Figure 9: Genes in AC toxicity-associated loci respond to TOP2i
ERM
2023-09-28
Last updated: 2023-09-28
Checks: 7 0
Knit directory: Cardiotoxicity/
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library(edgeR)
library(tidyverse)
library(ggsignif)
library(RColorBrewer)
library(cowplot)
library(ggpubr)
library(scales)
library(sjmisc)
library(kableExtra)
library(broom)
library(ComplexHeatmap)toplistall <- readRDS("data/toplistall.RDS")
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist, envir = .GlobalEnv)<environment: R_GlobalEnv>cpmcounts <- readRDS("data/cpmcount.RDS")
backGL <- read.csv("data/backGL.txt", row.names =1)
drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
drug_pal_fact <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031","#41B333")
col_fun1 = circlize::colorRamp2(c(-1, 3), c("white", "purple"))
col_funFC= circlize::colorRamp2(c(-2,0, 2), c("darkgreen","white", "darkorange2"))
col_funTOX = circlize::colorRamp2(c(-1,0, 1), c("darkviolet", "white","firebrick4"))pearson_extract <- function(corr_df,ENTREZID) {
ld50_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=LD50, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
tnni_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=rtnni, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
ldh_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=rldh, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
##ggbuild to get model:
ld50_build <- ggplot_build(ld50_plot)
ld50_data <- data.frame('rho_LD50'= c(ld50_build$data[[3]]$r,NA,NA),
'sig_LD50'=c(ld50_build$data[[3]]$p.value,NA,NA),
'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
tnni_build <- ggplot_build(tnni_plot)
tnni_data <- data.frame('rho_tnni'= c(tnni_build$data[[3]]$r),
'sig_tnni'=c(tnni_build$data[[3]]$p.value),
'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
ldh_build <- ggplot_build(ldh_plot)
ldh_data <- data.frame('rho_ldh'= c(ldh_build$data[[3]]$r),
'sig_ldh'=c(ldh_build$data[[3]]$p.value),
'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
results <- cbind(ldh_data[,c(3,1:2)],tnni_data[,1:2],ld50_data[,1:2])
return(results)
}
cpm_boxplot24h <-function(cpmcounts, GOI,brewer_palette, fill_colors, ylab) {
##GOI needs to be ENTREZID
df <- cpmcounts
df_plot <- df %>%
dplyr::filter(rownames(.)==GOI) %>%
pivot_longer(everything(),
names_to = "treatment",
values_to = "counts") %>%
separate(treatment, c("drug","indv","time")) %>%
mutate(time = case_match(time,"24h"~"24 hours", "3h"~"3 hours")) %>%
mutate(indv=factor(indv, levels = c(1,2,3,4,5,6))) %>%
mutate(drug =case_match(drug, "Da"~"DNR",
"Do"~"DOX",
"Ep"~"EPI",
"Mi"~"MTX",
"Tr"~"TRZ",
"Ve"~"VEH", .default = drug)) %>%
mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>%
dplyr::filter(time=="24 hours")
plot <- ggplot2::ggplot(df_plot, aes(x=drug, y=counts))+
geom_boxplot(position="identity",aes(fill=drug))+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(alpha= "none", size= "none")+
scale_color_brewer(palette = brewer_palette, guide = "none")+
scale_fill_manual(values=fill_colors)+
# facet_wrap("time", nrow=1, ncol=2)+
theme_bw()+
ylab(ylab)+
xlab("")+
ggtitle("24 hours")+
theme(strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
plot.title = element_text(size=12,hjust = 0.5,face="bold"),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
panel.background = element_rect(colour = "black", size=1),
axis.text.x = element_blank(),
strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))
print(plot)
}
pearson_cardiotox <- function(corr_df,ENTREZID) {
full_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=tox_score, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
##ggbuild to get model:
tox_build <- ggplot_build(full_plot)
tox_data <- data.frame('rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"),
'tox_val'= c(tox_build$data[[3]]$r,NA,NA),
'sig_tox'=c(tox_build$data[[3]]$p.value,NA,NA)
)
return(tox_data)
}Figure 9: Genes in AC toxicity-associated loci respond to TOP2i
A. GWAS and TWAS associated genes
GWAS_goi <- read.csv("output/GWAS_goi.csv",row.names = 1)
##get the abs FC of all GOI
GWASabsFCsig <-
toplistall %>%
mutate(drug=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>%
filter(ENTREZID %in% GWAS_goi$entrezgene_id) %>%
filter(time =="24_hours") %>%
dplyr::select(ENTREZID , id,logFC, adj.P.Val, SYMBOL) %>%
pivot_wider(id_cols=id,
names_from = SYMBOL,
values_from =adj.P.Val)
gwas_sig_mat <- GWASabsFCsig %>%
column_to_rownames(var="id") %>%
as.matrix()
GWASabsFC <- toplistall %>%
# filter(id !="TRZ") %>%
filter(time=="24_hours") %>%
