exactTest(edgeR)
exactTest()所属R语言包：edgeR
Exact Tests for Differences between Two Groups of Negative-Binomial Counts
精确检验两组负二项式计数的差异
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute genewise exact tests for differences in the means between two groups of negative-binomially distributed counts.
2-6。精确的测试计算为两个计数分布负binomially的手段之间的差异。
用法----------Usage----------
exactTest(object, pair=NULL, dispersion="auto", rejection.region="doubletail", big.count=900)
exactTestDoubleTail(y1, y2, dispersion=0, big.count=900)
exactTestBySmallP(y1, y2, dispersion=0, big.count=900)
exactTestByDeviance(y1, y2, dispersion=0, big.count=900)
exactTestBetaApprox(y1, y2, dispersion=0)
参数----------Arguments----------
参数：object
an object of class DGEList.
对象类DGEList。
参数：pair
vector of length two, either numeric or character, providing the pair of groups to be compared; if a character vector, then should be the names of two groups (e.g. two levels of object$samples$group); if numeric, then groups to be compared are chosen by finding the levels of object$samples$group corresponding to those numeric values and using those levels as the groups to be compared; if NULL, then first two levels of object$samples$group (a factor) are used. Note that the first group listed in the pair is the baseline for the comparison—so if the pair is c("A","B") then the comparison is B - A, so genes with positive log-fold change are up-regulated in group B compared with group A (and vice versa for genes with negative log-fold change).
如果一个特征向量，然后应该是两个组的名称（如object$samples$group水平）;组数字，然后长度二，无论是数字或字符，提供双组相比向量;进行比较，选择找到object$samples$group对应这些数值的水平，和使用的水平进行比较的群体;如果NULL，那么前两个层次object$samples$group（一个因素）使用。请注意，第一组在对上市基准比较所以，如果对c("A","B")然后比较B - A，因此具有积极的log倍变化的基因组上调B相比，A组（基因负log倍的变化，反之亦然）。
参数：dispersion
either a numeric vector of dispersions or a character string indicating that dispersions should be taken from the data object. If a numeric vector, then can be either of length one or of length equal to the number of tags. Allowable character values are "common", "tagwise" or "auto". "common" uses object$common.dispersion, "tagwise" uses object$tagwise.dispersion and "auto" uses tagwise when available and otherwise common.
无论是分散的数字向量或一个字符串，表示从数据对象应采取分散。如果一个数值向量，然后可以是一个长度或长度等于标签的数量。允许的字符值是"common"，"tagwise"或"auto"。 "common"使用object$common.dispersion，"tagwise"使用object$tagwise.dispersion和"auto"tagwise使用时，和其他常见的。
参数：rejection.region
type of rejection region for two-sided exact test. Possible values are "doubletail", "smallp" or "deviance".
类型排斥双面精确测试的区域。可能值"doubletail"，"smallp"或"deviance"。
参数：big.count
count size above which asymptotic beta approximation will be used.
计算规模以上将用于渐近公测逼近。
参数：y1
numeric matrix of counts for the first the two experimental groups to be tested for differences. Rows correspond to genes or transcripts and columns to libraries. Libraries are assumed to be equal in size - e.g. adjusted pseudocounts from the output of equalizeLibSizes.
数字矩阵的第一差异测试的两个实验组的计数。行对应的基因转录和库列。图书馆被认为是平等的大小 - 例如调整pseudocounts从equalizeLibSizes输出。
参数：y2
numeric matrix of counts for the second of the two experimental groups to be tested for differences. Rows correspond to genes or transcripts and columns to libraries. Libraries are assumed to be equal in size - e.g. adjusted pseudocounts from the output of equalizeLibSizes. Must have the same number of rows as y1.
数字矩阵的差异测试的两个实验组中的第二个罪名。行对应的基因转录和库列。图书馆被认为是平等的大小 - 例如调整pseudocounts从equalizeLibSizes输出。必须有相同的行数为y1。
Details
详情----------Details----------
The functions test for differential expression between two groups of count libraries. They implement the exact test proposed by Robinson and Smyth (2008) for a difference in mean between two groups of negative binomial random variables. The functions accept two groups of count libraries, and a test is performed for each row of data. For each row, the test is conditional on the sum of counts for that row. The test can be viewed as a generalization of the well-known exact binomial test, implemented in the function binom.test in the stats package, but generalized to overdispersed counts.
