R/candidate_search.R
In montilab/CaDrA: Candidate Driver Analysis
Defines functions
candidate_search
Documented in
candidate_search
#'
#' Candidate Search
#'
#' Performs heuristic search on a set of binary features to determine whether
#' there are features whose union is more skewed (enriched at the extremes)
#' than either features alone. This is the main functionality of
#' the \code{CaDrA} package.
#'
#' NOTE: The legacy function \code{topn_eval} is equivalent to the recommended
#' \code{candidate_search} function
#' @param FS a matrix of binary features or a SummarizedExperiment class object
#' from SummarizedExperiment package where rows represent features of interest
#' (e.g. genes, transcripts, exons, etc...) and columns represent the samples.
#' The assay of FS contains binary (1/0) values indicating the presence/absence
#' of omics features.
#' @param input_score a vector of continuous scores representing a phenotypic
#' readout of interest such as protein expression, pathway activity, etc.
#'
#' NOTE: \code{input_score} object must have names or labels that match the
#' column names of \code{FS} object.
#' @param method a character string specifies a scoring method that is
#' used in the search. There are 6 options: (\code{"ks_pval"} or \code{ks_score}
#' or \code{"wilcox_pval"} or \code{wilcox_score} or
#' \code{"revealer"} (conditional mutual information from REVEALER) or
#' \code{"custom"} (a user-defined scoring method)).
#' Default is \code{ks_pval}.
#' @param method_alternative a character string specifies an alternative
#' hypothesis testing (\code{"two.sided"} or \code{"greater"} or \code{"less"}).
#' Default is \code{less} for left-skewed significance testing.
#'
#' NOTE: This argument only applies to \code{ks_pval} and
#' \code{wilcox_pval} method
#' @param custom_function if method is \code{"custom"}, specifies
#' a user-defined function here. Default is \code{NULL}.
#'
#' NOTE: \code{custom_function} must take FS and input_score as its
#' input arguments and its final result must return a vector of row-wise scores
#' where its labels or names match the row names of \code{FS} object.
#' @param custom_parameters if method is \code{"custom"}, specifies a list of
#' additional arguments (excluding \code{FS} and \code{input_score})
#' to be passed to the \code{custom_function}. For example:
#' custom_parameters = list(alternative = "less"). Default is \code{NULL}.
#' @param weights if method is \code{ks_score} or \code{ks_pval}, specifying a
#' vector of weights will perform a weighted-KS testing. Default is \code{NULL}.
#'
#' NOTE: \code{weights} must have names or labels that match the labels of
#' \code{input_score}.
#' @param search_start a vector of character strings (separated by commas)
#' specifies feature names in the \code{FS} object to start the search with.
#' If \code{search_start} is provided, then \code{top_N} parameter will be
#' ignored and vice versa. Default is \code{NULL}.
#' @param top_N an integer specifies the number of features to start the
#' search over. By default, it starts with the feature that has the highest
#' best score (top_N = 1).
#'
#' NOTE: If \code{top_N} is provided, then \code{search_start} parameter
#' will be ignored and vice versa. If top_N > 10, it may result in a longer
#' search time.
#' @param search_method a character string specifies an algorithm to filter
#' out the best features (\code{"forward"} or \code{"both"}). Default is
#' \code{both} (i.e. backward and forward).
#' @param max_size an integer specifies a maximum size that a meta-feature
#' can extend to do for a given search. Default is \code{7}.
#' @param best_score_only a logical value indicates whether or not to return
#' the best score corresponding to each top N searches only.
#' Default is \code{FALSE}.
#' @param do_plot a logical value indicates whether or not to plot the
#' overlapping features of the resulting meta-feature matrix.
#'
#' NOTE: plot can only be produced if the resulting meta-feature matrix contains
#' more than 1 feature (e.g. length(search_start) > 1 or top_N > 1).
#' Default is \code{FALSE}.
#' @param verbose a logical value indicates whether or not to print the
#' diagnostic messages. Default is \code{FALSE}.
#'
#' @return If \code{best_score_only = TRUE}, the heuristic search will return
#' the best feature whose its union meta-feature matrix has the highest score
#' among the \code{top_N} feature searches.
#' If \code{best_score_only = FALSE}, a list of objects pertaining to
#' \code{top_N} feature searches will be returned. For each top_N feature search,
#' the candidate search will contain 7 objects: (1) its best meta-feature matrix
#' (\code{feature_set}), (2) its observed input scores (\code{input_score}),
#' (3) its corresponding best score pertaining to the union meta-feature
#' matrix (\code{score}), (4) names of the best meta-features (\code{best_features}),
#' (5) rank of the best meta-features in term of their best scores (\code{best indices}),
#' (6) marginal scores of the best meta-features (\code{marginal_best_scores}),
#' (7) cumulative scores of the best meta-features (\code{cumulative_best_scores}).
