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Last data update: 2014.03.03

R: Smooth expression values or scores

kernelize

R Documentation

Smooth expression values or scores

Description

'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.

Usage

getsteps(geneLocations, step.width)
kernelmatrix(steps, geneLocations, kernel, kernelparams)
kernelize(values, kernelweights)

Arguments

`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

Value

`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

Author(s)

MACAT Development team

Examples

  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,])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(macat)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: annotate
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: XML
Loading MicroArray Chromosome Analysis Tool...
Loading required packages...

Type 'demo(macatdemo)' for a quick tour...

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/macat/kernelize.Rd_%03d_medium.png", width=480, height=480)
> ### Name: kernelize
> ### Title: Smooth expression values or scores
> ### Aliases: kernelmatrix getsteps kernelize
> ### Keywords: manip
> 
> ### ** Examples
> 
>   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,])
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>