#' featuresHit
#'
#' Show top hit features
#' @import ggplot2
#' @keywords features
#' @export
featuresHit <- function(..., snv_data=NULL, write=FALSE){
if(missing(snv_data)){
snv_data<-getData(...)
}
# To condense exon counts into "exon"
snv_data$feature<-as.factor(gsub("exon_.*", "exon", snv_data$feature))
# Reoders descending
snv_data$feature<-factor(snv_data$feature, levels = names(sort(table(snv_data$feature), decreasing = TRUE)))
snv_data <- snv_data %>%
dplyr::group_by(feature) %>%
dplyr::add_tally() %>%
ungroup() %>%
dplyr::filter(n >= 5) %>%
droplevels()
#cols<-setCols(snv_data, "feature")
p <- ggplot(snv_data)
p <- p + geom_bar(aes(feature, fill = feature))
#p<-p + cols
p <- p + cleanTheme() +
theme(axis.title.x=element_blank(),
panel.grid.major.y = element_line(color="grey80", size = 0.5, linetype = "dotted"))
p <- p + scale_x_discrete(expand = c(0.01, 0.01))
p <- p + scale_y_continuous(expand = c(0.01, 0.01))
# colour to a pub palette:
# p<-p + ggpar(p, palette = 'jco')
if(write){
features_outfile<-paste("hit_features_count.pdf")
cat("Writing file", features_outfile, "\n")
ggsave(paste("plots/", features_outfile, sep=""), width = 20, height = 10)
}
p
}
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