#'
#' @examples
#'
#' # Load pre-computed feature set
#' data(sim_FS)
#'
#' # Load pre-computed input scores
#' data(sim_Scores)
#'
#' # Define additional parameters and run the function
#' candidate_search_result <- candidate_search(
#' FS = sim_FS, input_score = sim_Scores,
#' method = "ks_pval", method_alternative = "less", weights = NULL,
#' search_start = NULL, top_N = 3, search_method = "both",
#' max_size = 7, best_score_only = FALSE
#' )
#'
#' @export
#' @import SummarizedExperiment
candidate_search <- function(
FS,
input_score,
method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score",
"revealer", "custom"),
method_alternative = c("less", "greater", "two.sided"),
custom_function = NULL,
custom_parameters = NULL,
weights = NULL,
search_start = NULL,
top_N = 1,
search_method = c("both", "forward"),
max_size = 7,
best_score_only = FALSE,
do_plot = FALSE,
verbose = FALSE
){
# Set up verbose option
options(verbose = verbose)
# Match arguments
method <- match.arg(method)
method_alternative <- match.arg(method_alternative)
search_method <- match.arg(search_method)
# Select the appropriate method to compute scores based on
# skewness of a given binary matrix
# Return a vector of scores that already been ordered from
# most significant to least significant
# This comes in handy when doing the top-N evaluation of
# the top N 'best' features
rowscore <- calc_rowscore(
FS = FS,
input_score = input_score,
meta_feature = NULL,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
search_start = search_start,
top_N = top_N,
search_method = search_method,
max_size = max_size,
best_score_only = best_score_only,
do_plot = do_plot,
do_check = TRUE,
verbose = verbose
)
# Re-order rowscore in a decreasing order (from highest to lowest values)
# This comes in handy when doing the top-N evaluation of
# top N 'best' features
rowscore <- rowscore[order(rowscore, decreasing=TRUE)]
###### INITIALIZE VARIABLES ###########
#######################################
# Check if top_N is given and is numeric
top_N <- as.integer(top_N)
# Get the indices of features to start the search
search_feature_index <- check_top_N(
rowscore = rowscore,
feature_names = rownames(FS),
top_N = top_N,
search_start = search_start
)
## Check the search_method variable ####
back_search <- ifelse(search_method == "both", TRUE, FALSE)
## Check the max_size variable ####
max_size <- as.integer(max_size)
if(is.na(max_size) || length(max_size)==0 ||
max_size <= 0 || max_size > nrow(FS))
stop("Please specify a maximum size that a meta-feature matrix can extend ",
"to do for a given search (e.g., max_size must be >= 1) ",
"and max_size must be lesser than the number of features in FS.\n")
# Performs the search based on the given indices of the starting best features
topn_l <- lapply(seq_along(search_feature_index), function(x){
# x=1;
# Extract the index of the feature
feature_best_index <- search_feature_index[x]
# Get the name of the starting feature and its best score
start_feature <- rownames(FS)[feature_best_index]
best_feature <- start_feature
best_score <- rowscore[best_feature]
verbose("***Top Feature ", x, ": ", best_feature, "***\n")
verbose("Score: ", best_score, "\n")
# Update score and feature set since entry into the
# loop means there is an improvement (i > 0)
global_best_s <- best_score
# Initiate list of global features, indices, and scores
global_best_s_features <- c()
global_best_s_scores <- c()
# Counter variable for number of iterations
i <- 0
###### BEGIN ITERATIONS ###############
#######################################
verbose("Beginning candidate search...\n")
while((best_score > global_best_s | i == 0)
&& (length(global_best_s_features) < max_size)
&& (length(global_best_s_features) < nrow(FS))){
verbose("- Iteration number: ", (i+1), "\n")
verbose("Global best score: ", global_best_s, "\n")
verbose("Previous score: ", best_score, "\n")
# Update global score with current best score
global_best_s <- best_score
# Update meta-feature set, its indices, and best scores
global_best_s_features <- c(global_best_s_features, best_feature)
global_best_s_scores <- c(global_best_s_scores, best_score)
verbose("Current meta-feature set:\n ", global_best_s_features, "\n")
# Perform a backward check on the list of existing features and
# update global scores/feature lists accordingly
if(length(global_best_s_features) > 3 & back_search == TRUE){
backward_search_results <- forward_backward_check(
FS = FS,
input_score = input_score,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
search_start = search_start,
