iChip2(iChip)
iChip2()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 iChip2 implements the method of modeling ChIP-chip data through a high-order hidden Ising model.
通过高阶隐藏伊辛模型功能iChip2实现的建模芯片的芯片数据的方法。
用法----------Usage----------
iChip2(Y,burnin=2000,sampling=10000,winsize=2,sdcut=2,beta=2.5,verbose=FALSE)
参数----------Arguments----------
参数：Y
A n by 2 matrix or data frame. The first column of Y contains the chromosome IDs; the second column of Y contains the probe enrichment measurements. Y must be sorted, firstly by chromosome and then by genomic position. The probe enrichment 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.
由2矩阵或数据框N。 Y的第一列包含染色体的ID，第二列的Y探针富集测量。 Y必须进行排序，一是由染色体和基因组的位置。探针富集测量可能的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：winsize
The parameter to control the order of interactions between probes. For example, winsize = 2, means that probe i interacts with probes i-2,i-1,i+1 and i+2. A balance between high sensitivity and low FDR could be achieved by setting winsize = 2.
参数来控制探针之间的相互作用的顺序。例如，winsize = 2，意味着我互动与探针I-2，I-1，i +1和I +2探针。设置winsize = 2，可以实现高灵敏度和低FDR之间的平衡。
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：beta
The 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. In general, high resolution array such as Affymetrix tiling arrays have relatively stronger probe interactions than low resolution array such as Agilent tiling arrays. For the second order Ising model (winsize = 2), the critical value of beta is around 1.0. For low resolution array data (e.g. 280 bp resolution), beta could be set to close to the critical value; For high resolution array data (e.g. 35 bp resolution), beta could be set to a value between 2 to 4. In general, choosing a large value of beta amounts to using a more stringent criterion for detecting enriched regions in ChIP-chip experiments.
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。在一般情况下，如Affymetrix公司贴砖阵列高分辨率阵列比分辨率低，如安捷伦平铺阵列阵列探针相对较强的相互作用。二阶伊辛模型（winsize = 2），β的临界值为1.0左右。对于低分辨率阵列数据（如280 bp的决议），β可以设置到接近临界值;对于高分辨率阵列数据（例如，35 bp的分辨率），β可以设置为2至4之间的值。在一般情况下，选择使用更严格的标准，检测芯片的芯片实验丰富的区域的测试数额较大的值。
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 to 1.
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，如果探针有一个后验概率接近1。
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：lambda0
The posterior samples of the precision of the enrichment measurements of the probes in the non-binding/non-enriched state.
后样品的富集探针在non-binding/non-enriched状态的测量精度。
参数：lambda1
The posterior samples of the precision of the enrichment measurements of the probes in the binding/enriched state.
后绑定/丰富的国家探针的富集测量精度的样品。
作者（S）----------Author(s)----------
Qianxing Mo <a href="mailto:moq@mskcc.org">moq@mskcc.org</a>
参考文献----------References----------
a high-order Ising model. Biometrics, 2010 Jan 29 [Epub ahead of print]. DOI: 10.1111/j.1541-0420.2009.01379.x
参见----------See Also----------
iChip1, enrichreg, lmtstat
iChip1，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])
# prepare the data used for the Ising model[准备用于伊辛模型的数据]
oct4Y = cbind(oct4[,1],oct4lmt)
# Apply the second-order Ising model to the ChIP-chip data[二阶伊辛模型应用到芯片的芯片数据]
oct4res=iChip2(Y=oct4Y,burnin=1000,sampling=5000,winsize=2,sdcut=2,beta=1.25)
# 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]）]
# p53Y = cbind(p53[,1],p53lmt)[p53Y = cbind（P53 [1]，p53lmt）]
# p53res=iChip2(Y=p53Y,burnin=1000,sampling=5000,winsize=2,sdcut=2,beta=2.5)[p53res = iChip2（：Y = p53Y，燃尽= 1000，采样= 5000，winsize = 2，sdcut = 2，β= 2.5）]
# 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：
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 2012-2-25 22:11:53