mutate(logFC= logFC*(-1)) %>%
filter(ENTREZID %in% GWAS_goi$entrezgene_id) %>%
dplyr::select(SYMBOL ,time, id, logFC) %>%
pivot_wider(id_cols=id,
names_from = SYMBOL,
values_from = logFC) %>%
column_to_rownames(var="id") %>%
as.matrix()
study_anno <- data.frame(Study=c("GWAS","GWAS","TWAS","TWAS","GWAS","GWAS","GWAS","TWAS"),motif=c(rep("LR",7),"NR"))
rownames(study_anno) <- colnames(GWASabsFC)
ht <- HeatmapAnnotation(df = study_anno,
col = list(Study=c("GWAS"="darkorange","TWAS"= "blueviolet"),motif = c("LR"="#7CAE00","NR"="#C77CFF"), just = "left"))
Heatmap(GWASabsFC, name = "Fold change\nvalues",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
col = col_funFC,
row_order = c('DOX','EPI','DNR', 'MTX','TRZ'),
column_title = "Fold change values of GWAS and TWAS genes",
column_title_side = "top",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
column_order= c('RARG',
'TNS2',
'ZNF740',
'SLC28A3',
'RMI1',
'EEF1B2',
'FRS2',
'HDDC2'),
bottom_annotation = ht,
column_names_rot = 0,
column_names_gp = gpar(fontsize = 12,fontface="italic"),
column_names_centered =TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
if(gwas_sig_mat[i, j] <0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
B. Lactate dehydrogenase release
RNAnormlist <- read.csv("output/TNNI_LDH_RNAnormlist.txt")
level_order2 <- c('75','87','77','79','78','71')
RNAnormlist <- RNAnormlist %>%
mutate(indv= factor(indv,levels= level_order2))
RNAnormlist %>%
mutate(Drug = factor(Drug, levels = c( "DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
ggplot(., aes(x=Drug, y=rldh))+
geom_boxplot(position = "identity", fill = drug_pal_fact)+
geom_point(aes(col=indv, size =3,alpha=0.5))+
geom_signif(comparisons =list(c("VEH","DOX"),
c("VEH","EPI"),
c("VEH","DNR"),
c("VEH","MTX"),
c("VEH","TRZ")),
test="t.test",
map_signif_level=TRUE,
textsize =4,
step_increase = 0.1)+
theme_classic()+
guides(size = "none",alpha="none")+
scale_color_brewer(palette = "Dark2", name = "Individual")+
xlab("")+
ylab("Relative LDH activity ")+
ggtitle("Lactate dehydrogenase release at 24 hours")+
theme_classic()+
theme(strip.background = element_rect(fill = "transparent")) +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
legend.position = "none",
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
### C. Troponin I release
RNAnormlist %>%
mutate(Drug = factor(Drug, levels = c( "DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
ggplot(., aes(x=Drug, y=rtnni))+
geom_boxplot(position = "identity", fill = drug_pal_fact)+
geom_point(aes(col=indv, size =3,alpha=0.5))+
geom_signif(comparisons =list(c("VEH","DOX"),
c("VEH","EPI"),
c("VEH","DNR"),
c("VEH","MTX"),
c("VEH","TRZ")),
test="t.test",
map_signif_level=TRUE,
textsize =4,
step_increase = 0.1)+
theme_classic()+
guides(size = "none",alpha="none")+
scale_color_brewer(palette = "Dark2", name = "Individual")+
xlab("")+
ylab("Relative Troponin I levels ")+
ggtitle("Troponin I release at 24 hours")+
theme_classic()+
theme(strip.background = element_rect(fill = "transparent")) +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
legend.position = "none",
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
For more Troponin I and LDH release analysis, check this link out
Cardiotoxicity Score code
gene_corr_fig9 <- readRDS("output/gene_corr_fig9.RDS")
toxlist <- data.frame(ENTREZID = c(5916, 23371, 283337, 64078, 80010))
toxtest_2pt <- gene_corr_fig9 %>%
rowwise() %>%
# mutate_all(~replace(., is.na(.), 0)) %>%
mutate(tox_score = mean(c(rldh, rtnni), na.rm = TRUE)) %>%
dplyr::select(entrezgene_id, hgnc_symbol, Drug, indv, time, counts, tox_score) %>%
filter(entrezgene_id %in% toxlist$ENTREZID)
toxdata2pt <- list()
for (hay in 1:5) {
data <- toxtest_2pt %>%
dplyr::filter(entrezgene_id == toxlist$ENTREZID[hay])
dataname <- unique(data$hgnc_symbol)
p <- ggplot(data, aes(x = tox_score, y = counts)) +
geom_point(aes(col = indv)) +
geom_smooth(method = "lm") +
facet_wrap(hgnc_symbol ~ Drug, scales = "free") +
theme_classic() +
scale_color_brewer(
palette = "Dark2",
name = "Individual",
label = c("1", "2", "3", "4", "5", "6")
) +
ggpubr::stat_cor(
method = "pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc = 1,
size = 3
)
tox_build <- ggplot_build(p)
plot(p)
toxdata2pt[[dataname]] <- list(
'tox_val' = c(tox_build$data[[3]]$r),
'sig_tox' = c(tox_build$data[[3]]$p.value)
)
}
extraction code for heatmap below gene expression boxes ### D. SNP-related gene expression and cardiotoxicity score
The extraction code for the horizontal heatmap below gene expression boxplots in the paper is located here.
