cnEmission(VanillaICE)
cnEmission()所属R语言包：VanillaICE
Calculate emission probabilities for total copy number
发射概率计算总套数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculate emission probabilities for total copy number from
发射概率计算的总拷贝数
用法----------Usage----------
cnEmission(object, stdev, k = 5, cnStates, is.log, is.snp, normalIndex, verbose = TRUE, ...)
参数----------Arguments----------
参数：object
A CopyNumberSet, oligoSnpSet, or matrix.
一个CopyNumberSet，oligoSnpSet或matrix。
参数：stdev
A matrix. Ignored unless class of object is matrix. See details
矩阵。类的对象是被忽略，除非matrix。查看详情
参数：k
Integer. Size of window for running median. A running median of the total copy number is used to estimate the probability that a copy number estimate is an outlier.
整数。运行中位数窗口的大小。运行中位数的总拷贝数是用来估计拷贝数估计是离群的概率。
参数：cnStates
Numeric or integer. The theoretical or expected copy number for each hidden state.
数字或整数。每个隐藏状态的理论或预期的拷贝数。
参数：is.log
Logical. TRUE if the copy number estimates in object are on the log scale.
逻辑。 TRUE，如果拷贝数估计在object的log上规模。
参数：is.snp
Logical vector indicating which markers are polymorphic (TRUE) and nonpolymorphic (FALSE)
逻辑向量标记多态性（TRUE）和nonpolymorphic（假）
参数：normalIndex
Integer. The index of the 'normal' copy number state
整数。的“正常”的拷贝数状态指数
参数：verbose
Logical.
逻辑。
参数：...
Ignored
忽视
Details
详情----------Details----------
We calculate the emission probabilities of the total copy number (CN) estimates from a Normal-Uniform mixture. In particular, we assume the CN (suitably transformed) is emitted from a Normal distribution with mean given by cnStates. As outliers are common in high-throughput arrays, we allow for unusual values by adding a Uniform component to the mixture model that covers the support of the CN. (The support is determined by whether the CN is on the log scale as indicated by the is.log argument). To estimate the probability that CN is an outlier, we calculate CN - CNsmooth where CNsmooth is calculated from a running median with window given by argument k. We assume that the difference (CN-CNsmooth) is a mixture of two Normal distributions – copy number estimates that are not outliers should have a Normal distribution with mean zero and standard deviation 'sigma1', whereas outliers follow a Normal distribution with mean zero and standard deviation 'sigma2', sigma2 >> sigma1. We estimate the responsibilities for the mixture via EM, and use these values as a marker-specific estimate of the outlier probability. The emission probability is given by
我们计算的总拷贝数（CN）的发射概率的估计，从一个正常的均匀混合。特别是，我们假设从正态分布意味着cnStates给发出CN（适当改造）。作为离群常见的是在高通量阵列，我们允许不寻常的值加入一个统一的组件的混合模型，涵盖了CN的支持。 （支持决定是否CN是log规模is.log参数）。估计概率，CN是离群值，我们计算CN - CNsmooth其中CNsmooth是从一个窗口中运行由参数k位数计算。我们假设的差异（CN-CNsmooth）是两混合正态分布 - 拷贝数估计不离群应该有一个常态分布的平均值为零，标准偏差“sigma1”，而的离群遵循零均值正态分布和标准偏差，sigma2 >> sigma1 sigma2“。我们估计通过EM的混合责任，并使用这些标记特定的作为离群概率的估计值。发射概率
pihat * N(mean copy number state, sd) + (1-pihat) * Unif(MIN, MAX).
pihat * N个（平均拷贝数的状态，SD）+（1-pihat）* UNIF（最小值，最大值）。
值----------Value----------
Returns an array of the emission probabilities. The dimensions of the array are [feature index, sample index, state index].
返回一个数组的发射概率。数组的尺寸] [功能指数，样本指数，国家指数。
作者（S）----------Author(s)----------
R. Scharpf
参见----------See Also----------
cnEmission-methods
cnEmission-methods
See hmm method estimates emission probabilities and fits the Viterbi algorithm.
hmm方法估计发射概率和适合Viterbi算法。
See gtEmission for estimating the emission probabilities of diallic genotypes for each of the copy number states.
看到gtEmissiondiallic基因型为每个拷贝数状态发射概率的估计。
举例----------Examples----------
data(oligoSetExample, package="oligoClasses")
oligoSet <- order(oligoSet)
cn.emit <- cnEmission(oligoSet, k=5,
cnStates=log2(c(0.1, 1, 2, 3, 4)),
is.log=TRUE,
is.snp=isSnp(oligoSet),
normalIndex=3)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-26 15:55:01
