kernelize {macat} | R Documentation |
'kernelize' uses a kernel to smooth the data given in geneLocations by computing a weighted sum of the values vector. The weights for each position are given in the kernelweights matrix. A kernelweights matrix can be obtained by using the kernelmatrix function.
getsteps(geneLocations, step.width) kernelmatrix(steps, geneLocations, kernel, kernelparams) kernelize(values, kernelweights)
geneLocations |
a list of gene locations (length n) |
step.width |
the width of steps in basepairs |
steps |
a list of locations where the kernelization shall be computed |
kernel |
kernel function one of rbf, kNN or basePairDistance (or your own) |
kernelparams |
a list of named parameters for the kernel (default is fitted to the data) |
values |
vector of length n or matrix (m x n) of values that are to be smoothed |
kernelweights |
a matrix of (n x steps) where n is the length of the values vector and steps is the number of points where you wish to interpolate |
getsteps |
a list of locations starting at min(genLocations) going to max(geneLocations) with steps of size step.width |
kernelmatrix |
a matrix of (n x steps) containing the kernel weights for each location in steps |
kernelize |
a vector of length steps or a matrix (m x steps) containing the smoothed values |
MACAT Development team
data(stjd) genes = seq(100) geneLocations = abs(stjd$geneLocation[genes]) geneExpression = stjd$expr[genes,] step.width = 100000 steps = getsteps(geneLocations, step.width) weights = kernelmatrix(steps, geneLocations, rbf, list(gamma=1/10^13)) kernelized = kernelize(geneExpression, weights) plot(steps, kernelized[1,])