差两组计数库之间的表达功能的测试。他们实施精确的测试，罗宾逊和史密斯（2008）提出了两组负二项分布随机变量之间的差异在平均。该函数接受两个组数库，每一行数据进行测试。对于每一行，测试条件是该行数的总和。测试可以被看作是一个众所周知的确切二项式测试的推广，在功能实现binom.test统计软件包，但广义overdispersed计数。
The low level functions exactTestDoubleTail, exactTestBetaApprox, exactTestBySmallP and exactTestByDeviance all assume that the libraries have been normalized to have the same size (expected column sum under the null hypothesis). The higher level function exactTest is intended to be called by users. This has a more object-orientated flavor and produces an object containing all the necessary components for downstream analysis. exactTest equalizes the library sizes using equalizeLibSizes before calling one of the low level functions.
低级别的功能exactTestDoubleTail，exactTestBetaApprox，exactTestBySmallP和exactTestByDeviance所有承担该库已归有相同的大小（零假设下预期的列的总和）。更高级别的功能exactTest拟由用户调用。这有一个更面向对象的味道，并产生一个对象，包含所有下游分析的必要组成部分。 exactTest均衡库的大小，使用equalizeLibSizes之前调用低级别的功能之一。
The functions exactTestDoubleTail, exactTestBySmallP and exactTestByDeviance correspond to different ways to define the two-sided rejection region when the two groups have different numbers of samples. exactTestBySmallP implements the method of small probabilities as proposed by Robinson and Smyth (2008). This method corresponds to binom.test when the dispersion is near zero, but gives poor results when the dispersion is very large. exactTestDoubleTail computes two-sided p-values by doubling the smaller tail probability. exactTestByDeviance uses the deviance goodness of fit statistics to define the rejection region, and is therefore equivalent to a conditional likelihood ratio test. This has good statistical properties but is relatively slow to compute. For general remarks on different types of rejection regions for exact tests see Gibbons and Pratt (1975).
职能exactTestDoubleTail，exactTestBySmallP和exactTestByDeviance对应不同的方式来定义双面拒绝区域时，两组有不同数量的样本。 exactTestBySmallP实现了小概率的方法，罗宾逊和史密斯（2008）提出的。此方法对应binom.test时的色散接近零，但给穷人的结果时，分散性是非常大的。 exactTestDoubleTail双面p值计算小尾概率增加一倍。 exactTestByDeviance使用合适的统计偏差善良的定义排斥区域，因此，相当于一个条件似然比检验。具有良好的统计性质，但计算相对缓慢。为精确测试排斥区域的不同类型的一般性发言，看到长臂猿和普拉特（1975）。
exactTestBetaApprox implements an asymptotical beta distribution approximation to the conditional count distribution.
exactTestBetaApprox实现渐近Beta分布近似的条件计数分布。
值----------Value----------
exactTestDoubleTail and friends produce a numeric vector of genewise p-values, one for each row of y1 and y2.
exactTestDoubleTail和朋友产生了2-6。p值，一个每个y1和y2行的数字向量。
exactTest produces an object of class DGEExact containing the following components:
exactTest产生一个对象类DGEExact包含以下组件：
参数：table
a data frame containing the elements logConc, the log-average concentration/abundance for each tag in the two groups being compared, logFC, the log-abundance ratio, i.e. fold change, for each tag in the two groups being compared, p.value, exact p-value for differential expression using the NB model
logConc，对数平均浓度/每个标签的丰度相比，两组数据框包含的元素，logFC，log的丰度比，即倍，每一个标签，在两组相比，p.value，确切的差异表达的P-值使用的NB模型
参数：comparison
a vector giving the names of the two groups being compared
一个向量给两组进行比较的名称
参数：genes
a data frame containing information about each transcript; taken from object and can be NULL
一个数据框包含每个成绩单的信息;从object能NULL
作者（S）----------Author(s)----------
Mark Robinson, Davis McCarthy, Gordon Smyth
参考文献----------References----------
Biostatistics, 9, 321-332.
The American Statistician 29, 20-25.
参见----------See Also----------
equalizeLibSizes, binomTest
equalizeLibSizes，binomTest
举例----------Examples----------
# generate raw counts from NB, create list object[来自NB生成的原始计数，创建列表对象]
y <- matrix(rnbinom(80,size=1/0.2,mu=10),nrow=20,ncol=4)
rownames(y) <- paste("Gene",1:nrow(y),sep=".")
group <- factor(c(1,1,2,2))
d <- DGEList(counts=y,group=group,lib.size=rep(1000,4))
# estimate dispersions and find differences in expression[估计分散和寻找差异表达]
d <- estimateCommonDisp(d)
d <- estimateTagwiseDisp(d)
de <- exactTest(d)
topTags(de)
# same example using low level exactTest function directly[同样的例子，直接使用低水平exactTest功能]
p.value <- exactTestDoubleTail(y[,1:2],y[,3:4],dispersion=0.2)
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 17:07:27