top_N = top_N,
search_method = search_method,
max_size = max_size,
best_score_only = best_score_only,
do_plot = do_plot,
do_check = FALSE, # MAKE SURE DO_CHECK IS SILENCE HERE
verbose = verbose,
glob_f = global_best_s_features,
glob_s = global_best_s,
glob_f_scores = global_best_s_scores
)
# Update global features, scores
global_best_s_features <- backward_search_results[["best_features"]]
global_best_s_scores <- backward_search_results[["best_scores"]]
global_best_s <- backward_search_results[["score"]]
verbose("Current meta-feature set after performing backward search:\n ",
global_best_s_features, "\n")
}
# Compute row-wise directional scores given known meta features
meta_rowscore <- calc_rowscore(
FS = FS,
input_score = input_score,
meta_feature = global_best_s_features,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
search_start = search_start,
top_N = top_N,
search_method = search_method,
max_size = max_size,
best_score_only = best_score_only,
do_plot = do_plot,
do_check = FALSE, # MAKE SURE DO_CHECK IS SILENCE HERE
verbose = FALSE # MAKE SURE VERBOSE IS SILENCE HERE
)
# Set up verbose option
options(verbose = verbose)
# Get index of highest best score
best_index <- which.max(meta_rowscore)
# Get the highest best score
best_score <- meta_rowscore[best_index]
# Get feature name of highest best score
best_feature <- names(best_score)
# If no improvement (exiting loop)
if(best_score > global_best_s){
verbose("Feature that produced best score in combination ",
"with current meta-feature set: ", best_feature, "\n")
verbose("Score: ", best_score, "\n")
}else{
verbose("No further improvement in score has been found.\n")
}
# Increment the next loop
i <- i+1
} ######### End of while loop
verbose("\n\nFinished!\n")
verbose("Number of iterations covered: ", i, "\n")
verbose("Best score attained over ", i ,
" iterations: ", global_best_s, "\n")
if(length(global_best_s_features) == 1)
verbose("No meta-feature that improves the enrichment was found.\n")
verbose("Features returned in FS: ", global_best_s_features, "\n")
# We don't just want the combination (meta-feature) at the end.
# We want all the features that make up the meta-feature
# This can be obtained using the list of indices that were progressively
# excluded (if at all) in the step-wise procedure
# If returning only those features that led to the best global score
FS_best <- FS[global_best_s_features, , drop=FALSE]
# Make a list containing two elements.
#The first will be the FS with the features that gave the best meta-feature
#The second will be the score corresponding to the meta-feature
# (named by the starting feature that led to that score)
# Assign the name of the best meta-feature score to be the
# starting feature that gave that score
names(global_best_s) <- start_feature
# Get indices of best features
global_best_s_indices <- which(names(rowscore) %in% global_best_s_features)
names(global_best_s_indices) <- global_best_s_features
# Get marginal scores of best features
marginal_best_scores <- rowscore[which(names(rowscore) %in% global_best_s_features)]
names(marginal_best_scores) <- global_best_s_features
return(
list("feature_set" = FS_best,
"input_score" = input_score,
"score" = global_best_s,
"best_features" = global_best_s_features,
"best_indices" = global_best_s_indices,
"marginal_best_scores" = marginal_best_scores,
"cumulative_best_scores" = global_best_s_scores
)
)
})
# length of search_feature must be greater 2 in order to generate topn_plot
if(do_plot) topn_plot(topn_list = topn_l)
# best_score_only
if(best_score_only == TRUE){
# Extract best score from each top N run
scores_l <- lapply(seq_along(topn_l), function(l){ topn_l[[l]][['score']] })
# Obtain the best scores with its associated feature names
best_meta_scores <- unlist(scores_l)
# Fetch the best score with the highest value
best_score <- best_meta_scores[order(best_meta_scores, decreasing = TRUE)][1]
return(best_score)
}
# By Default, the function returns the top N candidate search results as
# a list of lists
return(topn_l)
}
# Performance backward selection
#' @param FS a matrix of binary features or a SummarizedExperiment class object
#' from SummarizedExperiment package where rows represent features of interest
#' (e.g. genes, transcripts, exons, etc...) and columns represent the samples.
#' The assay of FS contains binary (1/0) values indicating the presence/absence
#' of ‘omics’ features.
#' @param input_score a vector of continuous scores representing a phenotypic
#' readout of interest such as protein expression, pathway activity, etc.
