OrderedList(OrderedList)
OrderedList()所属R语言包：OrderedList
Detecting Similarities of Two Microarray Studies
两个芯片研究检测的异同
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function OrderedList aims for the comparison of comparisons: given two expression studies with one ranked (ordered) list of genes each, we might observe considerable overlap among the top-scoring genes. OrderedList quantifies this overlap by computing a weighted similarity score, where the top-ranking genes contribute more to the score than the genes further down the list. The final list of overlapping genes consists of those probes that contribute a certain percentage to the overall similarity score.
功能OrderedList旨在比较比较：两个表达研究的行列（有序）每个基因的列表，我们可能会发现得分最高的基因之间的相当大的重叠。 OrderedList量化计算加权相似性得分，其中排名靠前的基因作出更大的贡献比进一步下降列表得分基因重叠。重叠基因的最终名单，由一定比例的探针，有助于整体的相似性得分。
用法----------Usage----------
OrderedList(eset, B = 1000, test = "z", beta = 1, percent = 0.95,
verbose = TRUE, alpha=NULL, min.weight=1e-5, empirical=FALSE)
参数----------Arguments----------
参数：eset
Expression set containing the two studies of interest. Use prepareData to generate eset.
表达式设置包含这两项研究的兴趣。使用prepareData来产生eset。
参数：B
Number of internal sub-samples needed to optimize alpha.
内部需要优化阿尔法子样本数。
参数：test
String, one of 'fc' (log ratio = log fold change), 't' (t-test with equal variances) or 'z' (t-test with regularized variances). The z-statistic is implemented as described in Efron et al. (2001).
字符串，“FC”（log比率=log倍），T（t检验与方差相等）或“Z”（正规化方差t-检验）。 Z-统计数字是实施埃弗龙等。 （2001年）。
参数：beta
Either 1 or 0.5. In a comparison where the class labels of the studies match, we set beta=1. For example, in each single study the first class relates to bad prognosis while the second class relates to good prognosis. If a matching is not possible, we set beta=0.5. For example, we compare a study with good/bad prognosis classes to a study, in which the classes are two types of cancer tissues.
1或0.5。在研究类的标签相匹配的比较中，我们设置beta=1。例如，在每一个单一的研究一流的预后不良有关，而第二类涉及预后良好。如果一个匹配是不可能的，我们设置beta=0.5。例如，我们比较好/坏的预后类的一项研究的一项研究，其中类是两种类型的癌组织。
参数：percent
The final list of overlapping genes consists of those probes that contribute a certain percentage to the overall similarity score. Default is percent=0.95. To get the full list of genes, set percent=1.
重叠基因的最终名单，由一定比例的探针，有助于整体的相似性得分。默认percent=0.95。为了获得基因的完整列表，设置percent=1。
参数：verbose
Logical value for message printing.
消息打印的逻辑值。
参数：alpha
A vector of weighting parameters. If set to NULL (the default), parameters are computed such that top 100 to the top 2500 ranks receive weights above min.weight.
加权参数向量。如果设置为NULL（默认），参数计算等百强顶端2500的行列收到重量以上min.weight。
参数：min.weight
The minimal weight to be taken into account while computing scores.
要考虑的最小重量而计算分数。
参数：empirical
If TRUE, empirical confidence intervals will be computed by randomly permuting the class labels of each study. Otherwise, a hypergeometric distribution is used. Confidence intervals appear when using plot.OrderedList.
如果TRUE，经验置信区间将计算随机置换每一个研究类的标签。否则，使用超几何分布。置信区间出现时，使用plot.OrderedList。
Details
详情----------Details----------
In short, the similarity measure is computed as follows: Based on two-sample test statistics like the t-test, genes within each study are ranked from most up-regulated down to most down-regulated. Thus we have one ordered list per study. Now for each rank going both from top (up-regulated end) and from bottom (down-regulated end) we count the number of overlapping genes. The total overlap A_n for rank n is defined as:
总之，相似性度量的计算如下：像两样本t检验测试统计的基础上，在每一个研究基因的排名从最上调，大部分下调。因此，我们有一个下令每个研究名单。现在排名从顶部（上调月底），从底部的每个（下调端）我们计算的重叠基因的数量。总的重叠A_n级n被定义为：
where G_1 and G_2 are the two ordered list, f(G_1) and f(G_2) are the two flipped lists with the down-regulated genes on top and O_n is the size of the overlap of its two arguments. A preliminary version of the weighted overlap over all ranks n is then given as:
G_1和G_2是两个有序列表，f(G_1)和f(G_2)是两个翻转顶部和O_n下调基因名单它的两个参数重叠的大小。一个超过所有级别的加权重叠的初步版本n然后给出：
The final similarity score includes the case that we cannot match the classes in each study exactly and thus do not know whether up-regulation in one list corresponds to up- or down-regulation in the other list. Here parameter β comes into play:
最后的相似性得分的情况下，我们不能在每个研究完全匹配的类，因此不知道是否在一个列表中向上或其他列表下调。这里的参数β进场：
Parameter β is set by the user but parameter α has to be tuned in a simulation using sub-samples and permutations of the original class labels.
