Overview of study design and samples
Last updated: 2025-03-27
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Knit directory: hruban_wflow/
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New: data/pacto_bins.csv
New: data/pacto_features.csv
New: data/sel_chr.txt
New: data/supplementary_tables.xlsx
New: data/tumor_tmb.txt
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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Description of CheckPAC and PACTO patients at baseline. Categories show the location of tumor, the number of metastatic sites, the time between initial diagnosis and trial enrollment, prior surgery, smoking status, sex, method of assessment of progression, trial arm, and cancer stage.
library(VplotR)
library(forcats)
library(grid)
library(here)
library(readxl)
library(scales)
library(tidyverse)df2<-read_excel(here("data/for_leal_plot_042324.xlsx"), sheet = "for_leal_plot_pacto")
df2$Subfeature [1] "4" "3" "CheckPAC arm 1"
[4] "CheckPAC arm 2" "PACTO arm 1" "PACTO arm 2"
[7] "30-39" "40-49" "50-59"
[10] "60-69" "70-79" "80-89"
[13] "Radiological evidence" "Clinical" "Both"
[16] "Unknown" "M" "F"
[19] "Previous" "Current" "Not known"
[22] "Never" "No" "Yes"
[25] "<2 months" "0-1 years" "1-2 years"
[28] "2-3 years" "3-4 years" "1 site"
[31] "2 sites" "3 sites" "4 sites"
[34] "5 sites" "6 sites" "7 sites"
[37] "Body" "Head" "Tail"
[40] "Body,Tail" "Head,Body" "Uncinate Process"
[43] "Pancreas NOS" "Head, Uncinate process"#calculate value for number of subfeatures within each feature
tt <- with(df2[!duplicated(df2$Subfeature), ], table(Group))
#set color palette for each feature based on the number of subfeatures
green <- brewer_pal(pal = "Greens")(tt[names(tt) == "Stage"])
blue <- brewer_pal(pal = "Blues")(tt[names(tt) == "Arm"])
red <- brewer_pal(pal = "Reds")(tt[names(tt) == "Age"])
orange <- brewer_pal(pal = "Oranges")(tt[names(tt) == "Progression"])
purple <- brewer_pal(pal = "Purples")(tt[names(tt) == "Sex"])
purd <- brewer_pal(pal = "PuRd")(tt[names(tt) == "Smoking"])
bupu <- brewer_pal(pal = "BuPu")(tt[names(tt) == "Surgery"])
rdpu <- brewer_pal(pal = "RdPu")(tt[names(tt) == "Time"])
orrd <- brewer_pal(pal = "OrRd")(tt[names(tt) == "Sites"])
ylgn <- brewer_pal(pal = "YlGn")(tt[names(tt) == "Location"])
#create data frame of subfeatures with corresponding color filled in
Subfeature_df <- df2[!duplicated(df2$Subfeature), c("Subfeature", "Group")]
Subfeature_df$Group <- factor(Subfeature_df$Group, levels = c("Stage", "Arm", "Age", "Progression", "Sex", "Smoking", "Surgery", "Time", "Sites", "Location"))
Subfeature_df <- arrange(Subfeature_df, Group, Subfeature)
Subfeature_df$fill <- c(green, blue, red, orange, purple, purd, bupu, rdpu, orrd, ylgn)
Subfeature_df$Subfeature <- factor(Subfeature_df$Subfeature, levels = Subfeature_df$Subfeature)
df2$Subfeature <- factor(df2$Subfeature, levels = Subfeature_df$Subfeature)
levels(df2$Feature) <- gsub(" ", "\n", levels(df2$Feature))
#generate plot based on df2
g1 <- ggplot(data = df2, aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
labs(x = "", y = "Patients") +
scale_fill_manual(values = Subfeature_df$fill, name = "Clinical Feature") +
scale_x_discrete(limits = c("Stage", "Arm", "Age", "Progression", "Sex", "Smoking", "Surgery", "Time", "Sites", "Location")) +
theme_void() +
scale_y_continuous(trans = "reverse", position = "right", labels = scales::percent) +
theme(axis.text.x = element_text(colour = "black", size = 12)) +
theme(axis.text.y = element_blank()) +
theme(axis.title.y = element_blank()) +
theme(axis.title.x = element_blank()) +
coord_flip(ylim = c(0,1)) +
theme(legend.position = "bottom")
# second format of same kind of figure with key, this time plotting as "arm"
stage <- ggplot(data = df2[df2$Group == "Stage", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = green, name = "Stage" )
tmp <- ggplot_gtable(ggplot_build(stage))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_stage <- tmp$grobs[[leg]]
