Last updated: 2021-08-16
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Knit directory: Turati_NatCancer_2021/
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# knitr::opts_chunk$set(cache = T, autodep = T)
library(tidyverse)
library(DT)Several signatures/gene sets are used in this analysis.
MSigDB: all signatures have been downloaded using the msigdbr package.
Lineage signatures: from Laurenti et al (ProB, MLP, MEP, HSC, GMP, ETP, EarlyB, CMP)
Cell state: from Tirosh et al (Tirosh_G1_S, Tirosh_G2_M, TIROSH_Stemness)
msigdbr_full_signatures_table <- msigdbr::msigdbr("Homo sapiens")
groups <- msigdbr_full_signatures_table %>%
group_by(gs_cat, gs_subcat) %>%
summarise(n = n()) %>%
mutate(name = ifelse(gs_subcat != "", paste0(gs_cat, "-", gs_subcat), gs_cat))`summarise()` regrouping output by 'gs_cat' (override with `.groups` argument)signatures <- list()
sink <- groups %>% apply(1, function(x) {
signatures[[x["name"]]] <<- msigdbr_full_signatures_table %>%
filter(gs_cat == x["gs_cat"] & gs_subcat == x["gs_subcat"]) %>%
split(x = .$gene_symbol, f = .$gs_name)
})
# This table is available from Laurenti et al.
laurenti_signatures <- read.table("data-raw/PopSpecificSignatures_250.gmx", header = T)
sink <- lapply(colnames(laurenti_signatures), function(x) {
signatures[["lineages"]][[x]] <<- as.character(laurenti_signatures[, x])
})
# This table was extracted from Tirosh et al.
cell_state_signatures <- fgsea::gmtPathways("data-raw/cell_state.gmt")
sink <- lapply(names(cell_state_signatures), function(x) {
signatures[["cell_state"]][[x]] <<- as.character(cell_state_signatures[[x]])
})
usethis::use_directory("data")✓ Setting active project to '/Users/javier/Projects/Turati_NatCancer_2021'save(signatures, file = "data/signatures.rda")Here is a list of the different signature sets:
enframe(sapply(signatures, length), name = "set", "number") %>%
left_join(enframe(sapply(signatures, function(x) {
num <- length(x)
if (num > 10) {
names <- paste(c(names(x)[1:10], "..."), collapse = ",")
} else {
names <- paste(names(x), collapse = ",")
}
return(names)
}), name = "set", "signatures"), by = "set") %>%
DT::datatable()To use the signatures, simply do:
data("signatures")
hallmarks <- signatures[["H"]]
lineages <- signatures[["lineages"]]
cell_cycle_genes <- signatures[["lineages"]][["GO_CELL_CYCLE"]]
# Combinations
pathways <- c(signatures[["lineages"]], signatures[["H"]])
names(pathways) <- gsub("HALLMARK_", "", names(pathways))To use the paper palette with ggplot2, simply do:
data("paper_palette")
ggplot() +
... +
scale_fill_manual(values = paper_palette) +
...paper_palette <- c(
## Bulk treated
"BulkChemotherapy" = "#AF4FAF",
"Chemotherapy" = "#AF4FAF",
"Treated" = "#AF4FAF",
## Bulk relapse (240, 146, 242)
"BulkRelapse" = "#F092F2",
"Relapse" = "#F092F2",
## Bulk untreated (for simple)
"BulkUntreated" = "#43B0E3",
"Untreated" = "#43B0E3",
## Diagnosis
"Diagnosis" = "#E38522",
"MRD" = "#30AF30",
## PRS
"PRS" = "#307F7F",
## Cell cycle: deep quiescent
"Deep" = "#959867",
"DeepQ" = "#959867",
## Cell cycle: shallow quiescent
"Shallow" = "#94BE69",
"ShallowQ" = "#94BE69",
## Cell cycle: cycling
"Cycling" = "#CA4B1F",
"Cycling" = "#CA4B1F",
## Bulk headache: T/T (231, 119, 108)
"chem-chem" = "#E7776C",
"Chronically treated" = "#E7776C",
## Bulk headache: T/U
"chem-nochem" = "#A1A22C",
"Treatment Withdrawn" = "#A1A22C",
"Treatment withdrawn" = "#A1A22C",
## Bulk headache: U/T
"nochem-chem" = "#55B583",
"Acutely treated" = "#55B583",
## Bulk headache: U/U
"nochem-nochem" = "#0416C8",
"Untreated" = "#0416C8",
"Never treated" = "#0416C8",
## Lineages
"ProB" = "#AF4AFF",
"MLP" = "#797FF4",
"MEP" = "#44B4FF",
"HSC" = "#4BD6B3",
"GMP" = "#67F16C",
"ETP" = "#97A645",
"EarlyB" = "#CCB43D",
"CMP" = "#F88134",
"pre-BII" = "#FF4C24",
"pre-BI" = "#AB0300",
## Yes/No
"Yes"= "#307F7F",
"No" = "#B4B4B4")
usethis::use_directory("data")
save(paper_palette, file = "data/paper_palette.rda")
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DT_0.13 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_2.1.3
[9] ggplot2_3.3.1 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 lubridate_1.7.4 lattice_0.20-40
[4] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25
[7] R6_2.4.1 cellranger_1.1.0 backports_1.1.5
[10] reprex_0.3.0 evaluate_0.14 httr_1.4.1
[13] pillar_1.4.3 rlang_0.4.11 readxl_1.3.1
[16] rstudioapi_0.11 data.table_1.12.8 whisker_0.4
[19] Matrix_1.2-18 rmarkdown_2.1 BiocParallel_1.20.1
[22] htmlwidgets_1.5.1 munsell_0.5.0 broom_0.5.5
[25] fgsea_1.12.0 compiler_3.6.3 httpuv_1.5.2
[28] modelr_0.1.6 xfun_0.16 pkgconfig_2.0.3
[31] base64enc_0.1-3 htmltools_0.5.1.1 tidyselect_1.1.0
[34] gridExtra_2.3 crayon_1.3.4 dbplyr_1.4.2
[37] withr_2.4.2 later_1.0.0 grid_3.6.3
[40] nlme_3.1-145 jsonlite_1.6.1 gtable_0.3.0
[43] lifecycle_0.2.0 DBI_1.1.0 git2r_0.26.1
[46] magrittr_1.5 scales_1.1.0 cli_3.0.0
[49] stringi_1.4.6 msigdbr_7.0.1 fs_1.3.2
[52] promises_1.1.0 xml2_1.2.5 ellipsis_0.3.0
[55] generics_0.0.2 vctrs_0.3.0 fastmatch_1.1-0
[58] tools_3.6.3 glue_1.3.2 crosstalk_1.1.0.1
[61] hms_0.5.3 parallel_3.6.3 yaml_2.2.1
[64] colorspace_1.4-1 rvest_0.3.5 knitr_1.28
[67] haven_2.2.0 usethis_2.0.1