iChip1(iChip)
iChip1()所属R语言包：iChip
Bayesian modeling of ChIP-chip data through hidden Ising models
芯片的芯片数据通过隐藏伊辛模型的贝叶斯建模
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function iChip1 implements the algorithm of modeling ChIP-chip data through a standard hidden Ising model.
功能iChip1实现建模芯片的芯片数据的算法，通过一个标准的隐藏伊辛模型。
用法----------Usage----------
iChip1(enrich,burnin=2000,sampling=10000,sdcut=2,beta0=3,
minbeta=0,maxbeta=10,normsd=0.1,verbose=FALSE)
参数----------Arguments----------
参数：enrich
A vector containing the probe enrichment measurements. The measurements must be sorted, firstly by chromosome and then by genomic position. The measurements could be log2 ratios of the intensities of IP-enriched and control samples for a single replicate, or summary statistics such as t-like statistics or mean differences for multiple replicates. We suggest to use the empirical Bayesian t-statistics implemented in the limma package for multiple replicates. Note, binding probes must have a larger mean value than non-binding probes.
一个向量，包含探针富集测量。必须按测量，一是由染色体和基因组的位置。测量可能的log2 IP的丰富和控制样品的强度比为一个单一的复制，或如类似T-统计或多个重复的平均差异的汇总统计。我们建议使用的经验贝叶斯limma包在多个复制实施的t-统计。注意，结合探针必须有一个更大的平均值比非结合的探针。
参数：burnin
The number of MCMC burn-in iterations.
烧在迭代的MCMC方法。
参数：sampling
The number of MCMC sampling iterations. The posterior probability of binding and non-binding state is calculated based on the samples generated in the sampling period.
的MCMC采样迭代。在抽样期间产生的样本的基础上，绑定和非绑定状态的后验概率计算。
参数：sdcut
A value used to set the initial state for each probe. The enrichment measurements of a enriched probe is typically several standard deviations higher than the global mean enrichment measurements.
A值用来设置每个探针的初始状态。浓缩了丰富的探针测量通常是几个标准差高于全球平均富集测量。
参数：beta0
The initial parameter used to control the strength of interaction between probes, which must be a positive value. A larger value of beta represents a stronger interaction between probes. The value for beta0 could not be too small (e.g. < 1.0). Otherwise, the Ising system may not be able to reach a super-paramagnetic state.
初始参数用来控制探针强度之间的互动，它必须是一个积极的价值。的β值越大，代表之间的强相互作用探针。 beta0值不能太小（如<1.0）。否则，伊辛系统未必能达到超顺磁性状态。
参数：minbeta
The minimum value of beta allowed.
的β允许的最低值。
参数：maxbeta
The maximum value of beta allowed.
的β允许的最大值。
参数：normsd
iChip1 uses a Metropolis random walk proposal for sampling from the posterior distributions of the model parameters. The proposal distribution is a normal distribution with mean 0 and standard deviation specified by normsd.
iChip1使用从模型参数的后验分布的抽样都市报随机游动的建议。建议分布是正态分布，均值为0，标准偏差normsd指定。
参数：verbose
A logical variable. If TRUE, the number of completed MCMC iterations is reported.
逻辑变量。如果是TRUE，据悉完成的MCMC迭代。
值----------Value----------
A list with the following elements.
以下内容的列表。
参数：pp
The posterior probabilities of probes in the binding/enriched state. There is a strong evidence to be a binding/enriched probe if the probe has a posterior probability close to1.
探针在绑定/丰富的状态的后验概率。有一个有力的证据是有约束力的/丰富的探针，如果探针具有后验概率接近TO1。
参数：beta
The posterior samples of the interaction parameter of the Ising model.
伊辛模型的相互作用参数后的样品。
参数：mu0
The posterior samples of the mean measurement of the probes in the non-binding/non-enriched state.
后平均在non-binding/non-enriched状态的探针测量样品。
参数：mu1
The posterior samples of the mean measurement of the probes in the binding/enriched state.
后样品的平均测量探针绑定/丰富的国家。
参数：lambda
The posterior samples of the precision of the enrichment measurements of the probes.
后样品的探针富集测量精度。
作者（S）----------Author(s)----------
Qianxing Mo <a href="mailto:moq@mskcc.org">moq@mskcc.org</a>
参考文献----------References----------
data analysis. Bioinformatics 26(6), 777-783. doi:10.1093/bioinformatics/btq032
参见----------See Also----------
iChip2,enrichreg, lmtstat
iChip2，enrichreg，lmtstat
举例----------Examples----------
# oct4 and p53 data are log2 transformed and quantile-normalized intensities[Oct4和p53的数据log2转化和归位数的强度]
# Analyze the Oct4 data (average resolution is about 280 bps)[分析Oct4的数据（平均分辨率为大约280个基点）]
data(oct4)
### sort oct4 data, first by chromosome then by genomic position[＃排序Oct4的数据，首先由染色体基因组的位置，然后]
oct4 = oct4[order(oct4[,1],oct4[,2]),]
# calculate the enrichment measurements --- the limma t-statistics[计算富集测量--- limma t-统计]
oct4lmt = lmtstat(oct4[,5:6],oct4[,3:4])
# Apply the standard Ising model to the ChIP-chip data[标准伊辛模型应用到芯片的芯片数据]
oct4res = iChip1(enrich=oct4lmt,burnin=1000,sampling=5000,sdcut=2,
beta0=3,minbeta=0,maxbeta=10,normsd=0.1)
# check the enriched regions detected by the Ising model using[检查丰富的区域伊辛模型检测]
# posterior probability (pp) cutoff at 0.9 or FDR cutoff at 0.01[后验概率（PP）截止0.9或FDR在0.01截止]
enrichreg(pos=oct4[,1:2],enrich=oct4lmt,pp=oct4res$pp,cutoff=0.9,
method="ppcut",maxgap=500)
enrichreg(pos=oct4[,1:2],enrich=oct4lmt,pp=oct4res$pp,cutoff=0.01,
method="fdrcut",maxgap=500)
# Analyze the p53 data (average resolution is about 35 bps)[分析p53的数据（平均分辨率是约35个基点）]
# uncommenting the following code for running[注释下面的代码运行]
# data(p53)[数据（P53）]
# must sort the data first[必须首先对数据进行排序]
# p53 = p53[order(p53[,1],p53[,2]),][P53 = P53 [订单（P53 [1]，P53 [2]）]]
# p53lmt = lmtstat(p53[,9:14],p53[,3:8])[p53lmt = lmtstat（P53 [9:14]，P53 [3:8]）]
# p53res = iChip1(p53lmt,burnin=1000,sampling=5000,sdcut=2,beta0=3,[p53res = iChip1（p53lmt，燃尽= 1000，采样= 5000 beta0，sdcut = 2，= 3，]
# minbeta=0,maxbeta=10,normsd=0.1)[minbeta maxbeta = 0，= 10，normsd = 0.1）]
# enrichreg(pos=p53[,1:2],enrich=p53lmt,pp=p53res$pp,cutoff=0.9,[enrichreg（POS = P53 [1:2]，丰富= p53lmt，PP = p53res美元PP，截止= 0.9，]
# method="ppcut",maxgap=500)[方法=的“ppcut，maxgap = 500）]
# enrichreg(pos=p53[,1:2],enrich=p53lmt,pp=p53res$pp,cutoff=0.01,[enrichreg（POS = P53 [1:2]，充实= p53lmt，PP = p53res美元PP，截止= 0.01，]
# method="fdrcut",maxgap=500)[方法=的“fdrcut，maxgap = 500）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-2-25 22:11:45
