reformat long lines wherever possible

R/BRCA_GISTIC_MUT_SIG.R

@@ -1,12 +1,14 @@
 #' Genomic Data from TCGA BRCA MUT + GISTIC
 #'
-#' A SummarizedExperiment object consists of 16,873 genomic features across 951 samples.
+#' A SummarizedExperiment object consists of 16,873 genomic features 
+#' across 951 samples.
 #'
 #' @docType data
 #'
 #' @usage data(BRCA_GISTIC_MUT_SIG)
 #'
-#' @format An object of class \code{SummarizedExperiment} from \code{SummarizedExperiment} package
+#' @format An object of class \code{SummarizedExperiment} from 
+#' \code{SummarizedExperiment} package
 #' containing an assay of 16,873 rows (features) and 951 columns (samples)
 #' see \code{SummarizedExperiment} for more details.
 #'

R/CCLE_MUT_SCNA.R

@@ -1,12 +1,14 @@
 #' Genomic Data from CCLE MUT + SCNA
 #'
-#' A SummarizedExperiment object consists of 17,723 genomic features across 82 samples.
+#' A SummarizedExperiment object consists of 17,723 genomic features across 
+#' 82 samples.
 #'
 #' @docType data
 #'
 #' @usage data(CCLE_MUT_SCNA)
 #'
-#' @format An object of class \code{SummarizedExperiment} from \code{SummarizedExperiment} package
+#' @format An object of class \code{SummarizedExperiment} from 
+#' \code{SummarizedExperiment} package
 #' containing a matrix of 17,723 rows (features) and 82 columns (samples).
 #' See \code{SummarizedExperiment} for more details.
 #'

R/cadra.R

@@ -9,8 +9,8 @@
 #' @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.
+#' 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 
@@ -96,7 +96,8 @@
 CaDrA <- function(
     FS,
     input_score,
-    method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "custom"),
+    method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", 
+               "revealer", "custom"),
     custom_function = NULL,
     custom_parameters = NULL,
     alternative = c("less", "greater", "two.sided"),
@@ -191,7 +192,8 @@ CaDrA <- function(
               length(perm_best_scores),
               " permutated scores for the specified dataset",
               " and search parameters in cache path\n")
-      verbose("RE-COMPUTE PERMUTATION-BASED TESTINGS WITH LARGER NUMBER OF PERMUTATIONS\n")
+      verbose("RE-COMPUTE PERMUTATION-BASED TESTINGS ",
+              "WITH LARGER NUMBER OF PERMUTATIONS\n")
     }
 
     #######################################################################

R/cadra_functions.R

@@ -89,10 +89,11 @@ prefilter_data <- function(
 #' of omics features.
 #' @param input_score a vector of continuous scores of a molecular phenotype of
 #' interest such as protein expression, pathway activity, etc.
-#' NOTE: The \code{input_score} object must have names or labels that match the column
-#' names of FS object.
+#' NOTE: The \code{input_score} object must have names or labels that 
+#' match the column names of FS object.
 #' @param do_check a logical value indicates whether or not to validate if the  
-#' given parameters (FS and input_score) are valid inputs. Default is \code{TRUE}
+#' given parameters (FS and input_score) are valid inputs. 
+#' Default is \code{TRUE}
 #' 
 #' @noRd
 #' 
@@ -179,11 +180,12 @@ check_data_input <- function(
 #' @param top_N an integer specifies the number of features to start the
 #' search over (e.g. starting from the top 'N' features with the best scores). 
 #' Default is 1.
-#' If \code{top_N} is provided, then \code{search_start} parameter will be ignored. 
+#' If \code{top_N} is provided, then \code{search_start} parameter 
+#' will be ignored. 
 #' @param search_start a list of character strings (separated by commas)
 #' which specify a list of feature names to start the search with. 
-#' If \code{search_start} is provided, then \code{top_N} parameter will be ignored. 
-#' Default is \code{NULL}.
+#' If \code{search_start} is provided, then \code{top_N} parameter 
+#' will be ignored. Default is \code{NULL}.
 #' 
 #' @noRd
 #' 
@@ -213,10 +215,11 @@ check_data_input <- function(
 #'  feature_names = rownames(sim_FS)
 #' )
 #'
-#' @return If top_N is given, a vector of indices of top N features with the best
-#' scores will be returned and used to start the candidate_search() with. 
-#' Otherwise, the candidate_search() will start the search with a list of indices 
-#' of features defined in search_start.
+#' @return If top_N is given, a vector of indices of top N features with 
+#' the best scores will be returned and used to start the candidate_search() 
+#' with. 
+#' Otherwise, the candidate_search() will start the search with a list of  
+#' indices of features defined in search_start.
 check_top_N <- function(
     rowscore, 
     feature_names,
@@ -248,7 +251,8 @@ check_top_N <- function(
       which(feature_names == top_features[f])
     }) |> unlist()
 
-    verbose("Evaluating search over top ", length(search_feature_index), " features\n")
+    verbose("Evaluating search over top ", length(search_feature_index), 
+            " features\n")
 
   }else{
 
@@ -265,7 +269,8 @@ check_top_N <- function(
 
     if(length(search_start) == 0 || any(!search_start %in% feature_names))
       stop("The provided starting features: ", 
-           paste0(search_start[which(!search_start %in% feature_names)], collapse=", "),
+           paste0(search_start[which(!search_start %in% feature_names)], 
+                  collapse=", "),
            " does not exist among the row names of FS object.\n")
 
