From 767ea23c903f75e87aacc2de46fa827b55c652f7 Mon Sep 17 00:00:00 2001
From: paoloinglese
Date: Sun, 14 Apr 2024 18:18:15 +0100
Subject: [PATCH] 1.4.2
---
DESCRIPTION | 19 +++++++++----------
R/continuous_refimg.R | 4 +++-
R/examples/msImage_plot.R | 13 ++++++++-----
R/filter_csr.R | 1 +
R/filter_global.R | 2 +-
R/filter_split.R | 2 ++
R/msi.dataset_methods.R | 8 +++++---
R/sparseness_funcs.R | 2 ++
inst/CITATION | 2 +-
man/CSRPeaksFilter.Rd | 3 +++
man/PCAImage-msi.dataset-method.Rd | 5 ++++-
man/globalPeaksFilter.Rd | 2 +-
man/plot.Rd | 13 ++++++++-----
man/refImageContinuous.Rd | 5 ++++-
man/scatter.ratio.Rd | 3 +++
man/splitPeaksFilter.Rd | 3 +++
16 files changed, 58 insertions(+), 29 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index a791fac..edce8ca 100644
--- a/DESCRIPTION
+++ b/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
-Authors@R: c(person("Paolo", "Inglese", email = "p.inglese@outlook.com", 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 = "p.inglese@outlook.com",
+ 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"
+ dimension and simplifying the chemical interpretation.
+ The methods are described in Inglese P. et al (2019) .
Depends: R (>= 3.4.0)
License: GPL (>= 3)
Encoding: UTF-8
diff --git a/R/continuous_refimg.R b/R/continuous_refimg.R
index 53c7930..605b9c0 100644
--- a/R/continuous_refimg.R
+++ b/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,
diff --git a/R/examples/msImage_plot.R b/R/examples/msImage_plot.R
index a848dcc..7205a2e 100644
--- a/R/examples/msImage_plot.R
+++ b/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)
+}
diff --git a/R/filter_csr.R b/R/filter_csr.R
index d9910ad..0fedb67 100644
--- a/R/filter_csr.R
+++ b/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{p.inglese14@imperial.ac.uk}
#'
diff --git a/R/filter_global.R b/R/filter_global.R
index f21ef5b..76fa96b 100644
--- a/R/filter_global.R
+++ b/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.
diff --git a/R/filter_split.R b/R/filter_split.R
index aa509d9..934d5ef 100644
--- a/R/filter_split.R
+++ b/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{p.inglese14@imperial.ac.uk}
#'
diff --git a/R/msi.dataset_methods.R b/R/msi.dataset_methods.R
index 5994bc1..4c5e19b 100644
--- a/R/msi.dataset_methods.R
+++ b/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))
}
)
diff --git a/R/sparseness_funcs.R b/R/sparseness_funcs.R
index ddc60d9..fc8649d 100644
--- a/R/sparseness_funcs.R
+++ b/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.
#'
diff --git a/inst/CITATION b/inst/CITATION
index b860364..7f9a31a 100644
--- a/inst/CITATION
+++ b/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"
)
diff --git a/man/CSRPeaksFilter.Rd b/man/CSRPeaksFilter.Rd
index 5d49581..b537d52 100644
--- a/man/CSRPeaksFilter.Rd
+++ b/man/CSRPeaksFilter.Rd
@@ -52,6 +52,9 @@ generated.}
See \link[spatstat.explore]{cdf.test} for "KS" and \link[spatstat.explore]{clarkevans.test}.
for "ClarkEvans"}
}
+\value{
+List of the p-values and adjusted p-values for the CSR test.
+}
\description{
\code{CSRPeaksFilter} returns the significance for the null hypothesis that the
spatial distribution of the peak intensities follow a random pattern. A
diff --git a/man/PCAImage-msi.dataset-method.Rd b/man/PCAImage-msi.dataset-method.Rd
index bd79c04..fbe7fc0 100644
--- a/man/PCAImage-msi.dataset-method.Rd
+++ b/man/PCAImage-msi.dataset-method.Rd
@@ -7,16 +7,19 @@
image can be used to qualitatively evaluate the spatial heterogeneity of the
sample.}
\usage{
-\S4method{PCAImage}{msi.dataset}(object, alignToSample = TRUE)
+\S4method{PCAImage}{msi.dataset}(object, alignToSample = TRUE, seed = NULL)
}
\arguments{
\item{object}{\link{msi.dataset-class} object.}
\item{alignToSample}{boolean (default = TRUE). If TRUE, the principal component
scores are aligned to the pixel mean intensity.}
+
+\item{seed}{set the random seed (default = \code{NULL}).}
}
\value{
RGB raster representing the first 3 principal components
+(see \link{msImage}).
}
\description{
Generates an RGB msImage representing the first 3 principal components. This
diff --git a/man/globalPeaksFilter.Rd b/man/globalPeaksFilter.Rd
index 7d9e82f..36a9c10 100644
--- a/man/globalPeaksFilter.Rd
+++ b/man/globalPeaksFilter.Rd
@@ -40,7 +40,7 @@ scaled in [-1, 1] to match the same range of correlations.}
\item{verbose}{logical (default = \code{TRUE}). Additional output text.}
}
\value{
-\code{peak.filter} object. See link{applyPeaksFilter}.
+\code{peak.filter} object. See \link{applyPeaksFilter}.
}
\description{
\code{globalPeaksFilter} returns a list of peaks selected by their similarity
diff --git a/man/plot.Rd b/man/plot.Rd
index 63eb2bf..7838432 100644
--- a/man/plot.Rd
+++ b/man/plot.Rd
@@ -22,11 +22,14 @@ Visualize an MS image.
}
\examples{
## 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)
+}
}
diff --git a/man/refImageContinuous.Rd b/man/refImageContinuous.Rd
index 984f20f..bbba608 100644
--- a/man/refImageContinuous.Rd
+++ b/man/refImageContinuous.Rd
@@ -60,6 +60,9 @@ has negative correlation with the selected image in \code{alignTo}.}
\item{verbose}{logical (default = TRUE). Additional output text.}
}
+\value{
+A continuous valued reference image (see \link{msImage}).
+}
\description{
\code{refImageContinuous} returns the reference image, calculated using the
\code{method}.
@@ -110,5 +113,5 @@ roiImg <- refImageBinaryOtsu(refImg)
## plot(ref.roi$ROI)
}
\seealso{
-msiDataset
+msiDataset, msImage
}
diff --git a/man/scatter.ratio.Rd b/man/scatter.ratio.Rd
index 40485d8..5766a00 100644
--- a/man/scatter.ratio.Rd
+++ b/man/scatter.ratio.Rd
@@ -9,6 +9,9 @@ scatter.ratio(im)
\arguments{
\item{im}{2-D numeric matrix representing the image pixel intensities.}
}
+\value{
+calculated scatter ratio.
+}
\description{
\code{scatter.ratio} returns a measure of image scatteredness represented by
the ratio between the number of connected components and the total number of
diff --git a/man/splitPeaksFilter.Rd b/man/splitPeaksFilter.Rd
index 42f24f7..81c4333 100644
--- a/man/splitPeaksFilter.Rd
+++ b/man/splitPeaksFilter.Rd
@@ -36,6 +36,9 @@ in the results.}
\item{verbose}{logical (default = \code{TRUE}). Additional output text.}
}
+\value{
+\code{peak.filter} object. See \link{applyPeaksFilter}.
+}
\description{
\link{splitPeaksFilter} returns a list of estimated split peak indices. Each
element of the list contains an array of the original peak indices that can