参数β是由用户设置，但必须在使用子样本的原始类的标签排列的模拟调谐参数α。
值----------Value----------
Returns an object of class OrderedList, which consists of a list with entries:
返回一个对象类OrderedList，其中包括一个条目列表：
参数：n
Total number of genes.
基因总数。
参数：label
The concatenated study labels as provided by eset.
级联研究标签规定eset。
参数：p
The p-value specifying the significance of the similarity.
p值指定的相似性的意义。
参数：intersect
Vector with sorted probe IDs of the overlapping genes, which contribute percent to the overall similarity score.
与重叠的基因，这有助于percent整体的相似性得分排序探针标识的向量。
参数：alpha
The optimal regularization parameter alpha.
最佳正规化参数α。
参数：direction
Numerical value. Returns '1' if the similarity score is higher for the originally ordered lists and '-1' if the score is higher for the comparison of one original to one flipped list. Of special interest if beta=0.5.
数值。返回1，如果相似的得分是更高原本有序列表和-1，如果比分是一个比较高的原一翻转列表。的特殊利益，如果beta=0.5。
参数：scores
Matrix of observed test scores with genes in rows and studies in columns.
矩阵中的行和列的研究观察到的基因测试的分数。
参数：sim.scores
List with four elements with output of the resampling with optimal alpha. SIM.observed: The observed similarity sore. SIM.alternative: Vector of observed similarity scores simulated using sub-sampling within the distinct classes of each study. SIM.random: Vector of random similarity scores simulated by randomly permuting the class labels of each study. subSample: TRUE to indicate that sub-sampling was used.
名单与最佳alpha重采样输出的四个要素。 SIM.observed：观察到的相似性疼痛。 SIM.alternative：观测到的相似度使用内各研究不同类别的子采样的模拟矢量。 SIM.random：随机置换每一个研究类的标签模拟随机相似度向量。 subSample：TRUE表明子采样。
参数：pauc
Vector with pAUC-scores for each candidate of the regularization parameter α. The maximal pAUC-score defines the optimal α. See also plot.OrderedList.
向量与每个候选人的正规化参数αpAUC分数。最大得分pAUC定义最佳α。还可以看plot.OrderedList。
参数：call
List with some of the input parameters.
一些输入参数列表。
参数：empirical
List with confidence interval values. Is NULL if empirical=FALSE.
置信区间值的列表。是NULL如果empirical=FALSE。
作者（S）----------Author(s)----------
Xinan Yang, Claudio Lottaz, Stefanie Scheid
参考文献----------References----------
参见----------See Also----------
prepareData, OL.data, OL.result, plot.OrderedList, print.OrderedList, compareLists
prepareData，OL.data，OL.result，plot.OrderedList，print.OrderedList，compareLists
举例----------Examples----------
### Let's compare the two example studies.[＃＃让我们来比较两个例子研究。]
### The first entries of 'out' both relate to bad prognosis.[＃＃出的第一个条目都涉及到预后不良。]
### Hence the class labels match between the two studies[＃因此类标签符合两国研究]
### and we can use 'OrderedList' with default 'beta=1'.[＃＃，我们可以使用默认的“β= 1OrderedList”。]
data(OL.data)
a <- prepareData(
list(data=OL.data$breast,name="breast",var="Risk",out=c("high","low"),paired=FALSE),
list(data=OL.data$prostate,name="prostate",var="outcome",out=c("Rec","NRec"),paired=FALSE),
mapping=OL.data$map
)
## Not run: [＃无法运行：]
OL.result <- OrderedList(a)
## End(Not run)[＃结束（不运行）]
### The same comparison was done beforehand.[＃事先做相同的比较。]
data(OL.result)
OL.result
plot(OL.result)
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 2012-2-26 10:15:52
