R/study_R2.R

In iasva: Iteratively Adjusted Surrogate Variable Analysis

#' A function to study different values of R2 
#' 
#' study_R2() studies how different R2 thresholds is changing:
#'  1) number of marker genes; 
#'  2) clustering quality (assuming number of clusters is known). 
#' It generated diagnostic plots that shows how selected genes and
#'  clustering quality changes as a function of R2 threshold.
#' @importFrom graphics Axis mtext par plot
#' @importFrom stats .lm.fit cutree dist hclust lm p.adjust resid
#' @importFrom cluster silhouette
#' @importFrom SummarizedExperiment SummarizedExperiment assay
#'
#' @param Y A SummarizedExperiment class containing read counts where
#' rows represent genes and columns represent samples.
#' @param iasva.sv  matrix of estimated surrogate variables,
#'  one column for each surrogate variable. 
#' @param selected.svs list of SVs that are selected for the
#'  analyses. Default is SV2
#' @param no.clusters No of clusters to be used in the analyses.
#'  Default is 2.
#' @param verbose If verbose = TRUE, the function outputs detailed messages.
#' @return a summary plot that represents silhoutte index and marker gene counts
#'  as a function of R2 and corresponding matrices. 
#'  
#' @examples
#' counts_file <- system.file("extdata", "iasva_counts_test.Rds",
#'  package = "iasva")
#' counts <- readRDS(counts_file)
#' anns_file <- system.file("extdata", "iasva_anns_test.Rds",
#'  package = "iasva")
#'  anns <- readRDS(anns_file)
#' Geo_Lib_Size <- colSums(log(counts + 1))
#' Patient_ID <- anns$Patient_ID
#' mod <- model.matrix(~Patient_ID + Geo_Lib_Size)
#' summ_exp <- SummarizedExperiment::SummarizedExperiment(assays = counts)
#' iasva.res<- iasva(summ_exp, mod[, -1],verbose = FALSE, 
#' permute = FALSE, num.sv = 5)
#' iasva.sv <- iasva.res$sv
#' study_res <- study_R2(summ_exp, iasva.sv)
#' 
#' @export

study_R2 <- function(Y, iasva.sv, selected.svs = 2,
                     no.clusters = 2, verbose = FALSE) {
  # error handling
  stopifnot(class(Y)[1] == "SummarizedExperiment", 
            is.numeric(selected.svs),
            is.matrix(iasva.sv), is.numeric(no.clusters))
  C.scores <- matrix(0, 0, 0)
  Number.of.genes <- matrix(0, 0, 0)
  for (i in seq(0.1, 0.9, 0.05)) {
    marker.counts <- find_markers(Y, as.matrix(iasva.sv[, selected.svs]),
                                  rsq.cutoff = i)
    no.genes <- dim(marker.counts)[1]
    if (no.genes == 0) {
      break
    } else {
      my.dist <- dist(t(log(marker.counts + 1)))
      my.clustering <- hclust(my.dist, method = "ward.D2")
      my.silhoutte <- silhouette(cutree(my.clustering, no.clusters), my.dist)
      C1 <- mean(my.silhoutte[my.silhoutte[, 1] == 1, 3])
      C2 <- mean(my.silhoutte[my.silhoutte[, 1] == 2, 3])
      average.C <- (C1 + C2) / 2
      C.scores <- c(C.scores, average.C)
      Number.of.genes <- c(Number.of.genes, no.genes)
    }
  }
  output.matrix <- rbind(C.scores, Number.of.genes)
  end.point <- (length(C.scores) - 1) * 0.05 + 0.1
  colnames(output.matrix) <- seq(0.1, end.point, 0.05)
  par(mar = c(5, 5, 5, 5))
  plot(Number.of.genes,  xlab = "R^2", ylab = "Number genes selected",
       xaxt = "n", main = "Number of selected genes vs. Cluster quality",
       pch = 18, col = "blue", type = "b", lty = 2, cex = 2)
  Axis(1, at = seq(1, length(Number.of.genes)), side = 1,
       labels = seq(0.1, end.point, 0.05), las = 2)
  par(new = TRUE)
  plot(C.scores, xlab = "", ylab = "", axes = FALSE, pch = 18, col = "red",
       type = "b", lty = 2, cex = 2)
  Axis(side = 4)
  mtext(side = 4, line = 2, "Average Silhouette Score", col = "red")
  par(new = FALSE)
  return(output.matrix)
}