fix: naming system

R/function-filtering.R → R/quality-assessment.R

@@ -1,12 +1,12 @@
-#' @title Basic filtering functions for metabolomics
+#' @title Basic quality assessment functions for metabolomics
 #'
 #' @description
 #'
-#' When dealing with metabolomics results, it is often necessary to filter
-#' features based on certain criteria. These criteria are typically derived
-#' from statistical formulas applied to full rows of data, where each row
-#' represents a feature. The following functions provide basic filtering
-#' methods commonly used in the analysis of metabolomics data.
+#' The following functions allow to calculate basic quality assessment estimates
+#' typically employed in the analysis of metabolomics data. These functions are
+#' designed to be applied to entire rows of data, where each row corresponds to
+#' a feature. Subsequently, these estimates can serve as a foundation for
+#' feature filtering.
 #'
 #' - `rsd` and `rowRsd` are convenience functions to calculate the relative
 #'  standard deviation (i.e. coefficient of variation) of a numerical vector
@@ -45,9 +45,9 @@
 #'  for non-gaussian distributed data.
 #'
 #' @note
-#' For `rsd` and `rowRsd` the feature abundances are expected to be provided
-#' in natural scale and not e.g. log2 scale as it may lead to incorrect
-#'  interpretations.
+#' For `rsd` and `rowRsd` the feature abundances are expected to be provided in
+#' natural scale and not e.g. log2 scale as it may lead to incorrect
+#' interpretations.
 #'
 #' @return  See individual function description above for details.
 #'
@@ -57,7 +57,7 @@
 #'
 #' @importFrom stats sd mad median
 #'
-#' @name filteringFunctions
+#' @name quality_assessment
 #'
 #' @references
 #'
@@ -97,7 +97,7 @@
 NULL
 
 #' @export
-#' @rdname filteringFunctions
+#' @rdname quality_assessment
 rsd <- function(x, na.rm = TRUE, mad = FALSE) {
     if (mad)
         mad(x, na.rm = na.rm) / abs(median(x, na.rm = na.rm))
@@ -106,12 +106,12 @@ rsd <- function(x, na.rm = TRUE, mad = FALSE) {
 }
 
 #' @export
-#' @rdname filteringFunctions
+#' @rdname quality_assessment
 rowRsd <- function(x, na.rm = TRUE, mad = FALSE)
     apply(x, MARGIN = 1, rsd, na.rm = na.rm, mad = mad)
 
 #' @export
-#' @rdname filteringFunctions
+#' @rdname quality_assessment
 rowDratio <- function(x, y, na.rm = TRUE, mad = FALSE){
     if (mad)
         vec <- apply(y, 1, mad, na.rm = na.rm) /
@@ -122,19 +122,20 @@ rowDratio <- function(x, y, na.rm = TRUE, mad = FALSE){
 }
 
 #' @export
-#' @rdname filteringFunctions
+#' @rdname quality_assessment
 percentMissing <- function(x){
    ((sum(is.na(x))) / length(x))*100
 }
 
 #' @export
-#' @rdname filteringFunctions
+#' @rdname quality_assessment
 rowPercentMissing <- function(x){
     apply(x, MARGIN = 1, percentMissing)
 }
 
 #' @export
-#' @rdname filteringFunctions
+#' @rdname quality_assessment
+
 rowBlank <- function(x, y, na.rm = TRUE){
     m_samples <- apply(x, 1, mean, na.rm = na.rm)
     m_blank <- apply(y, 1, mean, na.rm = na.rm)

vignettes/MetaboCoreUtils.Rmd

@@ -546,18 +546,19 @@ Generally, injecting study samples in random order can reduce (or even avoid)
 influence of any related technical bias in the downstream analysis and is highly
 suggested to improve and assure data quality.
 
-## Filtering data: Identifying measurement error 
+## Basic quality assessment and pre-filtering of metabolomics data
 
 When dealing with metabolomics results, it is often necessary to filter
 features based on certain criteria. These criteria are typically derived
 from statistical formulas applied to full rows of data, where each row
 represents a feature. In this tutorial, we'll explore a set of functions
-designed for filtering metabolomics data.
+designed designed to calculate basic quality assessment metrics on which
+metabolomics data can subsequently be filtered.
 
 First, to get more information on the available function you can check the documentation 
 
 ```{r}
-?filteringFunctions
+?quality_assessment
 ```
 
 We will use a matrix representing metabolomics measurements from different
@@ -581,8 +582,9 @@ cv_result <- rowRsd(metabolomics_data)
 print(cv_result)
 ```
 
-Next, we will compute the D-ratio, a measure of dispersion, by comparing the
-standard deviation of QC samples to that of biological test samples.
+Next, we will compute the D-ratio [@broadhurst_guidelines_2018], a measure of
+dispersion, by comparing the standard deviation of QC samples to that of
+biological test samples.
 
 ```{r}
 # Generate QC samples