arm <- ggplot(data = df2[df2$Group == "Arm", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = blue, name = "Trial Arm" )
tmp <- ggplot_gtable(ggplot_build(arm))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_arm <- tmp$grobs[[leg]]
age <- ggplot(data = df2[df2$Group == "Age", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = red, name = "Age" )
tmp <- ggplot_gtable(ggplot_build(age))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_age <- tmp$grobs[[leg]]
progression <- ggplot(data = df2[df2$Group == "Progression", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = orange, name = "Method of progression" )
tmp <- ggplot_gtable(ggplot_build(progression))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_progression <- tmp$grobs[[leg]]
sex <- ggplot(data = df2[df2$Group == "Sex", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = purple, name = "Sex" )
tmp <- ggplot_gtable(ggplot_build(sex))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_sex <- tmp$grobs[[leg]]
smoking <- ggplot(data = df2[df2$Group == "Smoking", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = purd, name = "Smoking status" )
tmp <- ggplot_gtable(ggplot_build(smoking))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_smoking <- tmp$grobs[[leg]]
surgery <- ggplot(data = df2[df2$Group == "Surgery", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = bupu, name = "Prior surgery" )
tmp <- ggplot_gtable(ggplot_build(surgery))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_surgery <- tmp$grobs[[leg]]
time <- ggplot(data = df2[df2$Group == "Time", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = rdpu, name = "Time since first PDAC diagnosis" )
tmp <- ggplot_gtable(ggplot_build(time))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_time <- tmp$grobs[[leg]]
sites <- ggplot(data = df2[df2$Group == "Sites", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = orrd, name = "Number of metastatic sites" )
tmp <- ggplot_gtable(ggplot_build(sites))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_sites <- tmp$grobs[[leg]]
location <- ggplot(data = df2[df2$Group == "Location", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = ylgn, name = "Location" )
tmp <- ggplot_gtable(ggplot_build(location))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_location <- tmp$grobs[[leg]]# Checkpac plots
#load in data frame to be used
df2_c<-read_excel(here("data/for_leal_plot_042324.xlsx"), sheet = "for_leal_plot_checkpac")
df2_c$Subfeature [1] "3" "4" "CheckPAC arm 1"
[4] "CheckPAC arm 2" "PACTO arm 1" "PACTO arm 2"
[7] "30-39" "40-49" "50-59"
[10] "60-69" "70-79" "80-89"
[13] "Radiological evidence" "Clinical" "Both"
[16] "Unknown" "M" "F"
[19] "Previous" "Current" "Not known"
[22] "Never" "No" "Yes"
[25] "<2 months" "0-1 years" "1-2 years"
[28] "2-3 years" "3-4 years" "1 site"
[31] "2 sites" "3 sites" "4 sites"
[34] "5 sites" "6 sides" "7 sites"
[37] "Body" "Head" "Tail"
[40] "Body,Tail" "Head,Body" "Uncinate Process"
[43] "Pancreas NOS" "Head, Uncinate process"#calculate value for number of subfeatures within each feature
tt <- with(df2_c[!duplicated(df2_c$Subfeature), ], table(Group))
#set color palette for each feature based on the number of subfeatures
green <- brewer_pal(pal = "Greens")(tt[names(tt) == "Stage"])
blue <- brewer_pal(pal = "Blues")(tt[names(tt) == "Arm"])
red <- brewer_pal(pal = "Reds")(tt[names(tt) == "Age"])
orange <- brewer_pal(pal = "Oranges")(tt[names(tt) == "Progression"])
purple <- brewer_pal(pal = "Purples")(tt[names(tt) == "Sex"])
purd <- brewer_pal(pal = "PuRd")(tt[names(tt) == "Smoking"])
bupu <- brewer_pal(pal = "BuPu")(tt[names(tt) == "Surgery"])
rdpu <- brewer_pal(pal = "RdPu")(tt[names(tt) == "Time"])
orrd <- brewer_pal(pal = "OrRd")(tt[names(tt) == "Sites"])
ylgn <- brewer_pal(pal = "YlGn")(tt[names(tt) == "Location"])
#create data frame of subfeatures with corresponding color filled in
Subfeature_df_c <- df2_c[!duplicated(df2_c$Subfeature), c("Subfeature", "Group")]
