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Cross.validation.visualization.R
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Cross.validation.visualization.R
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library(ggplot2)
library(tidyverse)
## For each dataset
fig.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/02.figures/01.Classification/01.10x"
res <- readr::read_rds("/home/pauling/projects/04_SEmodel/07_NC_revision/01.res/01.featureSelection/01.10x.classification/02.5.cell.line.rds.gz")
res <- Reduce(rbind, res$auc)
cols <- c(
"Matisse" = "#1F77B4", "Flamenco" = "#FF7F0E",
"ForestGreen" = "#2CA02C", "Punch" = "#D62728",
"Wisteria" = "#9467BD", "SpicyMix" = "#8C564B",
"Orchid" = "#E377C2", "Gray" = "#7F7F7F",
"KeyLimePie" = "#BCBD22", "Java" = "#17BECF"
)
p <- res %>%
dplyr::mutate(method = ifelse(method == "sct","SCT",method)) %>%
dplyr::mutate(method = ifelse(method == "fano","Fano",method)) %>%
dplyr::mutate(method = ifelse(method == "raceid","RaceID",method)) %>%
ggplot(aes(factor(num), acc)) +
geom_boxplot(aes(colour = factor(method, levels = c("SE","SCT","Fano","Gini","RaceID","M3Drop","HVG"))), outlier.shape = NA) +
theme_bw() +
theme(
legend.position = 'top',
axis.title = element_text(size = 15),
axis.text = element_text(size = 15),
legend.title = element_text(size = 0),
legend.text = element_text(size = 13),
axis.text.y = element_text(color="black"),
axis.text.x = element_text(color="black")
) +
scale_colour_manual(values = unname(cols)[c(7,6,1,2,5,4,3)]) +
labs(
y = "Classification accuracy",
x = "Number of genes"
) +
guides(col = guide_legend(nrow = 1))
ggsave(plot = p, filename = "02.5.cell.line.pdf", path = fig.path, width = 7.71, height = 4.3)