scoring(macat)
scoring()所属R语言包：macat
Compute (regularized) t-scores for gene expression data
计算（正规化）T-分数的基因表达数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes for all genes in an expression matrix the (regularized) t-scores (statistics) with the given class labels and a number of permutations of these labels. Each gene is also assigned a p-value either empirically from the permutation scores or from a t-distribution.
此函数计算所有基因表达矩阵（正规化）T-分数给定类的标签，这些标签的排列数（统计）。每个基因也被分配了一个无论是经验，从置换的分数，或从t分布的p值。
用法----------Usage----------
scoring(data, labels, method = "SAM", pcompute = "tdist",
nperms = 1000, memory.limit = TRUE, verbose = TRUE)
参数----------Arguments----------
参数：data
Expression matrix with rows = genes and columns = samples
行=基因和列=样品的表达矩阵
参数：labels
Vector or factor of class labels; Scoring works only with two classes!
向量或因子类标签;评分只有两班！
参数：method
Either "SAM" to compute regularized t-scores, or "t.test" to compute Student's t-statistic
“萨姆”来计算正规化T-分数，或“t.test”的计算学生的t-统计
参数：pcompute
Method to compute p-values for each genes, either "empirical" to do permutations and compute p-values from them, or "tdist" to compute p-values based on respective t-distribution
计算每个基因的P-值，无论是“经验”做排列和他们的计算p值，或“TDIST”根据各自的t分布计算p值的方法
参数：nperms
Number of permutations of the labels to be investigated, if argument 'pcompute="empirical"'
标签的排列数进行调查，如果参数pcompute =“经验”
参数：memory.limit
Logical, if you have a really good computer (>2GB RAM), setting this FALSE will increase speed of computations
逻辑，如果你有一个很好的计算机（> 2GB内存），这种虚假的提高计算速度
参数：verbose
Logical, if progress should be reported to STDOUT
逻辑，如果进展到STDOUT，应当报
Details
详情----------Details----------
If 'pcompute="empirical"', the statistic is computed based on the given class labels, afterwards for 'nperms' permutations of the labels. The p-value for each gene is then the proportion of permutation statistics that are higher or equal than the statistic from the real labels. For each gene the 2.5%- and the 97.5%-quantile of the permutation statistics are also returned as lower and upper 'significance threshold'.
如果pcompute =“经验”，“统计计算，对给定类的标签，标签”nperms“排列之后。对每一个基因的p值，然后置换的统计数字，高于或等于真正的标签统计的比例。对于每一个基因的2.5％ - 97.5％置换统计位数也回到较低，意义上的阈值。
If 'pcompute="tdist", the statistic is computed only based on the given class labels, and the p-value is computed from the t-distribution with (Number of samples - 2) degrees of freedom.
统计如果“pcompute =”TDIST“，只计算基于给定的类的标签，p值（样本数 - 2）自由度t分布计算。
值----------Value----------
A list, with four components:
一个四部分组成名单：
参数：observed
(Regularized) t-scores for all genes based on the given labels
（正则）T-分数为所有的基因，根据给定的标签
参数：pvalues
P-values for all genes, either from permutations or t-distribution
所有基因的P值，无论是从排列或t分布
参数：expected.lower
2.5%-quantile of permutation test-statistics, supposed to be a lower 'significance border' for the gene; or NULL if p-values were computed from t-distribution
位数的排列应该是一个较低的意义边界的基因测试统计，或NULL如果从t分布计算p值2.5％
参数：expected.upper
97.5%-quantile of permutation test-statistics, supposed to be an upper 'significance border' for the gene; or NULL if p-values were computed from t-distribution
97.5％位数的排列测试，统计，应该是上的意义边界的基因;或NULL如果p值计算t分布
注意----------Note----------
In package macat, this function is only called internally
包中的macat，此功能仅在内部调用
作者（S）----------Author(s)----------
MACAT development team
参考文献----------References----------
<h3>See Also</h3>
举例----------Examples----------
data(stjd)
# compute gene-wise regularized t-statistics for[计算基因明智的正规化t-统计]
# T- vs. B-lymphocyte ALL:[T型与B淋巴单元ALL：]
isT <- as.numeric(stjd$labels=="T")
TvsB <- scoring(stjd$expr,isT,method="SAM",pcompute="none")
summary(TvsB$observed)
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 23:59:04
