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Add more optimization targets

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This commit is contained in:
Elias Projahn 2021-10-21 11:42:44 +02:00
parent 064f75ddae
commit c0a1d965d7
2 changed files with 19 additions and 6 deletions
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20
R/ranking.R
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@ -34,11 +34,14 @@ ranking <- function(results, weights) {
#' @param results Results from [analyze()] or [ranking()]. #' @param results Results from [analyze()] or [ranking()].
#' @param methods Methods to include in the score. #' @param methods Methods to include in the score.
#' @param reference_gene_ids IDs of the reference genes. #' @param reference_gene_ids IDs of the reference genes.
#' @param target The optimization target. It may be one of "mean", "min" or
#' "max" and results in the respective rank being optimized.
#' #'
#' @returns Named list pairing method names with their optimal weights. #' @returns Named list pairing method names with their optimal weights.
#' #'
#' @export #' @export
optimize_weights <- function(results, methods, reference_gene_ids) { optimize_weights <- function(results, methods, reference_gene_ids,
target = "mean") {
# Create the named list from the factors vector. # Create the named list from the factors vector.
weights <- function(factors) { weights <- function(factors) {
result <- NULL result <- NULL
@ -50,13 +53,20 @@ optimize_weights <- function(results, methods, reference_gene_ids) {
result result
} }
# Compute the mean rank of the reference genes when applying the weights. # Compute the target rank of the reference genes when applying the weights.
mean_rank <- function(factors) { target_rank <- function(factors) {
data <- ranking(results, weights(factors)) data <- ranking(results, weights(factors))
data[gene %chin% reference_gene_ids, mean(rank)]
data[gene %chin% reference_gene_ids, if (target == "min") {
min(rank)
} else if (target == "max") {
max(rank)
} else {
mean(rank)
}]
} }
factors <- stats::optim(rep(1.0, length(methods)), mean_rank)$par factors <- stats::optim(rep(1.0, length(methods)), target_rank)$par
total_weight <- sum(factors) total_weight <- sum(factors)
weights(factors / total_weight) weights(factors / total_weight)

5
man/optimize_weights.Rd
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@ -4,7 +4,7 @@
\alias{optimize_weights} \alias{optimize_weights}
\title{Find the best weights to rank the results.} \title{Find the best weights to rank the results.}
\usage{ \usage{
optimize_weights(results, methods, reference_gene_ids) optimize_weights(results, methods, reference_gene_ids, target = "mean")
} }
\arguments{ \arguments{
\item{results}{Results from \code{\link[=analyze]{analyze()}} or \code{\link[=ranking]{ranking()}}.} \item{results}{Results from \code{\link[=analyze]{analyze()}} or \code{\link[=ranking]{ranking()}}.}
@ -12,6 +12,9 @@ optimize_weights(results, methods, reference_gene_ids)
\item{methods}{Methods to include in the score.} \item{methods}{Methods to include in the score.}
\item{reference_gene_ids}{IDs of the reference genes.} \item{reference_gene_ids}{IDs of the reference genes.}
\item{target}{The optimization target. It may be one of "mean", "min" or
"max" and results in the respective rank being optimized.}
} }
\value{ \value{
Named list pairing method names with their optimal weights. Named list pairing method names with their optimal weights.