SPUTNIK (version 1.1)

CSRPeaksFilter: Performs the peak selection based on complete spatial randomness test.

Description

CSRPeaksFilter returns the significance for the null hypothesis that the spatial distribution of the peak intensities follow a random pattern. A significant p-value (q-values can be returned after applying multiple testing correction) allows to reject the hypothesis that the spatial distribution of a peak signal is random. The tests are performed using the functions available in the statspat R package.

Usage

CSRPeaksFilter(msiData, method = "ClarkEvans", covariateImage = NULL,
  covMethod = "sum", mzQueryCov = numeric(), mzTolerance = numeric(),
  useFullMZCov = TRUE, smoothCov = FALSE, smoothCovSigma = 2,
  invertCov = FALSE, adjMethod = "bonferroni", returnQvalues = TRUE,
  plotCovariate = FALSE, verbose = TRUE, ...)

Arguments

msiData

msi.dataset-class object. See msiDataset.

method

string (default = "ClarkEvans"). CSR statistical test applied to the peaks signal. Accepted values are:

• "ClarkEvans": performs a test based on the Clark and Evans aggregation R index. This test evaluates the compares of the nearest-neighbors distances to the case of purely random pattern.

• "KS": performs a test of goodness-of-fit between the signal pixels associated point process pattern and a spatial covariate using the Kolmogorov-Smirnov test. The covariate is defined by the reference image.

covariateImage

ms.image-class object. An image used as covariate (necessary for Kolmogorov-Smirnov test). If NULL, the covariate image is calculated using the method defined by `covMethod`.

covMethod

string (default = "sum"). Method used to calculate the reference image. Read only when method = "KS". Possible values are described in 'refAndROIimages'.

mzQueryCov

numeric. Values of m/z used to calculate the reference image. 2 values are interpreted as interval, multiple or single values are searched in the m/z vector. It should be left unset when using useFullMZCov = TRUE. Read only when method = "KS".

mzTolerance

numeric. Tolerance in PPM to match the mzQueryCov values in the m/z vector. It should be left unset when using useFullMZCov = TRUE.Read only when method = "KS".

useFullMZCov

logical (default = TRUE). Whether all the peaks should be used to calculate the covariate image. Read only when method = "KS".

smoothCov

logical (default = FALSE). Whether the covariate image should be smoothed using a Gaussian kernel. Read only when method = "KS".

smoothCovSigma

numeric (default = 2). Standard deviation of the smoothing Gaussian kernel. Read only when method = "KS".

invertCov

logical (default = FALSE). Whether the covariate image colors should be inverted.]

adjMethod

string (default = "bonferroni"). Multiple testing correction method. Possible values coincide with those of the stats::p.adjust function.

returnQvalues

logical (default = TRUE). Whether the computed q-values should be returned together with the p-values.

plotCovariate

logical (default = FALSE). Whether the covariate image should be visualized. Read only when method = "KS".

verbose

logical (defaul = TRUE). Additional output texts are generated.

...

additional parameters compatible with the statspat functions. See cdf.test for "KS" and clarkevans.test. for "ClarkEvans"

References

Baddeley, A., & Turner, R. (2005). Spatstat: an R package for analyzing spatial point patterns. Journal of statistical software, 12(6), 1-42.

Clark, P.J. and Evans, F.C. (1954) Distance to nearest neighbour as a measure of spatial relationships in populations. Ecology 35, 445<U+2013>453.

Berman, M. (1986) Testing for spatial association between a point process and another stochastic process. Applied Statistics 35, 54<U+2013>62.

Examples

# NOT RUN {
## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(200), 20, 40)

## Print original dimensions
print(dim(X))

## m/z vector
mzVector <- seq(600, 900, by = (900 - 600) / 39)

## Read the image size
imSize <- c(5, 4)

## Construct the ms.dataset object
msiX <- msiDataset(X, mzVector, imSize[1], imSize[2])

## Calculate the p-values using the Clark Evans test, then apply Benjamini-
## Hochberg correction.
csr <- CSRPeaksFilter(msiData = msiX, method = "ClarkEvans",
                      calculateCovariate = FALSE, adjMethod = "BH")

## Print selected peaks
print(csr$q.value)

## Create a new filter selecting corrected p-values < 0.001
selIdx <- which(csr$q.value < 0.001)
csrFilter <- createPeaksFilter(selIdx)
# }