Subfeature_df_c$Group <- factor(Subfeature_df_c$Group, levels = c("Stage", "Arm", "Age", "Progression", "Sex", "Smoking", "Surgery", "Time", "Sites", "Location"))
Subfeature_df_c <- arrange(Subfeature_df_c, Group, Subfeature)
Subfeature_df_c$fill <- c(green, blue, red, orange, purple, purd, bupu, rdpu, orrd, ylgn)
Subfeature_df_c$Subfeature <- factor(Subfeature_df_c$Subfeature, levels = Subfeature_df_c$Subfeature)
df2_c$Subfeature <- factor(df2_c$Subfeature, levels = Subfeature_df_c$Subfeature)
levels(df2_c$Feature) <- gsub(" ", "\n", levels(df2_c$Feature))
#generate plot based on df2_c
g1_c <- ggplot(data = df2_c, aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
labs(x = "", y = "Patients") +
scale_fill_manual(values = Subfeature_df$fill, name = "Clinical Feature") +
scale_x_discrete(limits = c("Stage", "Arm", "Age", "Progression", "Sex", "Smoking", "Surgery", "Time", "Sites", "Location")) +
theme_void() +
scale_y_continuous(trans = "reverse", position = "right", labels = scales::percent) +
theme(axis.text.x = element_blank()) +
theme(axis.text.y = element_text(colour = "black", size = 12)) +
theme(axis.title.y = element_blank()) +
theme(axis.title.x = element_blank()) +
coord_flip(ylim = c(0,1)) +
theme(legend.position = "bottom")
# second format of same kind of figure with key, this time plotting as "arm"
stage_c <- ggplot(data = df2_c[df2_c$Group == "Stage", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = green, name = "Stage" )
tmp <- ggplot_gtable(ggplot_build(stage))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_stage <- tmp$grobs[[leg]]
arm_c <- ggplot(data = df2_c[df2_c$Group == "Arm", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = blue, name = "Trial Arm" )
tmp <- ggplot_gtable(ggplot_build(arm))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_arm <- tmp$grobs[[leg]]
age_c <- ggplot(data = df2_c[df2_c$Group == "Age", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = red, name = "Age" )
tmp <- ggplot_gtable(ggplot_build(age))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_age <- tmp$grobs[[leg]]
progression_c <- ggplot(data = df2_c[df2_c$Group == "Progression", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = orange, name = "Method of progression" )
tmp <- ggplot_gtable(ggplot_build(progression))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_progression <- tmp$grobs[[leg]]
sex_c <- ggplot(data = df2_c[df2_c$Group == "Sex", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = purple, name = "Sex" )
tmp <- ggplot_gtable(ggplot_build(sex))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_sex <- tmp$grobs[[leg]]
smoking_c <- ggplot(data = df2_c[df2_c$Group == "Smoking", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = purd, name = "Smoking status" )
tmp <- ggplot_gtable(ggplot_build(smoking))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_smoking <- tmp$grobs[[leg]]
surgery_c <- ggplot(data = df2_c[df2_c$Group == "Surgery", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = bupu, name = "Prior surgery" )
tmp <- ggplot_gtable(ggplot_build(surgery))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_surgery <- tmp$grobs[[leg]]
time_c<- ggplot(data = df2_c[df2_c$Group == "Time", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") + theme(text = element_text(size = 20)) +
scale_fill_manual(values = rdpu, name = "Time since first PDAC diagnosis" )
tmp <- ggplot_gtable(ggplot_build(time))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_time <- tmp$grobs[[leg]]
sites_c <- ggplot(data = df2_c[df2_c$Group == "Sites", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = orrd, name = "Number of metastatic sites" )
tmp <- ggplot_gtable(ggplot_build(sites))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_sites <- tmp$grobs[[leg]]
location_c <- ggplot(data = df2_c[df2_c$Group == "Location", ],
aes(x = as.factor(Feature),
y = Patients,
fill = Subfeature)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = ylgn, name = "Location" )
tmp <- ggplot_gtable(ggplot_build(location))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend_location <- tmp$grobs[[leg]]#generate viewport locations for each of the legend boxes
