Generate Figure S4F
### scores were calculated based on LE genes for from severity comparison
FIG_OUT_PATH <- "Schulte-Schrepping/output/"
combined_genesets <- c("HALLMARK_TNFA_SIGNALING_VIA_NFKB",
"reactome_Fatty acid metabolism")
severity_gsva_esetlist <- readRDS("../input/SchulteSchrepping/cohort1/severe-mild_module_score_gsva_filtered_samples_genes_cohort1.rds")
severity_gsva_esetlist_df <- getgsvascore_list_df(severity_gsva_esetlist, combined_genesets)
## [1] "0_Classical Monocytes"
## [1] "1_HLA-DR+ CD83+ Monocytes"
## [1] "10_CD4+ T cells_2"
## [1] "11_CD4+ T cells_3"
## [1] "12_CD8+ T cells_1"
## [1] "13_CD8+ T cells_2"
## [1] "14_CD8+ T cells_3"
## [1] "15_NK cells"
## [1] "16_B cells_1"
## [1] "19_Plasmablasts"
## [1] "20_Megakaryocyte"
## [1] "3_HLA-DR- S100A+ monocytes"
## [1] "4_Non-classical Monocytes"
## [1] "5_Neutrophils"
## [1] "7_mDCs"
## [1] "9_CD4+ T cells_1"
severity_gsva_esetlist_df_T0 <- severity_gsva_esetlist_df %>% filter(Timepoint == "T0", celltype == "15_NK cells")
severity_gsva_esetlist_cohort2 <- readRDS("../input/SchulteSchrepping/cohort2/severe-mild_module_score_gsva_filtered_samples_genes_cohort2.rds")
severity_gsva_esetlist_cohort2_df <- getgsvascore_list_df(severity_gsva_esetlist_cohort2, combined_genesets)
## [1] "B cells"
## [1] "CD4+ T cells"
## [1] "CD8+ T cells"
## [1] "HLA-DRhi CD83hi Monocytes"
## [1] "HLA-DRlo CD163hi Monocytes"
## [1] "HLA-DRlo S100Ahi Monocytes"
## [1] "mixed_1"
## [1] "Neutrophils"
## [1] "NK cells"
## [1] "pDC"
## [1] "Plasmablasts"
## [1] "Prol. cells"
severity_gsva_esetlist_cohort2_df_T0 <- severity_gsva_esetlist_cohort2_df %>% filter(Timepoint == "T0", celltype == "NK cells")
# cohort1 plot
severity.color <- c("mild"="#00A1D5FF" ,"severe"="#B24745FF", "control" = "#79AF97FF")
p <- ggplot(severity_gsva_esetlist_df_T0, aes(x = HALLMARK_TNFA_SIGNALING_VIA_NFKB, y = `reactome_Fatty acid metabolism`)) +
geom_point(shape=21,aes(fill=group_per_sample),size=3, color="white") +
scale_fill_manual(name="Severity",values = severity.color) +
geom_smooth(se = F,method = "lm") +
stat_cor(method = "pearson") +
facet_wrap(~celltype, scales = "free") +
theme_bw()
p
# ggsave("../SchulteSchrepping/output/", filename = "NK.NFkBvsfatty.severity.cor.pdf", plot = p, device = "pdf", width = 4, height = 3)
# cohort2 plot
p <- ggplot(severity_gsva_esetlist_cohort2_df_T0, aes(x = HALLMARK_TNFA_SIGNALING_VIA_NFKB, y = `reactome_Fatty acid metabolism`)) +
geom_point(shape=21,aes(fill=group_per_sample),size=3, color="white") +
scale_fill_manual(name="Severity",values = severity.color) +
geom_smooth(se = F,method = "lm") +
stat_cor(method = "pearson") +
facet_wrap(~celltype, scales = "free") +
theme_bw()
p
# ggsave("../SchulteSchrepping/output/", filename = "NK.NFkBvsfatty.severity.cor.cohort2.pdf", plot = p, device = "pdf", width = 4, height = 3)
sI <- sessionInfo()
utils:::print.sessionInfo(sI[-c(10,11)])
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gridExtra_2.3 ggpubr_0.4.0.999 reshape2_1.4.3
## [4] Biobase_2.46.0 BiocGenerics_0.32.0 forcats_0.4.0
## [7] stringr_1.4.0 dplyr_0.8.4 purrr_0.3.3
## [10] readr_1.3.1 tidyr_1.0.2 tibble_3.0.3
## [13] ggplot2_3.3.2 tidyverse_1.3.0 plyr_1.8.5
## [16] matrixStats_0.55.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.1 jsonlite_1.6.1 splines_3.6.2 carData_3.0-3
## [5] modelr_0.1.6 assertthat_0.2.1 cellranger_1.1.0 yaml_2.2.1
## [9] pillar_1.4.3 backports_1.1.5 lattice_0.20-40 glue_1.3.1
## [13] digest_0.6.25 ggsignif_0.6.0 rvest_0.3.5 colorspace_1.4-1
## [17] htmltools_0.4.0 Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.0
## [21] haven_2.2.0 scales_1.1.0 openxlsx_4.1.4 rio_0.5.16
## [25] mgcv_1.8-31 generics_0.0.2 farver_2.0.3 car_3.0-6
## [29] ellipsis_0.3.0 withr_2.1.2 cli_2.0.2 magrittr_1.5
## [33] crayon_1.3.4 readxl_1.3.1 evaluate_0.14 fs_1.3.1
## [37] fansi_0.4.1 nlme_3.1-144 rstatix_0.6.0 xml2_1.2.2
## [41] foreign_0.8-75 tools_3.6.2 data.table_1.12.8 hms_0.5.3
## [45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 zip_2.0.4
## [49] compiler_3.6.2 rlang_0.4.7 grid_3.6.2 rstudioapi_0.11
## [53] labeling_0.3 rmarkdown_2.1 gtable_0.3.0 abind_1.4-5
## [57] DBI_1.1.0 curl_4.3 R6_2.4.1 lubridate_1.7.4
## [61] knitr_1.28 stringi_1.4.6 Rcpp_1.0.3 vctrs_0.3.4
## [65] dbplyr_1.4.2 tidyselect_1.0.0 xfun_0.12