evalScoring(macat)
evalScoring()所属R语言包：macat
Score differential expression, assess significance,
分数差异表达，评估的意义，
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes for all genes on one chromosome the regularized t-statistic to score differential gene expression for two given groups of samples. Additionally these scores are computed for a number of permutations to assess significance. Afterwards these scores are smoothed with a given kernel along the chromosome to give scores for chromosomal regions.
此函数计算所有基因在一条染色体上的正规化t-统计得分为两个组样品的基因差异表达。此外，这些成绩的评估意义的排列数计算。随后，这些成绩是平滑沿染色体的内核给分数的染色体区域。
用法----------Usage----------
evalScoring(data, class, chromosome, nperms=1000, permute="labels",
pcompute="empirical", subset=NULL,
newlabels=NULL,kernel=rbf,kernelparams=NULL,cross.validate=TRUE,
paramMultipliers=2^(-4:4),ncross=10,step.width=100000,
memory.limit=TRUE, verbose=TRUE)
参数----------Arguments----------
参数：data
Gene expression data in the MACAT list format. See data(stjude) for an example.
在麦科特列表格式的基因表达数据。见为例数据（stjude）。
参数：class
Which of the given class labels is to be analyzed
这是给定的类标签来进行分析
参数：chromosome
Chromosome to be analyzed
染色体进行分析
参数：nperms
Number of permutations
排列数
参数：permute
Method to do permutations. Default 'labels' does permutations of the class labels, which is the common and faster way to assess significance of differential expression. The altenative 'locations' does permutations of gene locations, is much slower and right now should be considered preliminary at best.
法做排列。默认的“标签”，排列之类的标签，这是共同的和更快的方式来评估差异表达的意义。 “位置”altenative做基因的位置排列，是要慢得多，现在应被视为最好的初步。
参数：pcompute
Method to determine the p-value for differential expression of each gene. Is only evaluated if the argument permute='labels' and in that case passed on to the function scoring
方法来确定每一个基因的表达差p值。只计算如果参数permute='labels'在这种情况下传递函数scoring
参数：subset
If a subset of samples is to be used, give vector of column- indices of these samples in the original matrix here.
如果是用于样品的一个子集，在原始矩阵列这些样本指数的向量。
参数：newlabels
If other labels than the ones in the MACAT-list-structure are to be used, give them as character vector/factor here. Make sure argument 'class' is one of them.
如果除麦科特列表结构的其他标签被使用，他们在这里给特征向量/因素。确保参类就是其中之一。
参数：kernel
Choose kernel to smooth scores along the chromose. Available are 'kNN' for k-Nearest-Neighbors, 'rbf' for radial-basis-function (Gaussian), 'basePairDistance' for a kernel, which averages over all genes within a given range of base pairs around a position.
选择内核沿chromose的顺利分数。可用“kNN的K-最近邻”，“RBF径向基函数（高斯），”basePairDistance“为内核，这对所有基因碱基对周围的位置范围内的平均。
参数：kernelparams
Additional parameters for the kernel as list, e.g., kernelparams=list(k=5) for taking the 5 nearest neighbours in the kNN-kernel. If NULL some defaults are set within the function.
额外的内核参数列表，例如，kernelparams =列表（K = 5）服用5最近邻居kNN的内核。如果为NULL的一些默认设置在函数内。
参数：cross.validate
Logical. Should the paramter settings for the kernel function be optimized by a cross-validation?
逻辑。应该放慢参数设置为内核函数进行优化，通过交叉验证？
参数：paramMultipliers
Numeric vector. If you do cross-validation of the kernel parameters, specify the multipliers of the given (standard) parameters to search over for the optimal one.
数字向量。如果你做交叉验证的内核参数，指定给定的参数（标准）的乘数寻找最佳的一个。
参数：ncross
Integer. If you do cross-validation, specify how many folds.
整数。如果你这样做交叉验证，指定多少褶皱。
参数：step.width
Defines the resolution of smoothed scores on the chromosome, is in fact the distance in base pairs between 2 positions, for which smoothed scores are to be calculated.
定义的分数在染色体上的平滑的分辨率，其实是2个职位，而平滑的分数计算中的碱基对之间的距离。
参数：memory.limit
If you have a computer with lots of RAM, setting this to FALSE will increase speed of computations.
如果你有一台电脑，有很多的内存设置为FALSE将提高计算速度。
参数：verbose
logical; should function's progress be reported to STDOUT ?; default: TRUE.
逻辑;函数的进步应该上报到stdout;默认：真。
Details
详情----------Details----------
Please see the package vignette for more details on this function.
此功能的更多细节，请参阅包小插曲。
值----------Value----------
List of class 'MACATevalScoring' with 11 components:
类的MACATevalScoring 11个元件的列表：
参数：original.geneid
Gene IDs of the genes on the chosen chromosome, sorted according to their position on the chromosome
选择染色体上的基因的基因ID，排序按照其在染色体上的位置
参数：original.loc
Location of genes on chromosome in base pairs from 5'end
染色体上基因的碱基对的位置，从5端
参数：original.score
Regularized t-score of genes on chromosome
正规化的T-分数染色体上的基因
参数：original.pvalue
Empirical p-value of genes on chromosome. How often was a higher score observed than this one with random permutations? In other words, how significant seems this score to be?
实证p值的染色体上的基因。往往是更高的分数比这一个随机排列的观察如何？换句话说，这个成绩怎么着，似乎是吗？
参数：steps
Positions on the chromosome in bp from 5', for which smoothed scores have been computed.
在BP染色体从5，而平滑的分数已经计算的岗位上。
参数：sliding.value
Smoothed regularized t-scores at step-positions.
平滑正规化继位的T-分数。
参数：lower.permuted.border
Smoothed scores from permutations, lower significance border, currently 2.5%-quantile of permutation scores.
从排列平滑的分数较低的意义边界，目前2.5％的分量排列分数。
参数：upper.permuted.border
Smoothed scores from permutations, upper significance border, currently 97.5%-quantile of permutation scores.
从排列，意义上的边界，目前97.5％的分量分数排列平滑的分数。
参数：chromosome
Chromosome, which has been analyzed
染色体，已分析
参数：class
Class, which has been analyzed
类，已分析
参数：chip
Identifier for used microarray
利用微阵列的标识符
作者（S）----------Author(s)----------
MACAT development team
参见----------See Also----------
scoring,plot.MACATevalScoring, getResults
scoring，plot.MACATevalScoring，getResults
举例----------Examples----------
data(stjd) # load example data[加载示例数据]
# if you have the data package 'stjudem' installed,[如果你有数据包stjudem“安装，]
# you should work on the full data therein, of which[你应该在其中完整的数据，]
# the provided example data, is just a piece[提供的数据为例，只不过是小菜一碟]
#loaddatapkg("stjudem")[loaddatapkg（“stjudem”）]
#data(stjude)[数据（stjude）]
# T-lymphocyte versus B-lymphocyte on chromosome 1, [T淋巴单元与B淋巴单元1号染色体上，]
# smoothed with k-Nearest-Neighbours kernel(k=15), [平滑k-最近邻居内核（K = 15），]
# few permutations for higher speed[更高速度的数排列]
chrom1Tknn <- evalScoring(stjd,"T",chromosome="1",permute="labels",
nperms=100,kernel=kNN,kernelparams=list(k=15),step.width=100000)
# plotting on x11:[上绘制X11：]
if (interactive())
plot(chrom1Tknn)
# plotting on HTML:[上绘制的HTML：]
if (interactive())
plot(chrom1Tknn,"html")
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 23:58:04
