In shbrief/GenomicSuperSignaturePaper: Reproduce GenomicSuperSignature paper
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, message = FALSE,
fig.width = 5, fig.height = 3.5)
Setup
Background
Using training and validation data of the original CRC study,
we compared associations between different subtype models and RAVs with the same
clinicopathological variables. Notably, these data were not part of RAV training
and are microarray datasets whereas the RAVs were trained exclusively from
RNA-seq data.
As described previously in CRC study, we used the likelihood-ratio test (LRT) to
compare the different subtype models for association with clinicopathological
variables. A p-value near 1 (-log10p-value near 0) means that no additional
information is provided by a full model composed of two subtype definitions
compared to a model with only one.
CMS-associated RAVs performed better than discrete CMS on all four phenotypes (plot) and also outperformed PCSSs except on tumor location (plot). Interestingly, PCSS-associated RAVs were still better than CMS but
slightly worse than PCSSs (plot), while CMS-associated RAVs were better than both CMS and PCSSs (plot), indicating that RAVs contain more comprehensive information. This performance improvement became more significant using only the 10 original validation datasets, excluding 8 datasets used to train the PCSS model. In conclusion, RAVs trained from heterogeneous datasets, not specific to CRC, captured biologically-relevant signatures for CRC as well or superior to focused efforts using CRC-specific databases, suggesting that RAVs are for general-use and can be applied to describe other diseases as well.
Select training dataset
CRC paper actually used both training and validation datasets (total of 18)
for Figure 4.
If you want to use all 18 datasets, assign the variable num_dat = 18. If you
want only 10 CRC datasets excluding 8 datasets used to build PCSSs, assign
num_dat = 10.
Required inputs
- 3 environmental variables for
Fig4C_CMSvs.R:
m2_name (column name for the model to be compared to CMS) m2_1 and m2_2 (names of the two model variables that form the group to
be compared to CMS) -
num_dat for the number of CRC datasets for testing (18 or 10)
-
6 environmental variables for Fig4C_contScores.R:
m1_name, m1_1, m1_2 for one model m2_name, m2_1, m2_2 for the othernum_dat for the number of CRC datasets for testing (18 or 10)
# width : width of boxplot
# ymin or xmin : lower whisker = smallest observation greater than or equal to lower hinge - 1.5 *
# IQR
# lower or xlower : lower hinge, 25% quantile
# notchlower : lower edge of notch = median - 1.58 * IQR / sqrt(n)
# middle or xmiddle : median, 50% quantile
# notchupper : upper edge of notch = median + 1.58 * IQR / sqrt(n)
# upper or xupper : upper hinge, 75% quantile
# ymax or xmax : upper whisker = largest observation less than or equal to upper hinge + 1.5 * IQR
boxplot_summary <- as.data.frame(matrix(nrow = 8*9, ncol = 11))
colnames(boxplot_summary) <- c("figure", "panel", "group", "datasets_used",
"minima", "whisker_min", "first_quartile",
"median", "third_quartile", "whisker_max",
"maxima")
boxplot_fig <- c("3B","3C","Sup5B","Sup5C","Sup5E","Sup5F","Sup6C","Sup6D","Sup6E")
boxplot_summary$figure <- rep(boxplot_fig, each = 8)
boxplot_summary$panel <- rep(rep(c(1,2,3,4), each = 2), 9)
boxplot_summary$group <- rep(c(1,2), 36)
## 4 panels and 2 groups
ref <- data.frame(panel = rep(1:4, each = 2), group = 1:2)
##### statsSummary function to extract boxplot stats
# plotRes : ggplot object (e.g. x$value from the above script)
# figName : name of the figure in the manuscript
statsSummary <- function(plotRes, figName) {
res <- ggplot_build(plotRes)
res_box <- res$data[[1]] # Summary
res_dat <- res$data[[2]] # Data points
ind <- which(boxplot_summary$figure == figName)
## the number of datasets used
for (i in seq_len(nrow(ref))) {
panel <- ref$panel[i]
group <- ref$group[i]
sub <- res_dat[which(res_dat$PANEL == panel & res_dat$group == group),]
## row to add sample size info
datasets_used_ind <- which(boxplot_summary$figure == figName &
boxplot_summary$panel == panel &
boxplot_summary$group == group)
boxplot_summary$datasets_used[datasets_used_ind] <- sum(!is.na(sub$y))
}
## if there is no outlier, just assign median value for the function
no_outlier_ind <- which(isEmpty(res_box$outliers))
res_box$outliers[no_outlier_ind] <- res_box$middle[no_outlier_ind]
boxplot_summary$minima[ind] <- sapply(res_box$ymin, pmin, as.numeric(unlist(res_box$outliers))) %>% diag
boxplot_summary$whisker_min[ind] <- res_box$ymin # lower extreme
boxplot_summary$first_quartile[ind] <- res_box$lower # 25% quartile (box min)
boxplot_summary$median[ind] <- res_box$middle # 50% quartile
boxplot_summary$third_quartile[ind] <- res_box$upper # 75% quartile (box max)
boxplot_summary$whisker_max[ind] <- res_box$ymax
boxplot_summary$maxima[ind] <- sapply(res_box$ymax, pmax, as.numeric(unlist(res_box$outliers))) %>% diag
return(boxplot_summary)
}
CMS vs. PCSS1/2
First, we reproduce the previous result comparing the performance of PCSSs
(continuous score) against CMS (discrete score).