#' The \code{input_score} object must have names or labels that match the column
#' names of FS object.
#' @param method a character string specifies a scoring method that is
#' used in the search. There are 4 options: (\code{"ks"} or \code{"wilcox"} or
#' \code{"revealer"} (conditional mutual information from REVEALER) or
#' \code{"custom"} (a user customized scoring method)). Default is \code{ks}.
#' @param custom_function if method is \code{"custom"}, specifies
#' the customized function here. Default is \code{NULL}.
#' @param custom_parameters if method is \code{"custom"}, specifies a list of
#' arguments to be passed to the \code{custom_function}. Default is \code{NULL}.
#' @param method_alternative a character string specifies an alternative hypothesis
#' testing (\code{"two.sided"} or \code{"greater"} or \code{"less"}).
#' Default is \code{less} for left-skewed significance testing.
#' @param weights if method is \code{ks_pval} or \code{ks_score}, specifying
#' a vector of weights will perform a weighted-KS testing. Default is
#' \code{NULL}.
#' @param glob_f a vector containing the features (or row names) whose
#' union gives the best score (so far) in the search.
#' Feature names should match those of the provided FS object
#' @param glob_f_s a vector of scores corresponding to the union of the
#' specified vector of features
#' @param verbose a logical value indicates whether or not to print the
#' diagnostic messages. Default is \code{FALSE}.
#' @param ... additional parameters to be passed to the \code{custom_function}
#'
#' @noRd
#'
#' @return return the set of features with its current best score that is
#' better than the existing meta-feature best score
#'
#' @import SummarizedExperiment
forward_backward_check <- function
(
FS,
input_score,
method,
method_alternative,
custom_function,
custom_parameters,
weights,
glob_f,
glob_s,
glob_f_scores,
verbose = FALSE,
...
){
# Set up verbose option
options(verbose = verbose)
verbose("Performing backward search...\n")
verbose("Iterating over ", length(glob_f), " chosen features...\n")
# Retrieve binary feature matrix of only global best features so far
if(is(FS, "SummarizedExperiment")){
gmat <- SummarizedExperiment::assay(FS)[glob_f,]
}else{
gmat <- FS[glob_f,]
}
# Here, we make a list that should store the features and their
# corresponding meta-feature score for each leave-one-out run
f_names <- list()
f_indices <- list()
f_scores <- list()
scores <- c()
# We want to see if leaving anyone feature out improves the overall
# meta-feature score
# Here we only consider previous features in the meta-feature matrix
# (i.e. not the last one which was just added)
for(n in seq_len(length(glob_f)-1)){
#n=1;
# Store features to list
f_names[[n]] <- glob_f[-n]
f_scores[[n]] <- glob_f_scores[-n]
# Take the leave-one-out union of features will result in a single
# vector to compute the scores on
f_union <- ifelse(colSums(gmat[-n,]) == 0, 0, 1)
# Here we are getting the union of the meta-features
u_mat <- matrix(f_union, nrow=1, byrow=TRUE,
dimnames=list(c("UNION"), colnames(FS)))
# If the class of FS is originally a SummarizedExperiment object
# Convert the matrix to SummarizedExperiment
# This makes sure the original class of FS object does not change
if(is(FS, "SummarizedExperiment")){
u_FS <- SummarizedExperiment::SummarizedExperiment(assays=u_mat)
}else{
u_FS <- u_mat
}
# Compute row scores for this meta feature
u_rowscore <- calc_rowscore(
FS = u_FS,
input_score = input_score,
meta_feature = NULL,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
verbose = FALSE,
...
)
# Get the highest best score
scores <- c(scores, u_rowscore[which.max(u_rowscore)])
} # end for loop
# Set up verbose option
options(verbose = verbose)
# Obtain index of union meta-feature that gives the best score
f_best_index <- which.max(scores)
# Obtain best score of the union meta-feature
f_best_score <- scores[f_best_index]
# Here, we check if the new meta-feature has a computed best score better than
# the previous meta-feature's score
if(f_best_score > glob_s){
verbose("Found improvement on removing existing features...\n")
verbose("New feature set: ", f_names[[f_best_index]], "\n")
verbose("New global best score: ", f_best_score, "\n")
# Return a set of features that gave a better score than
# the existing best score and its new best score as well
return(list(best_features = f_names[[f_best_index]],
best_scores = f_scores[[f_best_index]],
score = f_best_score))
}else{
# Don't change anything. Return the existing
# best set of features and its corresponding score
return(list(best_features = glob_f,
best_scores = glob_f_scores,
score = glob_s))
}
}
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Defines functions candidate_search
Documented in candidate_search
#'
#' Candidate Search
#'
#' Performs heuristic search on a set of binary features to determine whether
#' there are features whose union is more skewed (enriched at the extremes)
#' than either features alone. This is the main functionality of
#' the \code{CaDrA} package.
