In shbrief/GenomicSuperSignaturePaper: Reproduce GenomicSuperSignature paper

knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, 
                      message = FALSE, eval = FALSE, 
                      out.height="75%", out.width="75%")

Setup

suppressPackageStartupMessages({
    library(dplyr)
    library(GenomicSuperSignature)
})

data.dir <- system.file("extdata", package = "GenomicSuperSignaturePaper")
RAVmodel <- readRDS(file.path(data.dir, "RAVmodel_C2.rds"))
sizes <- sort(unique(metadata(RAVmodel)$size))

PCnumInCluster function

#' @param RAVmodel RAVmodel
#' @param x The cluster size (1 to 24) of the RAVmodel
#' @param plotting Under the default (\code{TRUE}), this function will output
#' the plot. If it is set to \code{FALSE}, the summary table of PC numbers in
#' a given cluster size (defined through \code{x} argument of this function)
#' will return.
#' 
#' @return The output is a bar plot showing the distribution of PCs for the
#' given cluster size.

library(ggplot2)

PCnumInCluster <- function(RAVmodel, x, plotting = TRUE) {
    ind <- which(metadata(RAVmodel)$size == x)
    cl <- which(metadata(RAVmodel)$cluster %in% ind)
    cl_pcs <- metadata(RAVmodel)$cluster[cl]

    PCs <- lapply(names(cl_pcs), function(x) {
        strsplit(x, "\\.PC") %>% unlist %>% .[2] %>% as.character
    }) %>% unlist

    data <- stack(table(PCs)) # summarize PC numbers
    blank <- data.frame(0, 1:20) # blank table for 20 PCs
    colnames(blank) <- c("values", "ind")
    blank$ind <- factor(blank$ind, levels = c(1:20)) # convert PC numbers as integer for rbind

    ol <- which(blank$ind %in% data$ind) # select PC numbers not in the clusters
    blank_merge <- blank[!blank$ind %in% ol,] # remove place holders
    data <- rbind(data, blank_merge) # add non-existing PC numbers

    ## Set PC number (`ind`) as a factor for plotting
    data$ind <- factor(data$ind, levels = c(1:20))
    data <- data[order(data$ind),]

    ## Return the table instead of plot
    if (!plotting) {return(data); stop()}

    ## ggplot
    fontAbove <- max(data$values)*0.04
    gp <- ggplot(data, aes(x = ind, y = values)) +
        geom_col() +
        geom_text(aes(label = values), vjust = -0.2, size = 2,
                  position = position_dodge(0.9)) +
        ylim(0,round(max(data$values)*1.1)) +
        labs(
            title = paste0("PC distibution in ", x,"-element RAVs"),
            x = "PC number",
            y = "Frequency"
        ) +
        theme(text = element_text(size = 8)) +
        theme_minimal(base_size = 8)

    return(gp)
}

Save the plot

pdf("PCs_In_Clusters.pdf")
library(ggpubr)

i = 0
ggarrange(PCnumInCluster(RAVmodel, sizes[i*4+1]),
          PCnumInCluster(RAVmodel, sizes[i*4+2]),
          PCnumInCluster(RAVmodel, sizes[i*4+3]),
          PCnumInCluster(RAVmodel, sizes[i*4+4]),
          ncol = 2, nrow = 2)

i = 1
ggarrange(PCnumInCluster(RAVmodel, sizes[i*4+1]),
          PCnumInCluster(RAVmodel, sizes[i*4+2]),
          PCnumInCluster(RAVmodel, sizes[i*4+3]),
          PCnumInCluster(RAVmodel, sizes[i*4+4]),
          ncol = 2, nrow = 2)

i = 2
ggarrange(PCnumInCluster(RAVmodel, sizes[i*4+1]),
          PCnumInCluster(RAVmodel, sizes[i*4+2]),
          PCnumInCluster(RAVmodel, sizes[i*4+3]),
          PCnumInCluster(RAVmodel, sizes[i*4+4]),
          ncol = 2, nrow = 2)

i = 3
ggarrange(PCnumInCluster(RAVmodel, sizes[i*4+1]),
          PCnumInCluster(RAVmodel, sizes[i*4+2]),
          PCnumInCluster(RAVmodel, sizes[i*4+3]),
          PCnumInCluster(RAVmodel, sizes[i*4+4]),
          ncol = 2, nrow = 2)

i = 4
ggarrange(PCnumInCluster(RAVmodel, sizes[i*4+1]),
          PCnumInCluster(RAVmodel, sizes[i*4+2]),
          PCnumInCluster(RAVmodel, sizes[i*4+3]),
          PCnumInCluster(RAVmodel, sizes[i*4+4]),
          ncol = 2, nrow = 2)

## There are 21 different cluster sizes and the largest one is 24-elements cluster
ggarrange(PCnumInCluster(RAVmodel, 24), ncol = 2, nrow = 2)
dev.off()

We summarized the proportion of lowerPCs (defined as PC11-20) for different sizes of clusters.

clSizes <- unique(metadata(RAVmodel)$size) %>% sort
res <- vector(mode = "list", length = length(clSizes))
tbl <- data.frame(clusterSize = clSizes, totalPCs = NA, lowerPCs = NA)

for (i in seq_along(clSizes)) {
    x <- PCnumInCluster(RAVmodel, clSizes[i], plotting = FALSE)
    tbl[i, "clusterSize"] <- clSizes[i]
    tbl[i, "totalPCs"] <- sum(x$values)
    tbl[i, "lowerPCs"] <- sum(x$values[11:20])
    res[[i]] <- x
}

tbl

write.csv(tbl, "lowerPCsInClusters.csv", row.names = FALSE)

shbrief/GenomicSuperSignaturePaper documentation built on Aug. 2, 2022, 2:04 p.m.