kernelize(macat)
kernelize()所属R语言包:macat
Smooth expression values or scores
流畅的表达值或分数
译者:生物统计家园网 机器人LoveR
描述----------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.
“kernelize使用一个内核,平滑在geneLocations给的数据计算的加权总和值向量。每个位置的权重定在kernelweights矩阵。使用kernelmatrix的函数,可以得到一个kernelweights矩阵。
用法----------Usage----------
getsteps(geneLocations, step.width)
kernelmatrix(steps, geneLocations, kernel, kernelparams)
kernelize(values, kernelweights)
参数----------Arguments----------
参数:geneLocations
a list of gene locations (length n)
一个基因位置的列表(长度为n)
参数:step.width
the width of steps in basepairs
步骤中碱基对的宽度
参数:steps
a list of locations where the kernelization shall be computed
应计算的核心化的地点列表
参数:kernel
kernel function one of rbf, kNN or basePairDistance (or your own)
RBF核的功能之一,KNN或basePairDistance(或自己)
参数:kernelparams
a list of named parameters for the kernel (default is fitted to the data)
一个命名为内核的参数列表(默认安装到的数据)
参数:values
vector of length n or matrix (m x n) of values that are to be smoothed
值将被平滑矢量长度为n的矩阵(MXN)
参数: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
一个矩阵的(NX步骤),其中n是值向量和步骤的长度是多少分,你想插
值----------Value----------
参数:getsteps
a list of locations starting at min(genLocations) going to max(geneLocations) with steps of size step.width
地点,开始在分钟(genLocations)大小step.width的步骤要最大(geneLocations)
参数:kernelmatrix
a matrix of (n x steps) containing the kernel weights for each location in steps
(NX步骤)的矩阵中每个位置步骤的内核重
参数:kernelize
a vector of length steps or a matrix (m x steps) containing the smoothed values
一个长度的步骤或一个矩阵(MX步骤)向量平滑值
作者(S)----------Author(s)----------
MACAT Development team
参见----------See Also----------
compute.sliding, evalScoring
compute.sliding,evalScoring
举例----------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,])
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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