#'
#' NOTE: The legacy function \code{topn_eval} is equivalent to the recommended
#' \code{candidate_search} function
#' @param FS a matrix of binary features or a SummarizedExperiment class object
#' from SummarizedExperiment package where rows represent features of interest
#' (e.g. genes, transcripts, exons, etc...) and columns represent the samples.
#' The assay of FS contains binary (1/0) values indicating the presence/absence
#' of omics features.
#' @param input_score a vector of continuous scores representing a phenotypic
#' readout of interest such as protein expression, pathway activity, etc.
#'
#' NOTE: \code{input_score} object must have names or labels that match the
#' column names of \code{FS} object.
#' @param method a character string specifies a scoring method that is
#' used in the search. There are 6 options: (\code{"ks_pval"} or \code{ks_score}
#' or \code{"wilcox_pval"} or \code{wilcox_score} or
#' \code{"revealer"} (conditional mutual information from REVEALER) or
#' \code{"custom"} (a user-defined scoring method)).
#' Default is \code{ks_pval}.
#' @param method_alternative a character string specifies an alternative
#' hypothesis testing (\code{"two.sided"} or \code{"greater"} or \code{"less"}).
#' Default is \code{less} for left-skewed significance testing.
#'
#' NOTE: This argument only applies to \code{ks_pval} and
#' \code{wilcox_pval} method
#' @param custom_function if method is \code{"custom"}, specifies
#' a user-defined function here. Default is \code{NULL}.
#'
#' NOTE: \code{custom_function} must take FS and input_score as its
#' input arguments and its final result must return a vector of row-wise scores
#' where its labels or names match the row names of \code{FS} object.
#' @param custom_parameters if method is \code{"custom"}, specifies a list of
#' additional arguments (excluding \code{FS} and \code{input_score})
#' to be passed to the \code{custom_function}. For example:
#' custom_parameters = list(alternative = "less"). Default is \code{NULL}.
#' @param weights if method is \code{ks_score} or \code{ks_pval}, specifying a
#' vector of weights will perform a weighted-KS testing. Default is \code{NULL}.
#'
#' NOTE: \code{weights} must have names or labels that match the labels of
#' \code{input_score}.
#' @param search_start a vector of character strings (separated by commas)
#' specifies feature names in the \code{FS} object to start the search with.
#' If \code{search_start} is provided, then \code{top_N} parameter will be
#' ignored and vice versa. Default is \code{NULL}.
#' @param top_N an integer specifies the number of features to start the
#' search over. By default, it starts with the feature that has the highest
#' best score (top_N = 1).
#'
#' NOTE: If \code{top_N} is provided, then \code{search_start} parameter
#' will be ignored and vice versa. If top_N > 10, it may result in a longer
#' search time.
#' @param search_method a character string specifies an algorithm to filter
#' out the best features (\code{"forward"} or \code{"both"}). Default is
#' \code{both} (i.e. backward and forward).
#' @param max_size an integer specifies a maximum size that a meta-feature
#' can extend to do for a given search. Default is \code{7}.
#' @param best_score_only a logical value indicates whether or not to return
#' the best score corresponding to each top N searches only.
#' Default is \code{FALSE}.
#' @param do_plot a logical value indicates whether or not to plot the
#' overlapping features of the resulting meta-feature matrix.
#'
#' NOTE: plot can only be produced if the resulting meta-feature matrix contains
#' more than 1 feature (e.g. length(search_start) > 1 or top_N > 1).
#' Default is \code{FALSE}.
#' @param verbose a logical value indicates whether or not to print the
#' diagnostic messages. Default is \code{FALSE}.
#'
#' @return If \code{best_score_only = TRUE}, the heuristic search will return
#' the best feature whose its union meta-feature matrix has the highest score
#' among the \code{top_N} feature searches.
#' If \code{best_score_only = FALSE}, a list of objects pertaining to
#' \code{top_N} feature searches will be returned. For each top_N feature search,
#' the candidate search will contain 7 objects: (1) its best meta-feature matrix
#' (\code{feature_set}), (2) its observed input scores (\code{input_score}),
#' (3) its corresponding best score pertaining to the union meta-feature
#' matrix (\code{score}), (4) names of the best meta-features (\code{best_features}),
#' (5) rank of the best meta-features in term of their best scores (\code{best indices}),
#' (6) marginal scores of the best meta-features (\code{marginal_best_scores}),
#' (7) cumulative scores of the best meta-features (\code{cumulative_best_scores}).
