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

In SPUTNIK: SPatially aUTomatic deNoising for Ims toolKit

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,
  adjMethod = "bonferroni",
  returnQvalues = TRUE,
  plotCovariate = FALSE,
  cores = 1,
  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 (required for Kolmogorov-Smirnov test).
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".
cores	integer (default = 1). Number of CPU cores. Parallel computation if greater than 1.
verbose	logical (default = TRUE). Additional output texts are generated.
...	additional parameters compatible with the statspat.core functions. See cdf.test for "KS" and clarkevans.test. for "ClarkEvans"

Author(s)

Paolo Inglese [e-mail]

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, 445–453.

Berman, M. (1986) Testing for spatial association between a point process and another stochastic process. Applied Statistics 35, 54–62.

Examples

## Load package
library("SPUTNIK")

## Mass spectrometry intensity matrix
X <- matrix(rnorm(16000), 400, 40)
X[X < 0] <- 0

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

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

## Read the image size
imSize <- c(20, 20)

## 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)

SPUTNIK documentation built on Nov. 24, 2022, 5:06 p.m.