18 CRC validation datasets
m2_name <- "PCSS"
m2_1 <- "PCSS1"
m2_2 <- "PCSS2"
num_dat <- 18
x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_PCSS.rds"))
10 CRC validation datasets
m2_name <- "PCSS"
m2_1 <- "PCSS1"
m2_2 <- "PCSS2"
num_dat <- 10
x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup6C")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_PCSS.rds"))
Figures in the paper
Here is the summary of boxplots in our paper.
| Figures | Models | # validation data |
|------------|----------|-------------------|
| Fig.3B | CMS vs. RAV834/833 | 18 |
| Fig.3C | PCSS1/2 vs. RAV834/833 | 18 |
| Sup.Fig.5B | CMS vs. RAV1575/834 | 18 |
| Sup.Fig.5C | PCSS1/2 vs. RAV1575/834 | 18 |
| Sup.Fig.5E | CMS vs. RAV832/188 | 18 |
| Sup.Fig.5F | PCSS1/2 vs. RAV832/188 | 18 |
| Fig.6C | CMS vs. PCSS1/2 | 10 |
| Fig.6D | CMS vs. RAV834/833 | 10 |
| Fig.6E | PCSS1/2 vs. RAV834/833 | 10 |
Compare to CMS
CMS vs. RAV1575/834
RAV1575/834 are the most similar RAVs to PCSS1/2, respectively, based on
Pearson correlation.
m2_name <- "RAV"
m2_1 <- "RAV1575"
m2_2 <- "RAV834"
num_dat <- 18
x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5B")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_1575_834.rds"))
CMS vs. RAV834/833
RAV834/833 have the highest r-squared score when we compared the sample scores
against the metadata.
18 CRC validation datasets
m2_name <- "RAV"
m2_1 <- "RAV834"
m2_2 <- "RAV833"
num_dat <- 18
x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "3B")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_834_833.rds"))
png("outputs/png/boxplot_CMS_vs_834_833.png", width = 600, height = 500)
print(x)$visible
dev.off()
10 CRC validation datasets
m2_name <- "RAV"
m2_1 <- "RAV834"
m2_2 <- "RAV833"
num_dat <- 10
x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup6D")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_834_833.rds"))
png("outputs/png/boxplot_CMS_vs_834_833.png", width = 600, height = 500)
print(x)$visible
dev.off()
CMS vs. RAV832/188
RAV832/188 are the most frequently validated RAVs for 18 CRC datasets.
m2_name <- "RAV"
m2_1 <- "RAV832"
m2_2 <- "RAV188"
num_dat <- 18
x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5E")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_832_188.rds"))
png("outputs/png/boxplot_CMS_vs_832_188.png", width = 600, height = 500)
print(x)$visible
dev.off()
Compare to PCSS
PCSS vs. RAV1575/834
RAV1575/834 are the most similar RAVs to PCSS1/2, respectively, based on
Pearson correlation.
m1_name <- "PCSS"
m1_1 <- "PCSS1"
m1_2 <- "PCSS2"
m2_name <- "RAV"
m2_1 <- "RAV1575"
m2_2 <- "RAV834"
num_dat <- 18
x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5C")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_1575_834.rds"))
PCSS vs. RAV834/833
RAV834/833 have the highest r-squared score when we compared the sample scores
against the metadata.
