post.interaction(AffyExpress)
post.interaction()所属R语言包：AffyExpress
Create a list of tables for the result based on the main effect variable
创建一个结果表列出了基于的主要影响变量
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Once the interaction effect is being detected for a list of genes, that means that for these group of genes, the main effect is different across different level of another variable (call it effect modifier). This function will create a list of data frame for each level of the effect modifier.
一旦被检测的基因列表的互动效应，这意味着这些基因组跨越另一个变量的不同水平（调用它的影响修饰符），主要作用是不同的。此功能将创建一个数据框的效果修饰符的每个级别的列表。
用法----------Usage----------
post.interaction(strata.var, compare1, compare2, design.int, object, method, adj="none",
p.value=0.05, m.value=0)
参数----------Arguments----------
参数：strata.var
a categorical variable serves as a potential effect modifier
分类变量作为一个潜在的影响修饰符
参数：compare1
the first value of the main covariate. For example, suppose that the main covariate is drug, and there are three unique values: "drug1", "drug2", and "placebo". You would like to compare "drug1" to "drug2". Then you would use "drug1" as compare1
主要协变量的第一个值。例如，假设，主要协变量是药物，并有三个独特的价值观：“drug1”，“drug2”，“安慰剂”。您想比较“drug1”为“drug2”的。那么你可以使用作为比较1“drug1”
参数：compare2
the second value of the main covariate. Based on the example above, if you would like to compare "drug1" vs "drug2", then you would use "drug2" as compare2
主要协变量的第二个值。基于上面的例子，如果你想比较“drug1”与“drug2”的，那么你会使用捕捉“drug2”
参数：design.int
the design matrix of the interaction effect
设计矩阵的互动效应
参数：object
an "ExpressionSet"
“ExpressionSet”
参数：method
It is used to run regression within each level of the effect modifier. Three methods are supported by this function: "L" for using LIMMA method - compute moderated t-statistics and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value, "F" for using ordinary linear regression, "P" for permuation test by resampling the phenotype
它被用来运行在每个水平的效果修饰回归。三种方法都支持这个功能：“L”的使用LIMMA方法 - 使用普通的线性经验Bayes收缩朝着一个共同的价值，“F”的标准误差为计算放缓t-统计和差异表达的log赔率回归，重新取样型“P”为permuation测试
参数：adj
adjustment method for multiple comparison test, including "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". The default value is "none". Type help(p.adjust) for more detail.
多次对比试验，其中包括“冬青”，“hochberg”，“HOMMEL”，“邦弗朗尼”，“波黑”，“”，“FDR”，“无”的调整方法。默认值是“没有”。键入help（p.adjust）的更多细节。
参数：p.value
a p-value used to select significant gene within each level of the effect modifier
p值内使用每个效果修饰符水平选择显著基因
参数：m.value
a fold change value used to select significant gene within each level of the effect modifier
褶皱的变化值，用来在每个水平的效果修饰，选择显著基因
值----------Value----------
a list of data frame. The length of the data frame equals on number of levels of effect modifier.
一个数据框的列表。数据框的长度等于效果修饰水平的数量。
Each dataframe contains rows for all the genes from object and the following columns: ID (probeid); Log2Ratio (estimate of the effect or the contrast, on the log2 scale); F (F statistics); P.Value (raw p-value); adj.P.Value (adjusted p-value or q-value); significant (either TRUE or FALSE based on p.value and m.value)
每个dataframe包含所有的基因，从对象和下面的列行：ID（probeid）; Log2Ratio（估计效果或对比的log2规模，），F（F统计量）;的P.Value（原P-值）; adj.P.Value（p值或Q-值调整）;显著（基于p.value和m.value TRUE或FALSE）
作者（S）----------Author(s)----------
Xiwei Wu <a href="mailto:xwu@coh.org">xwu@coh.org</a>, Xuejun Arthur Li <a href="mailto:xueli@coh.org">xueli@coh.org</a>
举例----------Examples----------
data(testData)
normaldata<-pre.process("rma",testData)
## Create design matrix for interaction effect between "group"[＃创建为“本集团”之间的互动效应的设计矩阵]
## and "gender"[＃和“性别”]
design.int<-make.design(pData(normaldata), c("group", "gender"), int=c(1,2))
## Create the interaction contrast[＃创建互动的对比。]
contrast.int<-make.contrast(design.int, interaction=TRUE)
## Run Regression to detect interaction effect[＃运行回归检测相互作用的影响]
result.int<-regress(normaldata, design.int, contrast.int, "L")
## Select differentally expressed genes based on p.value[＃选择基于对p.value differentally基因表达]
select.int<-select.sig.gene(result.int, p.value=0.05)
## Identify genes with the interaction effect[＃确定基因与互动效应]
sig.ID<-select.int$ID[select.int$significant==TRUE]
sig.index<-match(sig.ID, rownames(exprs(normaldata)))
## Create separate tables for each level of effect modifier[＃创建单独的表中每一级效果修饰]
result<-post.interaction("group","M", "F", design.int, normaldata[sig.index,],
"L","none", 0.05, log2(1.5))
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-25 11:15:37