#'
#' @examples
#'
#' # Load pre-computed feature set
#' data(sim_FS)
#'
#' # Load pre-computed input scores
#' data(sim_Scores)
#'
#' # Define additional parameters and run the function
#' candidate_search_result <- candidate_search(
#' FS = sim_FS, input_score = sim_Scores,
#' method = "ks_pval", method_alternative = "less", weights = NULL,
#' search_start = NULL, top_N = 3, search_method = "both",
#' max_size = 7, best_score_only = FALSE
#' )
#'
#' @export
#' @import SummarizedExperiment
candidate_search <- function(
FS,
input_score,
method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score",
"revealer", "custom"),
method_alternative = c("less", "greater", "two.sided"),
custom_function = NULL,
custom_parameters = NULL,
weights = NULL,
search_start = NULL,
top_N = 1,
search_method = c("both", "forward"),
max_size = 7,
best_score_only = FALSE,
do_plot = FALSE,
verbose = FALSE
){
# Set up verbose option
options(verbose = verbose)
# Match arguments
method <- match.arg(method)
method_alternative <- match.arg(method_alternative)
search_method <- match.arg(search_method)
# Select the appropriate method to compute scores based on
# skewness of a given binary matrix
# Return a vector of scores that already been ordered from
# most significant to least significant
# This comes in handy when doing the top-N evaluation of
# the top N 'best' features
rowscore <- calc_rowscore(
FS = FS,
input_score = input_score,
meta_feature = NULL,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
search_start = search_start,
top_N = top_N,
search_method = search_method,
max_size = max_size,
best_score_only = best_score_only,
do_plot = do_plot,
do_check = TRUE,
verbose = verbose
)
# Re-order rowscore in a decreasing order (from highest to lowest values)
# This comes in handy when doing the top-N evaluation of
# top N 'best' features
rowscore <- rowscore[order(rowscore, decreasing=TRUE)]
###### INITIALIZE VARIABLES ###########
#######################################
# Check if top_N is given and is numeric
top_N <- as.integer(top_N)
# Get the indices of features to start the search
search_feature_index <- check_top_N(
rowscore = rowscore,
feature_names = rownames(FS),
top_N = top_N,
search_start = search_start
)
## Check the search_method variable ####
back_search <- ifelse(search_method == "both", TRUE, FALSE)
## Check the max_size variable ####
max_size <- as.integer(max_size)
if(is.na(max_size) || length(max_size)==0 ||
max_size <= 0 || max_size > nrow(FS))
stop("Please specify a maximum size that a meta-feature matrix can extend ",
"to do for a given search (e.g., max_size must be >= 1) ",
"and max_size must be lesser than the number of features in FS.\n")
# Performs the search based on the given indices of the starting best features
topn_l <- lapply(seq_along(search_feature_index), function(x){
# x=1;
# Extract the index of the feature
feature_best_index <- search_feature_index[x]
# Get the name of the starting feature and its best score
start_feature <- rownames(FS)[feature_best_index]
best_feature <- start_feature
best_score <- rowscore[best_feature]
verbose("***Top Feature ", x, ": ", best_feature, "***\n")
verbose("Score: ", best_score, "\n")
# Update score and feature set since entry into the
# loop means there is an improvement (i > 0)
global_best_s <- best_score
# Initiate list of global features, indices, and scores
global_best_s_features <- c()
global_best_s_scores <- c()
# Counter variable for number of iterations
i <- 0
###### BEGIN ITERATIONS ###############
#######################################
verbose("Beginning candidate search...\n")
while((best_score > global_best_s | i == 0)
&& (length(global_best_s_features) < max_size)
&& (length(global_best_s_features) < nrow(FS))){
verbose("- Iteration number: ", (i+1), "\n")
verbose("Global best score: ", global_best_s, "\n")
verbose("Previous score: ", best_score, "\n")
# Update global score with current best score
global_best_s <- best_score
# Update meta-feature set, its indices, and best scores
global_best_s_features <- c(global_best_s_features, best_feature)
global_best_s_scores <- c(global_best_s_scores, best_score)
verbose("Current meta-feature set:\n ", global_best_s_features, "\n")
# Perform a backward check on the list of existing features and
# update global scores/feature lists accordingly
if(length(global_best_s_features) > 3 & back_search == TRUE){
backward_search_results <- forward_backward_check(
FS = FS,
input_score = input_score,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
search_start = search_start,
top_N = top_N,
search_method = search_method,
