1.4.2

DESCRIPTION

@@ -1,21 +1,20 @@
 Package: SPUTNIK
 Type: Package
-Title: Spatially automatic denoising for Ims toolkit
+Title: Spatially Automatic Denoising for Imaging Mass Spectrometry Toolkit
 Version: 1.4.2
 Author: Paolo Inglese [aut, cre], Goncalo Correia [aut, ctb]
 Maintainer: Paolo Inglese <[email protected]>
-Authors@R: c(person("Paolo", "Inglese", email = "[email protected]", role = c("aut", "cre")),
-        person("Goncalo", "Correia", role = c("aut", "ctb")))
-Description: A set of tools for the peak filtering of mass spectrometry
-  imaging data (MSI or IMS) based on spatial distribution of signal. Given a 
-  region-of-interest (ROI), representing the spatial region where the informative
+Authors@R: c(person("Paolo", "Inglese", email = "[email protected]", 
+  role = c("aut", "cre")), person("Goncalo", "Correia", role = c("aut", "ctb")))
+Description: Set of tools for peak filtering of mass spectrometry
+  imaging data based on spatial distribution of signal. Given a 
+  region-of-interest, representing the spatial region where the informative
   signal is expected to be localized, a series of filters determine which peak
   signals are characterized by an implausible spatial distribution. The filters
-  reduce the dataset dimensionality and increase its information vs noise ratio,
+  reduce the dataset dimension and increase its information vs noise ratio,
   improving the quality of the unsupervised analysis results, reducing data
-  dimensionality and simplifying the chemical interpretation.
-  "SPUTNIK: an R package for filtering of spatially related peaks in mass 
-  spectrometry imaging data" <doi:10.1093/bioinformatics/bty622>
+  dimension and simplifying the chemical interpretation.
+  The methods are described in Inglese P. et al (2019) <doi:10.1093/bioinformatics/bty622>.
 Depends: R (>= 3.4.0)
 License: GPL (>= 3)
 Encoding: UTF-8

R/continuous_refimg.R

@@ -34,6 +34,8 @@
 #' has negative correlation with the selected image in \code{alignTo}.
 #' @param verbose logical (default = TRUE). Additional output text.
 #'
+#' @return A continuous valued reference image (see \link{msImage}).
+#'
 #' @details Function to extract the continuous reference image from a 
 #' \code{\link{msi.dataset-class}} object.
 #' The continuous reference image represents the spatial location of the sample.
@@ -46,7 +48,7 @@
 #'
 #' @example R/examples/graph_funcs.R
 #'
-#' @seealso msiDataset
+#' @seealso msiDataset, msImage
 #' @export
 #' 
 refImageContinuous <- function(msiData,

R/examples/msImage_plot.R

@@ -1,8 +1,11 @@
 ## Load package
-library("SPUTNIK")
 
-## Create ms.image-class object
-msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)
+\donttest{
+  library("SPUTNIK")
 
-## Plot the image
-## plot(msIm)
+  ## Create ms.image-class object
+  msIm <- msImage(values = matrix(rnorm(200), 40, 50), name = "test", scale = TRUE)
+
+  ## Plot the image
+  plot(msIm)
+}

R/filter_csr.R

@@ -34,6 +34,7 @@
 #' @param ... additional parameters compatible with the \code{statspat.core} functions.
 #' See \link[spatstat.explore]{cdf.test} for "KS" and \link[spatstat.explore]{clarkevans.test}.
 #' for "ClarkEvans"
+#' @return List of the p-values and adjusted p-values for the CSR test.
 #'
 #' @author Paolo Inglese \email{[email protected]}
 #'

R/filter_global.R

@@ -23,7 +23,7 @@
 #' @param cores integer (default = 1). Number of cores for parallel computing.
 #' @param verbose logical (default = \code{TRUE}). Additional output text.
 #'
-#' @return \code{peak.filter} object. See link{applyPeaksFilter}.
+#' @return \code{peak.filter} object. See \link{applyPeaksFilter}.
 #'
 #' @details A filter based on the similarity between the peak signals and a reference
 #' signal. The reference signal, passed as an \code{\link{ms.image-class}} object.

R/filter_split.R

@@ -56,6 +56,8 @@
 #'   \item the merged peaks image should be more structured than the single
 #'   peak images, accordingly to the selected \code{sparseness}.
 #' }
+#' 
+#' @return \code{peak.filter} object. See \link{applyPeaksFilter}.
 #'
 #' @author Paolo Inglese \email{[email protected]}
 #'

R/msi.dataset_methods.R

@@ -60,8 +60,10 @@ if (is.null(getGeneric('PCAImage'))) {
 #' @param object \link{msi.dataset-class} object.
 #' @param alignToSample boolean (default = TRUE). If TRUE, the principal component
 #' scores are aligned to the pixel mean intensity.
+#' @param seed set the random seed (default = \code{NULL}).
 #' 
 #' @return RGB raster representing the first 3 principal components
+#' (see \link{msImage}).
 #' 
 #' @importFrom grDevices rgb
 #' @import irlba
@@ -72,8 +74,8 @@ if (is.null(getGeneric('PCAImage'))) {
 setMethod(
   f = "PCAImage",
   signature = signature(object = "msi.dataset"),
-  definition = function(object, alignToSample = TRUE) {
-    set.seed(123)
+  definition = function(object, alignToSample = TRUE, seed = NULL) {
+    set.seed(seed)
     pca <- prcomp_irlba(object@matrix, center = TRUE, scale. = TRUE, n = 3,)
     if (alignToSample) {
       if (cor(apply(object@matrix, 1, mean), pca$x[, 1]) < 0) {
@@ -83,7 +85,7 @@ setMethod(
     colors <- apply(pca$x, 2, function(x) (x - min(x)) / (max(x) - min(x)))
     colors <- rgb(colors[, 1], colors[, 2], colors[, 3])
     colors <- matrix(colors, object@nrow, object@ncol)
-    return (msImage(values = colors, name = 'PCA', scale = FALSE))
+    return(msImage(values = colors, name = 'PCA', scale = FALSE))
   }
 )
 

R/sparseness_funcs.R

@@ -7,6 +7,8 @@
 #'
 #' @param im 2-D numeric matrix representing the image pixel intensities.
 #'
+#' @return calculated scatter ratio.
+#'
 #' @references Otsu, N. (1979). A threshold selection method from gray-level
 #' histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
 #'

inst/CITATION

@@ -7,5 +7,5 @@ bibentry(
   volume   = "36",
   number   = "1",
   pages    = "178-180",
-  doi      = "https://doi.org/10.1093/bioinformatics/bty622"
+  url      = "https://doi.org/10.1093/bioinformatics/bty622"
 )