R/Diagnostic.microarray.R

In madhulika-EBI/Batchevaluation: Toolkit for batch correction

#' Diagnostic plot and ranking of batch-correction methods
#' @description The function performs ranking and plotting of the evaluation data obtained at
#' the previous step 'batch_evaluation’. Here, methods are ranked on their individual performance,
#' and finally 'sumRank' is the final Rank of each method for the given dataset.
#' Rank 1 will be the best performer method.

#' @param  evaluation evaluation is the evaluation list obtained from the previous step `evaluation_matrix`.

#' @return A list of two data-frame- (a) raw - simple data-frame output of evaluation matrix and, (b) ranked-
#' Ranked data-frame of evaluation matrix which has additional column `sumRank` containing final Rank of each method.
#' Ranks are in descending performance order, i.e. method having score 1 will be the best method.
#'
#' @import dplyr
#' @import GGally
#' @importFrom hrbrthemes theme_ipsum
#' @importFrom colorspace divergingx_hcl
#' @name diagnostic_matrix.microarray

#' @export
diagnostic_matrix.microarray <- function(evaluation)

{
  minus <- function(x, na.rm = FALSE) (-x)


  evaluation <- evaluation

  w_m <- do.call(rbind, lapply(evaluation, as.matrix)) %>% t %>% as.data.frame()
  w3_m <- w_m %>% mutate_at(vars(-c(batch,pcRegression,silhouette)),minus)
  w3_m <- mutate_each(w3_m, list(dense_rank))

  w3_m <- w3_m %>% mutate(sumRank = rowSums(.)) %>% mutate(across(c("sumRank"), dense_rank))
  row.names(w3_m) <- rownames(w_m)
  w3_m$method <- row.names(w3_m)

  Rank <- list()
  Rank$raw <- w_m
  Rank$raw_rank <- w3_m
  Rank
  }