     # Get the index of the search_start strings
@@ -316,8 +321,8 @@ ks_test_double_wrap <- function(n_x, y, alt=c("less", "greater", "two.sided")) {
 #' Random permutation matrix generator
 #'
 #' Produces a random permutation score matrix given a vector of sample-specific
-#' scores representing a phenotypic readout of interest such as protein expression,
-#' pathway activity, etc.
+#' scores representing a phenotypic readout of interest such as protein 
+#' expression, pathway activity, etc.
 #' @param input_score a vector of continuous scores of a molecular phenotype of 
 #' interest such as protein expression, pathway activity, etc.
 #' NOTE: The \code{input_score} object must have names or labels to track 

R/calc_rowscore.R

@@ -8,8 +8,8 @@
 #' @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_mat} object.
+#' NOTE: \code{input_score} object must have names or labels that match the 
+#' column names of \code{FS_mat} 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 
@@ -33,8 +33,8 @@
 #' NOTE: This argument is applied to KS and Wilcoxon method
 #' @param weight 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}.
-#' @param seed_names a vector of one or more features representing known “causes”
-#' of activation or features associated with a response of interest.
+#' @param seed_names a vector of one or more features representing known 
+#' “causes” of activation or features associated with a response of interest.
 #' It is applied for \code{method = "revealer"} only.
 #' @param do_check a logical value indicates whether or not to validate if the  
 #' given parameters (\code{FS_mat} and \code{input_score}) are valid inputs. 
@@ -132,7 +132,8 @@
 calc_rowscore <- function(
     FS_mat,
     input_score,
-    method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "custom"),
+    method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", 
+               "revealer", "custom"),
     custom_function = NULL,
     custom_parameters = NULL,   
     alternative = c("less", "greater", "two.sided"),

R/candidate_search.R

@@ -3,20 +3,21 @@
 #'
 #' 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. 
+#' 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 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 
+#' @param FS 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.
+#' 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 
@@ -71,8 +72,9 @@
 #' @return If \code{best_score_only} is set to \code{TRUE}, the function will
 #' return a list of objects containing ONLY the best score of the union 
 #' meta-feature matrix for each top N searches. If \code{best_score_only} is set
-#' to \code{FALSE}, a list of objects containing the returned meta-feature matrix,
-#' as well as its corresponding best score and observed input scores are returned.
+#' to \code{FALSE}, a list of objects containing the returned meta-feature 
+#' matrix, as well as its corresponding best score and observed input scores 
+#' are returned.
 #' 
 #' @examples
 #'
@@ -95,7 +97,8 @@
 candidate_search <- function(
     FS,
     input_score,
-    method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", "revealer", "custom"),
+    method = c("ks_pval", "ks_score", "wilcox_pval", "wilcox_score", 
+               "revealer", "custom"),
     custom_function = NULL,
     custom_parameters = NULL,
     alternative = c("less", "greater", "two.sided"),
@@ -164,7 +167,8 @@ candidate_search <- function(
   ## 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))
+  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 can extend to do ",
          "for a given search (max_size must be >= 1)",
          "and max_size must be lesser than the number of features in FS\n")
@@ -260,7 +264,8 @@ candidate_search <- function(
 
         # Update best_meta based on feature set
         best_meta <- as.numeric(ifelse(
-          colSums(SummarizedExperiment::assay(FS)[global_best_s_features,]) == 0, 0, 1))
+          colSums(SummarizedExperiment::assay(FS)[global_best_s_features,]) == 
+            0, 0, 1))
 
       }
 
@@ -273,7 +278,8 @@ candidate_search <- function(
       # Here "*1" is used to convert the boolean back to integer 1's and 0's
       # Notice we remove anything in best_s_index from the original matrix
       # first to form the meta matrix.
-      meta_mat <- base::sweep(SummarizedExperiment::assay(FS)[-best_s_index,], 2, best_meta, `|`)*1
+      meta_mat <- base::sweep(SummarizedExperiment::assay(FS)[-best_s_index,], 
+                              2, best_meta, `|`)*1
 
       # Check if there are any features that are all 1s generated from
       # taking the union between the matrix
@@ -332,7 +338,8 @@ candidate_search <- function(
 
     verbose("\n\nFinished!\n\n")
     verbose("Number of iterations covered: ", i, "\n")
-    verbose("Best score attained over ", i , " iterations: ", global_best_s, "\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")
@@ -373,23 +380,25 @@ candidate_search <- function(
     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]
+    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
+  # 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 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 FS 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
@@ -405,8 +414,9 @@ candidate_search <- function(
 #' @param 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 weight 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 weight 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
@@ -498,7 +508,8 @@ forward_backward_check <- function
 
     # 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_best_score))
+    return(list(best_features=f_names[[f_best_index]], 
+                best_scores=f_best_score))
 
   }else{
 

R/custom_rowscore.R

@@ -133,18 +133,21 @@ custom_rowscore <- function
     custom_parameters[
       which(!names(custom_parameters) %in% names(req_args))],
     known_parameters[
-      which(!names(known_parameters) %in% c(names(req_args), names(custom_parameters)))]
+      which(!names(known_parameters) %in% c(names(req_args), 
+                                            names(custom_parameters)))]
   )
 
-  # Extract a list of custom_function() parameters that existed in combined variables
+  # Extract a list of custom_function() parameters that existed in 
+  # combined variables
   included_parameters <- combined_parameters[
     which(names(combined_parameters) %in% names(custom_args))] 
 
   # Check if some parameters not existed in a list of combined variables
   excluded_parameters <- custom_args[
     which(!names(custom_args) %in% names(combined_parameters))]
 
-  # If some parameters are excluded, check to see if that argument has a default value
+  # If some parameters are excluded, 
+  # check to see if that argument has a default value
   # Finally, return all necessarily arguments to be passed to custom_function()
   all_parameters <- c(
     included_parameters,