R/MiFracData.R

In MiDA: Microarray Data Analysis

#' Subset an expression matrix based on probe's feature importance
#'
#' This function reduces the number of rows (probes) in gene/transcript expression matrix,
#' leaving only those that have the biggest feature importance for binary classification.
#'
#'@param Matrix numeric matrix of expression data where each row corresponds to a probe (gene, transcript),
#'  and each column correspondes to a specimen (patient). Probe IDs must be indicated as matrix row names.
#'@param importance.list a list of data frames, containing the result of binary classification:
#'probe IDs in first column and probe's feature importance (relative influence) in the second column
#'in the order from most important to the least important for classification.
#'Such list is the \code{\link{MiBiClassGBODT}} output (\code{Importance}).
#'@param NRows integer defines how many probes are to be left in the expression matrix.
#'
#'@details
#'Function provides gene expression matrix subsetting according to probe's feature importance for binary
#'classification, i.e., feature selection. Feature selection provides better classification and
#'identification of significant genes while "not important" genes are taken away from analysis.
#'The procedure of the pairwise combinations of the feature selection and classification methods are
#'described by Pirooznia et al (2008).
#'\cr
#'The function is able to use multiple feature importance data at a time to subset one expression matrix.
#'If \code{importance.list} contains more than one data frame (i.e., the result of a binary classification
#'for more than one model created during cross-validation), the function selects most important probes
#'from each data frame and then removes the repeats.
#'Thus, the output matrix may contain number of probes more than \code{NRows}.
#'
#'@return expression matrix with only selected probes in alphabetical order as rows
#'and all specimens as columns.
#'
#'@examples
#'# get gene expression and specimen data
#' data("IMexpression");data("IMspecimen")
#' #sample expression matrix and specimen data for binary classification,
#' #only "NORM" and "EBV" specimens are left
#' SampleMatrix<-MiDataSample(IMexpression, IMspecimen$diagnosis,"norm", "ebv")
#' dim(SampleMatrix) # 100 probes
#' SampleSpecimen<-MiSpecimenSample(IMspecimen$diagnosis, "norm", "ebv")
#' #Fitting, low tuning for faster running
#' ClassRes<-MiBiClassGBODT(SampleMatrix, SampleSpecimen, n.crossval = 3,
#'                          ntrees = 10, shrinkage = 1, intdepth = 2)
#' # List of influence data frames for all 3 models build using cross-validation
#' # is the 2nd element of BiClassGBODT results
#' # take 10 most important probes from each model
#' Sample2Matrix<-MiFracData(SampleMatrix, importance.list = ClassRes[[2]], 10)
#' dim(Sample2Matrix) # less than 100 probes left
#'
#'@references
#'Pirooznia M., Yang J.Y., Yang M.Q., Deng Y. (2008) A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics 9 (Suppl1), S13. \url{https://doi.org/10.1186/1471-2164-9-S1-S13}
#'
#'@seealso \code{\link{MiBiClassGBODT}}
#'
#'@author Elena N. Filatova
#'
#'@export



MiFracData <- function(Matrix, importance.list, NRows){
  params <- c()
  for (i in 1:length(importance.list)){params <- c(params, as.character(importance.list[[i]][1:NRows, 1]))} # make vector of important genes from all lists
  paramsId <- match(unique(params), rownames(Matrix)) # take away repeats
  fracdata <- Matrix[paramsId, ]
  fracdata <- fracdata[sort(rownames(fracdata), decreasing = F), ] # genes in alphabetical order
  return(fracdata)
}