18 CRC validation datasets
m1_name <- "PCSS"
m1_1 <- "PCSS1"
m1_2 <- "PCSS2"
m2_name <- "RAV"
m2_1 <- "RAV834"
m2_2 <- "RAV833"
num_dat <- 18
x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "3C")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_834_833.rds"))
png("outputs/png/boxplot_PCSS_vs_834_833.png", width = 600, height = 500)
print(x)$visible
dev.off()
10 CRC validation datasets
m1_name <- "PCSS"
m1_1 <- "PCSS1"
m1_2 <- "PCSS2"
m2_name <- "RAV"
m2_1 <- "RAV834"
m2_2 <- "RAV833"
num_dat <- 10
x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup6E")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_834_833.rds"))
PCSS vs. RAV832/188
RAV832/188 are the most frequently validated RAVs for 18 CRC datasets.
m1_name <- "PCSS"
m1_1 <- "PCSS1"
m1_2 <- "PCSS2"
m2_name <- "RAV"
m2_1 <- "RAV832"
m2_2 <- "RAV188"
num_dat <- 18
x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5F")
dirName <- paste0(num_dat, "_CRC_datasets")
saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_832_188.rds"))
PCSS vs. RAV834/3290
RAV3290 is associated with "stage" metadata of CRC datasets.
m1_name <- "PCSS"
m1_1 <- "PCSS1"
m1_2 <- "PCSS2"
m2_name <- "RAV"
m2_1 <- "RAV834"
m2_2 <- "RAV3290"
num_dat <- 10
x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
saveRDS(x, "outputs/boxplot_PCSS_vs_834_3290.rds")
PCSS vs. RAV834/596
RAV596 is associated with "grade" metadata of CRC datasets.
m1_name <- "PCSS"
m1_1 <- "PCSS1"
m1_2 <- "PCSS2"
m2_name <- "RAV"
m2_1 <- "RAV834"
m2_2 <- "RAV596"
num_dat <- 10
x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
saveRDS(x, "outputs/boxplot_PCSS_vs_834_596.rds")
##### Change Supplementary Figures number
boxplot_fig <- c("3B","3C","Sup8B","Sup8C","Sup8E","Sup8F","Sup9C","Sup9D","Sup9E")
boxplot_summary$figure <- rep(boxplot_fig, each = 8)
write.csv(boxplot_summary, "outputs/boxplot_summary.csv", row.names = FALSE)
Session Info
sessionInfo()
shbrief/GenomicSuperSignaturePaper documentation built on Aug. 2, 2022, 2:04 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, message = FALSE, fig.width = 5, fig.height = 3.5)
Setup
Background
Using training and validation data of the original CRC study, we compared associations between different subtype models and RAVs with the same clinicopathological variables. Notably, these data were not part of RAV training and are microarray datasets whereas the RAVs were trained exclusively from RNA-seq data.
As described previously in CRC study, we used the likelihood-ratio test (LRT) to compare the different subtype models for association with clinicopathological variables. A p-value near 1 (-log10p-value near 0) means that no additional information is provided by a full model composed of two subtype definitions compared to a model with only one.
CMS-associated RAVs performed better than discrete CMS on all four phenotypes (plot) and also outperformed PCSSs except on tumor location (plot). Interestingly, PCSS-associated RAVs were still better than CMS but slightly worse than PCSSs (plot), while CMS-associated RAVs were better than both CMS and PCSSs (plot), indicating that RAVs contain more comprehensive information. This performance improvement became more significant using only the 10 original validation datasets, excluding 8 datasets used to train the PCSS model. In conclusion, RAVs trained from heterogeneous datasets, not specific to CRC, captured biologically-relevant signatures for CRC as well or superior to focused efforts using CRC-specific databases, suggesting that RAVs are for general-use and can be applied to describe other diseases as well.
Select training dataset
CRC paper actually used both training and validation datasets (total of 18)
for Figure 4.
If you want to use all 18 datasets, assign the variable num_dat = 18. If you
want only 10 CRC datasets excluding 8 datasets used to build PCSSs, assign
num_dat = 10.
