hmm(VanillaICE)
hmm()所属R语言包：VanillaICE
Application of the Viterbi algorithm to copy number and/or
Viterbi算法的应用数量和/或复制
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A wrapper for fitting the HMM.
装修的HMM的包装。
用法----------Usage----------
hmm(object, hmm.params, use.baf=FALSE, ...)
参数----------Arguments----------
参数：object
one of the following classes derived from eSet: SnpSet, oligoSnpSet, CopyNumberSet, CNSet
一个从ESET派生的以下类：SnpSet，oligoSnpSet，CopyNumberSet，CNSet
参数：hmm.params
HmmOptionList. See details.
HmmOptionList。查看详情。
参数：use.baf
Logical. Whether to use the BAFs instead of the genotype calls and confidence scores to estimate the emission probabilities. See details.
逻辑。是否使用的企业，会计与财务概论，而不是基因型的检测和信心分数来估算排放的可能性。查看详情。
参数：...
The argument k to the function runmed can be passed for assessing the probability of an outlier. See details.
参数k功能runmed可以通过评估一个离群的概率。查看详情。
Details
详情----------Details----------
The probability that a point estimate is an outlier is assessed by subtracting the running median of the copy number estimates from the point estimates (referred to as 'delta'). For copy number changes that span multiple markers, we expect delta to be near zero and to have a small variance. For single point outliers, delta can be large in absolute value and have a large variance. We estimate the mixture probabilities (the probability that an observation is an outlier) using EM. We plug in the estimate of the outlier probability into the emission probabilities. Specifally, we model the emission probabilities as a Uniform-Gaussian mixture, where the Uniform component handles outliers.
点估计是离群的概率评估减去运行中位数的拷贝数估计的点估计（简称为“Delta”）。对于跨越多个标记的拷贝数变化，我们预计Delta是接近零，并有一个小的方差。单点离群，Delta大，在绝对值和有一个大的差异。我们估计混合概率（观察是一个离群的概率）使用EM。我们堵塞在离群进入发射概率概率的估计。 specifally，我们作为一个统一的混合高斯，统一的组件处理离群的发射概率模型。
The function used for the running median is runmed. One can pass an integer value of $k$ to runmed. Larger values of k should result in fewer segments with low coverage.
用于运行中位数的函数是runmed。人们可以通过元的整数值runmedK $。 k值越大应导致少，覆盖率低段。
For oligoSnpSet objects, the emission probability for the HMM is calculated as
oligoSnpSet对象的HMM发射概率计算
log emission = log GT + log CN
登录排放量=log的GT +log点数
where log GT is the emission probability for the genotype calls and log CN is the emission probability for the copy number estimates. When use.baf is TRUE, the emission probabilities are estimated from the B allele frequencies instead of the genotype calls / confidence scores. Generally, use of the use.baf argument requires a CNSet object obtained from preprocessing with the crlmm package. However, users may supply the B allele frequencies from external sources (e.g., BeadStudio software) when available.
logGT是排放的的基因型分型和记录点数的概率是拷贝数估计的发射概率。当use.baf为TRUE，发射的概率估计从B等位基因频率，而不是基因型分型/信心分数。一般来说，使用use.baf参数要求CNSetcrlmm包对象从预处理得到。然而，用户可能会从外部源（如，BeadStudio软件）时提供的B等位基因频率。
See the examples below for using the use.baf argument and how to visualize the predicted states along with the low-level genotype and copy number summaries.
看到下面的例子中使用use.baf参数和如何可视化预测的状态与低级别的基因型和拷贝数摘要。
值----------Value----------
An object of class RangedData.