RARG
cpm_boxplot24h(cpmcounts,GOI='5916',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("RARG")~log[2]~"cpm "))))
toxdata2ptr <- map_df(toxdata2pt, ~as.data.frame(.x), .id="gene")
RARG_sig_mat2ptr <- toxdata2ptr %>%
dplyr::filter(gene=="RARG") %>%
dplyr::select(sig_tox) %>%
t() %>%
matrix()
RARG_mat2ptr <- toxdata2ptr%>%
dplyr::filter(gene=="RARG") %>%
dplyr::select(tox_val) %>%
t() %>%
matrix()
Heatmap(RARG_mat2ptr, name = " RARG 2pt Cardiotox score",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
column_title_side = "top",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
column_names_rot = 0,
col= col_funTOX,
row_labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"),
column_names_gp = gpar(fontsize = 12,fontface="italic"),
column_names_centered = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
if(RARG_sig_mat2ptr[i, j] <0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
TNS2
cpm_boxplot24h(cpmcounts,GOI='23371',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("TNS2")~log[2]~"cpm "))))
TNS2_sig_mat2ptr <- toxdata2ptr %>%
dplyr::filter(gene=="TNS2") %>%
dplyr::select(sig_tox) %>%
t() %>%
matrix()
TNS2_mat2ptr <- toxdata2ptr%>%
dplyr::filter(gene=="TNS2") %>%
dplyr::select(tox_val) %>%
t() %>%
matrix()
Heatmap(TNS2_mat2ptr, name = " TNS2 2pt Cardiotox score",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
column_title_side = "top",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
column_names_rot = 0,
col= col_funTOX,
row_labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"),
column_names_gp = gpar(fontsize = 12,fontface="italic"),
column_names_centered = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
if(TNS2_sig_mat2ptr[i, j] <0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
ZNF740
cpm_boxplot24h(cpmcounts,GOI='283337',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("ZNF740")~log[2]~"cpm "))))
ZNF740_sig_mat2ptr <- toxdata2ptr %>%
dplyr::filter(gene=="ZNF740") %>%
dplyr::select(sig_tox) %>%
t() %>%
matrix()
ZNF740_mat2ptr <- toxdata2ptr%>%
dplyr::filter(gene=="ZNF740") %>%
dplyr::select(tox_val) %>%
t() %>%
matrix()
Heatmap(ZNF740_mat2ptr, name = " ZNF740 2pt Cardiotox score",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
column_title_side = "top",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
column_names_rot = 0,
col= col_funTOX,
row_labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"),
column_names_gp = gpar(fontsize = 12,fontface="italic"),
column_names_centered = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
if(ZNF740_sig_mat2ptr[i, j] <0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
RMI1
cpm_boxplot24h(cpmcounts,GOI='80010',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("RMI1")~log[2]~"cpm "))))
toxdata2ptr <- map_df(toxdata2pt, ~as.data.frame(.x), .id="gene")
RMI1_sig_mat2ptr <- toxdata2ptr %>%
dplyr::filter(gene=="RMI1") %>%
dplyr::select(sig_tox) %>%
t() %>%
matrix()
RMI1_mat2ptr <- toxdata2ptr%>%
dplyr::filter(gene=="RMI1") %>%
dplyr::select(tox_val) %>%
t() %>%
matrix()
Heatmap(RMI1_mat2ptr, name = " RMI1 2pt Cardiotox score",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
column_title_side = "top",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