Required inputs
- 3 environmental variables for
Fig4C_CMSvs.R: m2_name(column name for the model to be compared to CMS)m2_1andm2_2(names of the two model variables that form the group to be compared to CMS)-
num_datfor the number of CRC datasets for testing (18 or 10) -
6 environmental variables for
Fig4C_contScores.R: m1_name,m1_1,m1_2for one modelm2_name,m2_1,m2_2for the othernum_datfor the number of CRC datasets for testing (18 or 10)
# width : width of boxplot # ymin or xmin : lower whisker = smallest observation greater than or equal to lower hinge - 1.5 * # IQR # lower or xlower : lower hinge, 25% quantile # notchlower : lower edge of notch = median - 1.58 * IQR / sqrt(n) # middle or xmiddle : median, 50% quantile # notchupper : upper edge of notch = median + 1.58 * IQR / sqrt(n) # upper or xupper : upper hinge, 75% quantile # ymax or xmax : upper whisker = largest observation less than or equal to upper hinge + 1.5 * IQR boxplot_summary <- as.data.frame(matrix(nrow = 8*9, ncol = 11)) colnames(boxplot_summary) <- c("figure", "panel", "group", "datasets_used", "minima", "whisker_min", "first_quartile", "median", "third_quartile", "whisker_max", "maxima") boxplot_fig <- c("3B","3C","Sup5B","Sup5C","Sup5E","Sup5F","Sup6C","Sup6D","Sup6E") boxplot_summary$figure <- rep(boxplot_fig, each = 8) boxplot_summary$panel <- rep(rep(c(1,2,3,4), each = 2), 9) boxplot_summary$group <- rep(c(1,2), 36) ## 4 panels and 2 groups ref <- data.frame(panel = rep(1:4, each = 2), group = 1:2) ##### statsSummary function to extract boxplot stats # plotRes : ggplot object (e.g. x$value from the above script) # figName : name of the figure in the manuscript statsSummary <- function(plotRes, figName) { res <- ggplot_build(plotRes) res_box <- res$data[[1]] # Summary res_dat <- res$data[[2]] # Data points ind <- which(boxplot_summary$figure == figName) ## the number of datasets used for (i in seq_len(nrow(ref))) { panel <- ref$panel[i] group <- ref$group[i] sub <- res_dat[which(res_dat$PANEL == panel & res_dat$group == group),] ## row to add sample size info datasets_used_ind <- which(boxplot_summary$figure == figName & boxplot_summary$panel == panel & boxplot_summary$group == group) boxplot_summary$datasets_used[datasets_used_ind] <- sum(!is.na(sub$y)) } ## if there is no outlier, just assign median value for the function no_outlier_ind <- which(isEmpty(res_box$outliers)) res_box$outliers[no_outlier_ind] <- res_box$middle[no_outlier_ind] boxplot_summary$minima[ind] <- sapply(res_box$ymin, pmin, as.numeric(unlist(res_box$outliers))) %>% diag boxplot_summary$whisker_min[ind] <- res_box$ymin # lower extreme boxplot_summary$first_quartile[ind] <- res_box$lower # 25% quartile (box min) boxplot_summary$median[ind] <- res_box$middle # 50% quartile boxplot_summary$third_quartile[ind] <- res_box$upper # 75% quartile (box max) boxplot_summary$whisker_max[ind] <- res_box$ymax boxplot_summary$maxima[ind] <- sapply(res_box$ymax, pmax, as.numeric(unlist(res_box$outliers))) %>% diag return(boxplot_summary) }
CMS vs. PCSS1/2
First, we reproduce the previous result comparing the performance of PCSSs (continuous score) against CMS (discrete score).
18 CRC validation datasets
m2_name <- "PCSS" m2_1 <- "PCSS1" m2_2 <- "PCSS2" num_dat <- 18 x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_PCSS.rds"))
10 CRC validation datasets
m2_name <- "PCSS" m2_1 <- "PCSS1" m2_2 <- "PCSS2" num_dat <- 10 x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup6C") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_PCSS.rds"))
Figures in the paper
Here is the summary of boxplots in our paper.
| Figures | Models | # validation data | |------------|----------|-------------------| | Fig.3B | CMS vs. RAV834/833 | 18 | | Fig.3C | PCSS1/2 vs. RAV834/833 | 18 | | Sup.Fig.5B | CMS vs. RAV1575/834 | 18 | | Sup.Fig.5C | PCSS1/2 vs. RAV1575/834 | 18 | | Sup.Fig.5E | CMS vs. RAV832/188 | 18 | | Sup.Fig.5F | PCSS1/2 vs. RAV832/188 | 18 | | Fig.6C | CMS vs. PCSS1/2 | 10 | | Fig.6D | CMS vs. RAV834/833 | 10 | | Fig.6E | PCSS1/2 vs. RAV834/833 | 10 |
Compare to CMS
CMS vs. RAV1575/834
RAV1575/834 are the most similar RAVs to PCSS1/2, respectively, based on Pearson correlation.
m2_name <- "RAV" m2_1 <- "RAV1575" m2_2 <- "RAV834" num_dat <- 18 x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5B") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_1575_834.rds"))
CMS vs. RAV834/833
RAV834/833 have the highest r-squared score when we compared the sample scores against the metadata.
