| title | author | date |
|---|---|---|
Fig2D |
N. Alcala |
4/22/2022 |
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(readxl)
library(ggridges)Colors for each experiment (organoid and parental tumor families), type, and grades:
colors_org = c(LNET2="#aade87ff",LNET6="#5fd38dff",LNET13="#16502dff",LNET14="#6f917cff",
LNET5="#e6a73cff",LNET10="#ff9955ff",LNET15="#ffd42aff", LNET16 = "#ff6600ff", LNET18= "#d0742fff",
LNET19="#2aff80ff",
LNET20 = "#f6e62bff",
LCNEC3="#ff8080ff",LCNEC4="#d35f5fff", LCNEC23 = "#ff5555ff",
LCNEC11="#ff5599ff",PANEC1="#8d5fd3ff",
SINET7="#2ad4ffff",SINET8="#80b3ffff",SINET9="#5f8dd3ff",SINET12="#5fbcd3ff", SINET21="#0066ffff", SINET22="#2c5aa0ff")
colors_types = c(SI= "#5bc2f0ff", Lung = "#9b9972ff", Pancreas = "#8d5fd3ff")
colors_grades = c(G1= "#58b873f9", G2 = "#ff9955ff", G3 = "#f0677dff", "G1/G2" = "#58c1f0ff")Data is in TableS1.xlsx, second tab (see supplementary files from the paper)
expr_genes_KI67.tib = read_xlsx("TableS1.xlsx",sheet = 2,skip=2,col_types = c("text","numeric","text","text","text"))Figure presenting the expression of MKI67 in organoids and reference tumors:
ggplot( expr_genes_KI67.tib %>% filter(Experiment=="Reference",Type!="SCLC",Grade!="NA") , aes(y=Grade,x=Expression.TPM,fill=Grade) ) + geom_density_ridges2(scale = 1.5,col=NA) +
geom_point(data = expr_genes_KI67.tib %>% filter(Experiment!="Reference",str_detect(Sample,"p[0-9.]*$")) , size=4, pch=16, col="white",fill="black") +
geom_point(data = expr_genes_KI67.tib %>% filter(Experiment!="Reference",str_detect(Sample,"p[0-9.]*$")) , size=2.7, pch=16, col="black",fill="black") +
theme_classic() + labs(y="Histopathological type",x=expression(italic(MKI67)~" Expression (TPM)") ) +
geom_vline(xintercept = 1,linetype="dashed") +
scale_x_log10(breaks=c(0.01,0.1,1,10,10**2),limits=c(0.01,200),labels=c("≤0.01",0.1,1,10,100)) + scale_fill_manual(values=alpha(colors_grades,0.5))
Compute mean expression levels per grade and tumor type, in organoids and reference samples
expr_genes_KI67.tib %>% group_by(Experiment=="Reference",Type,Grade) %>% summarize(mean(Expression.TPM))## # A tibble: 10 × 4
## # Groups: Experiment == "Reference", Type [7]
## `Experiment == "Reference"` Type Grade `mean(Expression.TPM)`
## <lgl> <chr> <chr> <dbl>
## 1 FALSE LCNEC G3 39.0
## 2 FALSE LNET G1 1.15
## 3 FALSE LNET G2 5.51
## 4 FALSE SINET G1/G2 1.02
## 5 TRUE LCNEC G3 37.8
## 6 TRUE LNET G1 1.10
## 7 TRUE LNET G2 4268.
## 8 TRUE LNET NA 2.05
## 9 TRUE SCLC G3 71.4
## 10 TRUE SINET G1/G2 3.54
expr_genes_KI67.tib %>% filter(str_detect(Sample,"p[0-9]")) %>% group_by(Experiment=="Reference",Type,Grade) %>% summarize(mean(Expression.TPM))## # A tibble: 4 × 4
## # Groups: Experiment == "Reference", Type [3]
## `Experiment == "Reference"` Type Grade `mean(Expression.TPM)`
## <lgl> <chr> <chr> <dbl>
## 1 FALSE LCNEC G3 38.8
## 2 FALSE LNET G1 1.45
## 3 FALSE LNET G2 4.28
## 4 FALSE SINET G1/G2 0.691
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.3.so
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
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## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggridges_0.5.3 readxl_1.3.1 forcats_0.5.1 stringr_1.4.0
## [5] dplyr_1.0.8 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
## [9] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.1 xfun_0.29 bslib_0.3.1 haven_2.4.3
## [5] colorspace_2.0-2 vctrs_0.3.8 generics_0.1.2 htmltools_0.5.2
## [9] yaml_2.2.2 utf8_1.2.2 rlang_1.0.1 jquerylib_0.1.4
## [13] pillar_1.7.0 withr_2.4.3 glue_1.6.1 DBI_1.1.2
## [17] dbplyr_2.1.1 modelr_0.1.8 plyr_1.8.6 lifecycle_1.0.1
## [21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 rvest_1.0.2
## [25] evaluate_0.15 knitr_1.38 tzdb_0.2.0 fastmap_1.1.0
## [29] fansi_1.0.2 highr_0.9 broom_0.7.12 Rcpp_1.0.8.3
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## [37] fs_1.5.2 hms_1.1.1 digest_0.6.29 stringi_1.7.6
## [41] grid_4.1.2 cli_3.1.1 tools_4.1.2 magrittr_2.0.2
## [45] sass_0.4.0 crayon_1.4.2 pkgconfig_2.0.3 ellipsis_0.3.2
## [49] xml2_1.3.3 reprex_2.0.1 lubridate_1.8.0 assertthat_0.2.1
## [53] rmarkdown_2.11 httr_1.4.2 rstudioapi_0.13 R6_2.5.1
## [57] compiler_4.1.2