max_size = max_size,
best_score_only = best_score_only,
do_plot = do_plot,
do_check = FALSE, # MAKE SURE DO_CHECK IS SILENCE HERE
verbose = verbose,
glob_f = global_best_s_features,
glob_s = global_best_s,
glob_f_scores = global_best_s_scores
)
# Update global features, scores
global_best_s_features <- backward_search_results[["best_features"]]
global_best_s_scores <- backward_search_results[["best_scores"]]
global_best_s <- backward_search_results[["score"]]
verbose("Current meta-feature set after performing backward search:\n ",
global_best_s_features, "\n")
}
# Compute row-wise directional scores given known meta features
meta_rowscore <- calc_rowscore(
FS = FS,
input_score = input_score,
meta_feature = global_best_s_features,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
search_start = search_start,
top_N = top_N,
search_method = search_method,
max_size = max_size,
best_score_only = best_score_only,
do_plot = do_plot,
do_check = FALSE, # MAKE SURE DO_CHECK IS SILENCE HERE
verbose = FALSE # MAKE SURE VERBOSE IS SILENCE HERE
)
# Set up verbose option
options(verbose = verbose)
# Get index of highest best score
best_index <- which.max(meta_rowscore)
# Get the highest best score
best_score <- meta_rowscore[best_index]
# Get feature name of highest best score
best_feature <- names(best_score)
# If no improvement (exiting loop)
if(best_score > global_best_s){
verbose("Feature that produced best score in combination ",
"with current meta-feature set: ", best_feature, "\n")
verbose("Score: ", best_score, "\n")
}else{
verbose("No further improvement in score has been found.\n")
}
# Increment the next loop
i <- i+1
} ######### End of while loop
verbose("\n\nFinished!\n")
verbose("Number of iterations covered: ", i, "\n")
verbose("Best score attained over ", i ,
" iterations: ", global_best_s, "\n")
if(length(global_best_s_features) == 1)
verbose("No meta-feature that improves the enrichment was found.\n")
verbose("Features returned in FS: ", global_best_s_features, "\n")
# We don't just want the combination (meta-feature) at the end.
# We want all the features that make up the meta-feature
# This can be obtained using the list of indices that were progressively
# excluded (if at all) in the step-wise procedure
# If returning only those features that led to the best global score
FS_best <- FS[global_best_s_features, , drop=FALSE]
# Make a list containing two elements.
#The first will be the FS with the features that gave the best meta-feature
#The second will be the score corresponding to the meta-feature
# (named by the starting feature that led to that score)
# Assign the name of the best meta-feature score to be the
# starting feature that gave that score
names(global_best_s) <- start_feature
# Get indices of best features
global_best_s_indices <- which(names(rowscore) %in% global_best_s_features)
names(global_best_s_indices) <- global_best_s_features
# Get marginal scores of best features
marginal_best_scores <- rowscore[which(names(rowscore) %in% global_best_s_features)]
names(marginal_best_scores) <- global_best_s_features
return(
list("feature_set" = FS_best,
"input_score" = input_score,
"score" = global_best_s,
"best_features" = global_best_s_features,
"best_indices" = global_best_s_indices,
"marginal_best_scores" = marginal_best_scores,
"cumulative_best_scores" = global_best_s_scores
)
)
})
# length of search_feature must be greater 2 in order to generate topn_plot
if(do_plot) topn_plot(topn_list = topn_l)
# best_score_only
if(best_score_only == TRUE){
# Extract best score from each top N run
scores_l <- lapply(seq_along(topn_l), function(l){ topn_l[[l]][['score']] })
# Obtain the best scores with its associated feature names
best_meta_scores <- unlist(scores_l)
# Fetch the best score with the highest value
best_score <- best_meta_scores[order(best_meta_scores, decreasing = TRUE)][1]
return(best_score)
}
# By Default, the function returns the top N candidate search results as
# a list of lists
return(topn_l)
}
# Performance backward selection
#' @param FS a matrix of binary features or a SummarizedExperiment class object
#' from SummarizedExperiment package where rows represent features of interest
#' (e.g. genes, transcripts, exons, etc...) and columns represent the samples.
#' The assay of FS contains binary (1/0) values indicating the presence/absence
#' of ‘omics’ features.
#' @param input_score a vector of continuous scores representing a phenotypic
#' readout of interest such as protein expression, pathway activity, etc.
#' The \code{input_score} object must have names or labels that match the column
#' names of FS object.