18 CRC validation datasets
m2_name <- "RAV" m2_1 <- "RAV834" m2_2 <- "RAV833" num_dat <- 18 x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "3B") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_834_833.rds")) png("outputs/png/boxplot_CMS_vs_834_833.png", width = 600, height = 500) print(x)$visible dev.off()
10 CRC validation datasets
m2_name <- "RAV" m2_1 <- "RAV834" m2_2 <- "RAV833" num_dat <- 10 x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup6D") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_834_833.rds")) png("outputs/png/boxplot_CMS_vs_834_833.png", width = 600, height = 500) print(x)$visible dev.off()
CMS vs. RAV832/188
RAV832/188 are the most frequently validated RAVs for 18 CRC datasets.
m2_name <- "RAV" m2_1 <- "RAV832" m2_2 <- "RAV188" num_dat <- 18 x <- source("R/Fig4C_CMSvs.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5E") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_CMS_vs_832_188.rds")) png("outputs/png/boxplot_CMS_vs_832_188.png", width = 600, height = 500) print(x)$visible dev.off()
Compare to PCSS
PCSS vs. RAV1575/834
RAV1575/834 are the most similar RAVs to PCSS1/2, respectively, based on Pearson correlation.
m1_name <- "PCSS" m1_1 <- "PCSS1" m1_2 <- "PCSS2" m2_name <- "RAV" m2_1 <- "RAV1575" m2_2 <- "RAV834" num_dat <- 18 x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5C") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_1575_834.rds"))
PCSS vs. RAV834/833
RAV834/833 have the highest r-squared score when we compared the sample scores against the metadata.
18 CRC validation datasets
m1_name <- "PCSS" m1_1 <- "PCSS1" m1_2 <- "PCSS2" m2_name <- "RAV" m2_1 <- "RAV834" m2_2 <- "RAV833" num_dat <- 18 x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "3C") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_834_833.rds")) png("outputs/png/boxplot_PCSS_vs_834_833.png", width = 600, height = 500) print(x)$visible dev.off()
10 CRC validation datasets
m1_name <- "PCSS" m1_1 <- "PCSS1" m1_2 <- "PCSS2" m2_name <- "RAV" m2_1 <- "RAV834" m2_2 <- "RAV833" num_dat <- 10 x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup6E") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_834_833.rds"))
PCSS vs. RAV832/188
RAV832/188 are the most frequently validated RAVs for 18 CRC datasets.
m1_name <- "PCSS" m1_1 <- "PCSS1" m1_2 <- "PCSS2" m2_name <- "RAV" m2_1 <- "RAV832" m2_2 <- "RAV188" num_dat <- 18 x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
boxplot_summary <- statsSummary(x$value, "Sup5F") dirName <- paste0(num_dat, "_CRC_datasets") saveRDS(x, file.path("outputs", dirName, "boxplot_PCSS_vs_832_188.rds"))
PCSS vs. RAV834/3290
RAV3290 is associated with "stage" metadata of CRC datasets.
m1_name <- "PCSS" m1_1 <- "PCSS1" m1_2 <- "PCSS2" m2_name <- "RAV" m2_1 <- "RAV834" m2_2 <- "RAV3290" num_dat <- 10 x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
saveRDS(x, "outputs/boxplot_PCSS_vs_834_3290.rds")
PCSS vs. RAV834/596
RAV596 is associated with "grade" metadata of CRC datasets.
m1_name <- "PCSS" m1_1 <- "PCSS1" m1_2 <- "PCSS2" m2_name <- "RAV" m2_1 <- "RAV834" m2_2 <- "RAV596" num_dat <- 10 x <- source("R/Fig4C_contScores.R", print.eval = TRUE)
saveRDS(x, "outputs/boxplot_PCSS_vs_834_596.rds")
##### Change Supplementary Figures number boxplot_fig <- c("3B","3C","Sup8B","Sup8C","Sup8E","Sup8F","Sup9C","Sup9D","Sup9E") boxplot_summary$figure <- rep(boxplot_fig, each = 8) write.csv(boxplot_summary, "outputs/boxplot_summary.csv", row.names = FALSE)
Session Info
sessionInfo()
R Package Documentation
Browse R Packages
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