对象类RangedData。
作者（S）----------Author(s)----------
R. Scharpf
参考文献----------References----------
chromosomal alterations using high-throughput SNP arrays, Annals of Applied Statistics
参见----------See Also----------
hmm.setup, runmed, calculateRBaf
hmm.setup，runmed，calculateRBaf
举例----------Examples----------
data(locusLevelData, package="oligoClasses")
oligoSet <- new("oligoSnpSet",
copyNumber=log2(locusLevelData[["copynumber"]]/100),
call=locusLevelData[["genotypes"]],
callProbability=locusLevelData[["crlmmConfidence"]],
annotation=locusLevelData[["platform"]])
oligoSet <- oligoSet[!is.na(chromosome(oligoSet)), ]
oligoSet <- oligoSet[order(chromosome(oligoSet), position(oligoSet)),]
oligoSet <- oligoSet[chromosome(oligoSet) < 3, ]
hmmOpts <- hmm.setup(oligoSet, is.log=TRUE)
fit <- hmm(oligoSet, hmmOpts, k=3)
xyplot(cn~x, oligoSet, range=fit[4, ], frame=2e6,pch=21, cex=0.5, panel=xypanel)
## Useful accessors for RangedData[＃有用的存取RangedData]
ranges(fit)
##Log likelihood ratio comparing likelihood of predicted state to the 'normal' state[＃登录似然比预测的状态的可能性比较“正常”状态]
## for each segment[＃为每段]
fit$LLR
## the number of SNPs / nonpolymorphic loci in each segment[＃在每段个SNPs / nonpolymorphic位点的数量]
coverage2(fit)
sampleNames(fit)
chromosome(fit)
##---------------------------------------------------------------------------[＃------------------------------------------------- --------------------------]
##[＃]
## Downstream of CRLMM for genotyping[＃下游CRLMM基因型的]
##[＃]
##---------------------------------------------------------------------------[＃------------------------------------------------- --------------------------]
## Not run: [＃无法运行：]
if(require("crlmm")){
data(cnSetExample, package="crlmm")
cnSetExample <- order(cnSetExample)
oligoSet <- as(cnSetExample, "oligoSnpSet")
assayDataElement(oligoSet, "baf") <- calculateRBaf(cnSetExample)[["baf"]]
## uses genotype emission probabilities[＃使用基因型发射概率]
hmmOpts <- HmmOptionList(oligoSet, is.log=TRUE, ICE=FALSE)
fit2 <- hmm(oligoSet, hmmOpts, use.baf=FALSE)
xyplot(cn ~ x | range, data=oligoSet, range=fit2[1:10, ], frame=2e6,
panel=xypanel, cex=0.3, pch=21, border="blue",
scales=list(x="free"),
col.hom="lightblue", col.het="salmon", col.np="grey60", fill.np="grey60")
xyplot(baf ~ x | range, data=oligoSet, range=fit2[1:10, ], frame=2e6,
panel=xypanel, cex=0.3, pch=21, border="blue",
scales=list(x="free"),
col.hom="lightblue", col.het="salmon", col.np="grey60", fill.np="grey60")
hmmOpts <- HmmOptionList(oligoSet, is.log=TRUE)
## use BAFs for emission probs.[＃排放probs使用的BAFS。]
fit3 <- hmm(oligoSet, hmmOpts, use.baf=TRUE)
## plot the copy number for the first 10 ranges[＃绘制第10范围内的拷贝数]
xyplot(cn ~ x | range, data=oligoSet, range=fit3[1:10, ], frame=2e6,
panel=xypanel, cex=0.2, pch=21, border="blue",
scales=list(x="free"),
col.hom="lightblue", col.het="salmon", col.np="grey60", fill.np="grey60")
## plot the BAFs for the first 10 ranges[＃绘制第10范围内的企业，会计与财务概论]
xyplot(baf ~ x | range, data=oligoSet, range=fit3[1:10, ], frame=2e6,
panel=xypanel, cex=0.2, pch=21, border="orange",
scales=list(x="free"),
col.hom="lightblue", col.het="salmon", col.np="grey60", fill.np="grey60")
} ## if(require("crlmm"))[＃如果（需要（“crlmm”））]
## End(Not run) ## \dontrun[＃结束（不运行）＃＃\ dontrun]
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 15:55:20