column_names_rot = 0,
col= col_funTOX,
row_labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"),
column_names_gp = gpar(fontsize = 12,fontface="italic"),
column_names_centered = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
if(RMI1_sig_mat2ptr[i, j] <0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
| Version | Author | Date |
|---|---|---|
| 13d05d5 | reneeisnowhere | 2023-07-17 |
SLC28A3
cpm_boxplot24h(cpmcounts,GOI='64078',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("SLC28A3")~log[2]~"cpm "))))
toxdata2ptr <- map_df(toxdata2pt, ~as.data.frame(.x), .id="gene")
SLC28A3_sig_mat2ptr <- toxdata2ptr %>%
dplyr::filter(gene=="SLC28A3") %>%
dplyr::select(sig_tox) %>%
t() %>%
matrix()
SLC28A3_mat2ptr <- toxdata2ptr%>%
dplyr::filter(gene=="SLC28A3") %>%
dplyr::select(tox_val) %>%
t() %>%
matrix()
Heatmap(SLC28A3_mat2ptr, name = " SLC28A3 2pt Cardiotox score",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
column_title_side = "top",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
column_names_rot = 0,
col= col_funTOX,
row_labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"),
column_names_gp = gpar(fontsize = 12,fontface="italic"),
column_names_centered = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
if(SLC28A3_sig_mat2ptr[i, j] <0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
| Version | Author | Date |
|---|---|---|
| 13d05d5 | reneeisnowhere | 2023-07-17 |
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.16.0 broom_1.0.5 kableExtra_1.3.4
[4] sjmisc_2.8.9 scales_1.2.1 ggpubr_0.6.0
[7] cowplot_1.1.1 RColorBrewer_1.1-3 ggsignif_0.6.4
[10] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[16] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3
[19] tidyverse_2.0.0 edgeR_3.42.4 limma_3.56.2
[22] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rlang_1.1.1 magrittr_2.0.3 clue_0.3-64
[4] GetoptLong_1.0.5 git2r_0.32.0 matrixStats_1.0.0
[7] compiler_4.3.1 mgcv_1.9-0 getPass_0.2-2
[10] png_0.1-8 systemfonts_1.0.4 callr_3.7.3
[13] vctrs_0.6.3 rvest_1.0.3 pkgconfig_2.0.3
[16] shape_1.4.6 crayon_1.5.2 fastmap_1.1.1
[19] backports_1.4.1 magick_2.7.5 labeling_0.4.3
[22] utf8_1.2.3 promises_1.2.1 rmarkdown_2.24
[25] tzdb_0.4.0 ps_1.7.5 xfun_0.40
[28] cachem_1.0.8 jsonlite_1.8.7 later_1.3.1
[31] parallel_4.3.1 cluster_2.1.4 R6_2.5.1
[34] bslib_0.5.1 stringi_1.7.12 car_3.1-2
[37] jquerylib_0.1.4 Rcpp_1.0.11 iterators_1.0.14
[40] knitr_1.44 IRanges_2.34.1 Matrix_1.6-1
[43] splines_4.3.1 httpuv_1.6.11 timechange_0.2.0
[46] tidyselect_1.2.0 rstudioapi_0.15.0 abind_1.4-5
[49] yaml_2.3.7 doParallel_1.0.17 codetools_0.2-19
[52] sjlabelled_1.2.0 processx_3.8.2 lattice_0.21-8
[55] withr_2.5.0 evaluate_0.21 xml2_1.3.5
[58] circlize_0.4.15 pillar_1.9.0 carData_3.0-5
[61] whisker_0.4.1 foreach_1.5.2 stats4_4.3.1
[64] insight_0.19.5 generics_0.1.3 rprojroot_2.0.3
[67] hms_1.1.3 S4Vectors_0.38.1 munsell_0.5.0
[70] glue_1.6.2 tools_4.3.1 locfit_1.5-9.8
[73] webshot_0.5.5 fs_1.6.3 colorspace_2.1-0
[76] nlme_3.1-163 cli_3.6.1 fansi_1.0.4
[79] viridisLite_0.4.2 svglite_2.1.1 gtable_0.3.4
[82] rstatix_0.7.2 sass_0.4.7 digest_0.6.33
[85] BiocGenerics_0.46.0 farver_2.1.1 rjson_0.2.21
[88] htmltools_0.5.6 lifecycle_1.0.3 httr_1.4.7
[91] GlobalOptions_0.1.2