#' @param method a character string specifies a scoring method that is
#' used in the search. There are 4 options: (\code{"ks"} or \code{"wilcox"} or
#' \code{"revealer"} (conditional mutual information from REVEALER) or
#' \code{"custom"} (a user customized scoring method)). Default is \code{ks}.
#' @param custom_function if method is \code{"custom"}, specifies
#' the customized function here. Default is \code{NULL}.
#' @param custom_parameters if method is \code{"custom"}, specifies a list of
#' arguments to be passed to the \code{custom_function}. Default is \code{NULL}.
#' @param method_alternative a character string specifies an alternative hypothesis
#' testing (\code{"two.sided"} or \code{"greater"} or \code{"less"}).
#' Default is \code{less} for left-skewed significance testing.
#' @param weights if method is \code{ks_pval} or \code{ks_score}, specifying
#' a vector of weights will perform a weighted-KS testing. Default is
#' \code{NULL}.
#' @param glob_f a vector containing the features (or row names) whose
#' union gives the best score (so far) in the search.
#' Feature names should match those of the provided FS object
#' @param glob_f_s a vector of scores corresponding to the union of the
#' specified vector of features
#' @param verbose a logical value indicates whether or not to print the
#' diagnostic messages. Default is \code{FALSE}.
#' @param ... additional parameters to be passed to the \code{custom_function}
#'
#' @noRd
#'
#' @return return the set of features with its current best score that is
#' better than the existing meta-feature best score
#'
#' @import SummarizedExperiment
forward_backward_check <- function
(
FS,
input_score,
method,
method_alternative,
custom_function,
custom_parameters,
weights,
glob_f,
glob_s,
glob_f_scores,
verbose = FALSE,
...
){
# Set up verbose option
options(verbose = verbose)
verbose("Performing backward search...\n")
verbose("Iterating over ", length(glob_f), " chosen features...\n")
# Retrieve binary feature matrix of only global best features so far
if(is(FS, "SummarizedExperiment")){
gmat <- SummarizedExperiment::assay(FS)[glob_f,]
}else{
gmat <- FS[glob_f,]
}
# Here, we make a list that should store the features and their
# corresponding meta-feature score for each leave-one-out run
f_names <- list()
f_indices <- list()
f_scores <- list()
scores <- c()
# We want to see if leaving anyone feature out improves the overall
# meta-feature score
# Here we only consider previous features in the meta-feature matrix
# (i.e. not the last one which was just added)
for(n in seq_len(length(glob_f)-1)){
#n=1;
# Store features to list
f_names[[n]] <- glob_f[-n]
f_scores[[n]] <- glob_f_scores[-n]
# Take the leave-one-out union of features will result in a single
# vector to compute the scores on
f_union <- ifelse(colSums(gmat[-n,]) == 0, 0, 1)
# Here we are getting the union of the meta-features
u_mat <- matrix(f_union, nrow=1, byrow=TRUE,
dimnames=list(c("UNION"), colnames(FS)))
# If the class of FS is originally a SummarizedExperiment object
# Convert the matrix to SummarizedExperiment
# This makes sure the original class of FS object does not change
if(is(FS, "SummarizedExperiment")){
u_FS <- SummarizedExperiment::SummarizedExperiment(assays=u_mat)
}else{
u_FS <- u_mat
}
# Compute row scores for this meta feature
u_rowscore <- calc_rowscore(
FS = u_FS,
input_score = input_score,
meta_feature = NULL,
method = method,
method_alternative = method_alternative,
custom_function = custom_function,
custom_parameters = custom_parameters,
weights = weights,
verbose = FALSE,
...
)
# Get the highest best score
scores <- c(scores, u_rowscore[which.max(u_rowscore)])
} # end for loop
# Set up verbose option
options(verbose = verbose)
# Obtain index of union meta-feature that gives the best score
f_best_index <- which.max(scores)
# Obtain best score of the union meta-feature
f_best_score <- scores[f_best_index]
# Here, we check if the new meta-feature has a computed best score better than
# the previous meta-feature's score
if(f_best_score > glob_s){
verbose("Found improvement on removing existing features...\n")
verbose("New feature set: ", f_names[[f_best_index]], "\n")
verbose("New global best score: ", f_best_score, "\n")
# Return a set of features that gave a better score than
# the existing best score and its new best score as well
return(list(best_features = f_names[[f_best_index]],
best_scores = f_scores[[f_best_index]],
score = f_best_score))
}else{
# Don't change anything. Return the existing
# best set of features and its corresponding score
return(list(best_features = glob_f,
best_scores = glob_f_scores,
score = glob_s))
}
}
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