compute_sliding: Compute and plot smoothing of expression values or scores...

In macat: MicroArray Chromosome Analysis Tool

Description

'compute.sliding' computes a smoothing of the expression data or scores along the chromosome using the specified kernel function. This function is also used within the 'evalScoring' function. 'plotSliding' creates a plot of the smoothed expression values / scores.

Usage

compute.sliding(data, chromosome, sample, kernel, kernelparams=NULL, step.width = 1e+06)
plotSliding(data, chromosome, sample, kernel, kernelparams=NULL,
step.width=1000000, ...)

Arguments

data	A MACATData list holding the Expression values and gene locations
chromosome	the chromosome to be smoothed
sample	the sample (patient) whose expression values are smoothed
kernel	a kernel function (one of rbf, kNN, basePairDistance or your own)
kernelparams	a list of named parameters for the kernel (by default estimated from the data)
step.width	the smoothing is computed stepwise every step.width basepairs (default is 100000)
...	further graphical parameters passed on to plot.default

Value

for compute.sliding: a matrix of dimension (steps x 2) with in the first column the locations in basepairs where an interpolation is computed, and in the second column the smoothed values. plotSliding does not return anything and is merely called for its side-effect producing the plot.

Author(s)

MACAT development team

See Also

kernelize, evalScoring

Examples

data(stjd)
# just compute smoothed values:
smooth = compute.sliding(stjd, chromosome=3, sample=6, rbf,
                         kernelparams=list(gamma=1/10^13))
# compute and plot smoothed values:
plotSliding(stjd, chromosome=3, sample=6,rbf,
            kernelparams=list(gamma=1/10^13),pch=20,
            main="Chromosome 3")

macat documentation built on Nov. 8, 2020, 5:44 p.m.