parallel_4.1.2 grid_4.1.2 rpart_4.1-15 tidyr_1.2.0 rmarkdown_2.11 carData_3.0-5 Rtsne_0.15
[141] git2r_0.33.0 shiny_1.7.1