plotAndCalculateWeakAndStrongGenotype: Visualizes and calculates strong and weak genotypes.

In SNPhood: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data

Description

The function plotAndCalculateWeakAndStrongGenotype finds the strongest and weakest genotypes based on reads extracted around each region. Strong and weak genotypes are found using the reads extracted from SNPhood and their corresponding genotypes as found by the function associateGenotypes Note the reads have to be merged using the function mergeReadGroups before running this function.

Usage

plotAndCalculateWeakAndStrongGenotype(SNPhood.o, normalize = TRUE,
  nClustersVec = 3, fileToPlot = NULL, verbose = FALSE)

Arguments

SNPhood.o	Object of class SNPhood
normalize	Logical(1). Default TRUE. Should a normalization be done on the counts/enrichments values before clustering? If set to TRUE, a normalization procedure based on subtracting the mean dividing by standard deviation for each region is performed. For more details, see the vignette.
nClustersVec	Numeric. Default 2. The number of clusters the data should be divided into. This can either be a vector or a single value. if multiple clusters are specified, multiple clustering analyses will be performed and for each of them, a plot is produced. make sure to specify the parameter fileToPlot in that case; otherwise, only the last plot may be visible.
fileToPlot	Character(1) or NULL. Default NULL. Filename of the PDF file for the output plots. If set to NULL, plots will be plotted to the currently active device.
verbose	Logical(1). Default FALSE. Should the verbose mode (i.e., diagnostic messages during execution of the script) be enabled?

Value

Modified SNPhood object with the results of the analysis stored in the object. Specifically, a matrix for average reads per SNP for datasets which have strong and weak genotypes, respectively, are stored in the slot additionalResults$genotype. The SNPs which have invariant genotypes across all the samples being analyzed are also saved. In addition, clustering on the strong and weak genotype read mateices are reportd as in the function plotAndClusterMatrix.

Examples

data(SNPhood.o, package="SNPhood")
SNPhood_merged.o = mergeReadGroups(SNPhood.o)
SNPhood_merged.o = plotAndCalculateWeakAndStrongGenotype(SNPhood_merged.o, nClustersVec = 6)
SNPhood_merged.o = plotAndCalculateWeakAndStrongGenotype(SNPhood_merged.o, nClustersVec = 2:6, verbose = FALSE)

SNPhood documentation built on Nov. 8, 2020, 6:22 p.m.