vp_legend_location <- viewport(x = 0.1, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_sites <- viewport(x = 0.22, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_time <- viewport(x = 0.36, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_surgery <- viewport(x = 0.46, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_smoking <- viewport(x = 0.54, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_sex<- viewport(x = 0.62, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_progression<- viewport(x = 0.7, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_age<- viewport(x = 0.78, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_arm<- viewport(x = 0.88, y = 1.5, width = 2, height = 2, just = "top")
vp_legend_stage<- viewport(x = 0.95, y = 1.5, width = 2, height = 2, just = "top")
vp_plot_a<-viewport(x=.27, y=.6, width=.52, height=.7)
vp_plot_b<-viewport(x=.75, y=.6, width=.48, height=.7)
grid.newpage()
# generate pdf version of plot by adding in viewports to g1_c
pushViewport(viewport(x = 0.5, y = 0.6, width = 1, height = 0.7))
print(g1_c + theme(legend.position = "none"), vp = vp_plot_a)Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_bar()`).print(g1 + theme(legend.position = "none"), vp = vp_plot_b)Warning: Removed 9 rows containing missing values or values outside the scale range
(`geom_bar()`).upViewport(1)
pushViewport(viewport(y=0.23, width=1, height=0.35, name="B"))
grid.rect(gp=gpar(col="white"))
pushViewport(vp_legend_stage)
grid.draw(legend_stage)
upViewport(1)
pushViewport(vp_legend_arm)
grid.draw(legend_arm)
upViewport(1)
pushViewport(vp_legend_age)
grid.draw(legend_age)
upViewport(1)
pushViewport(vp_legend_progression)
grid.draw(legend_progression)
upViewport(1)
pushViewport(vp_legend_sex)
grid.draw(legend_sex)
upViewport(1)
pushViewport(vp_legend_smoking)
grid.draw(legend_smoking)
upViewport(1)
pushViewport(vp_legend_surgery)
grid.draw(legend_surgery)
upViewport(1)
pushViewport(vp_legend_time)
grid.draw(legend_time)
upViewport(1)
pushViewport(vp_legend_sites)
grid.draw(legend_sites)
upViewport(1)
pushViewport(vp_legend_location)
grid.draw(legend_location)
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 15.3.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] lubridate_1.9.4 stringr_1.5.1 dplyr_1.1.4
[4] purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
[7] tibble_3.2.1 tidyverse_2.0.0 scales_1.3.0
[10] readxl_1.4.5 here_1.0.1 forcats_1.0.0
[13] VplotR_1.14.0 ggplot2_3.5.1 GenomicRanges_1.56.2
[16] GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1
[19] BiocGenerics_0.50.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 farver_2.1.2
[3] Biostrings_2.72.1 bitops_1.0-9
[5] fastmap_1.2.0 GenomicAlignments_1.40.0
[7] promises_1.3.2 digest_0.6.37
[9] timechange_0.3.0 lifecycle_1.0.4
[11] processx_3.8.6 magrittr_2.0.3
[13] compiler_4.4.1 rlang_1.1.5
[15] sass_0.4.9 tools_4.4.1
[17] yaml_2.3.10 knitr_1.49
[19] labeling_0.4.3 S4Arrays_1.4.1
[21] DelayedArray_0.30.1 plyr_1.8.9
[23] RColorBrewer_1.1-3 abind_1.4-8
[25] BiocParallel_1.38.0 withr_3.0.2
[27] git2r_0.35.0 colorspace_2.1-1
[29] SummarizedExperiment_1.34.0 cli_3.6.4
[31] rmarkdown_2.29 crayon_1.5.3
[33] generics_0.1.3 rstudioapi_0.17.1
[35] tzdb_0.4.0 httr_1.4.7
[37] reshape2_1.4.4 cachem_1.1.0
[39] zlibbioc_1.50.0 parallel_4.4.1
[41] cellranger_1.1.0 XVector_0.44.0
[43] matrixStats_1.5.0 vctrs_0.6.5
[45] Matrix_1.7-3 jsonlite_1.9.1
[47] callr_3.7.6 hms_1.1.3
[49] jquerylib_0.1.4 glue_1.8.0
[51] codetools_0.2-20 ps_1.9.0
[53] cowplot_1.1.3 stringi_1.8.4
[55] gtable_0.3.6 later_1.4.1
[57] UCSC.utils_1.0.0 munsell_0.5.1
[59] pillar_1.10.1 htmltools_0.5.8.1
[61] GenomeInfoDbData_1.2.12 R6_2.6.1
[63] rprojroot_2.0.4 evaluate_1.0.3
[65] lattice_0.22-6 Biobase_2.64.0
[67] Rsamtools_2.20.0 httpuv_1.6.15
[69] bslib_0.9.0 Rcpp_1.0.14
[71] SparseArray_1.4.8 whisker_0.4.1
[73] xfun_0.51 fs_1.6.5
[75] MatrixGenerics_1.16.0 zoo_1.8-13
[77] getPass_0.2-4 pkgconfig_2.0.3