RPadvance(RankProd)
RPadvance()所属R语言包：RankProd
Advanced Rank Product Analysis of Microarray
排名先进的芯片产品分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Advance rank product method to identify differentially expressed genes. It is possible to combine data from different studies, e.g. data sets
推进排名产品的方法来确定差异表达基因。它是可以从不同的研究，如结合数据数据集
用法----------Usage----------
RPadvance(data,cl,origin,num.perm=100,logged=TRUE,
na.rm=FALSE,gene.names=NULL,plot=FALSE,
rand=NULL)
参数----------Arguments----------
参数：data
the data set that should be analyzed. Every row of this data set must correspond to a gene.
应分析数据集。这组数据的每一行必须对应一个基因。
参数：cl
a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one group data, the label for each sample should be 1.
矢量样品含有类标签。在这两个类的未成的情况下，样品的标签是0（例如，对照组）或1（例如，病例组）。对于一组数据，对每一个样品的标签应为1。
参数：origin
a vector containing the origin labels of the sample. e.g. for the data sets generated at multiple laboratories, the label is the same for samples within one lab and different for samples from different labs.
矢量含有样品的产地标签。例如在多个实验室产生的数据集，标签是同在一个实验室样品和来自不同实验室的样品不同。
参数：num.perm
number of permutations used in the calculation of the null density. Default is 'B=100'.
空密度计算中使用的数字排列。默认值是“B = 100。
参数：logged
if "TRUE", data has bee logged, otherwise set it to "FALSE"
如果“真”，数据记录了蜜蜂，否则将其设置为“FALSE”
参数：na.rm
if 'FALSE' (default), the NA value will not be used in computing rank. If 'TRUE', the missing values will be replaced by the genewise mean of the non-missing values. Gene will all value missing will be assigned "NA"
如果“假”（默认），NA值将不会被用于计算排名。如果“TRUE”，缺少的值将被替换非缺失值2-6。平均。基因将所有的价值缺失，将分配的“不适用”
参数：gene.names
if "NULL", no gene name will be attached to the estimated percentage of false prediction (pfp).
如果“空”，没有基因的名称将被连接到虚假的预测，估计百分比（PFP）。
参数：plot
If "TRUE", plot the estimated pfp verse the rank of each gene
如果“真”，绘制估计亲民党诗句的每一个基因的排名
参数：rand
if specified, the random number generator will be put in a reproducible state.
如果指定，随机数发生器将在一个可重复的状态。
值----------Value----------
A result of identifying differentially expressed genes between two classes. The identification consists of two parts, the identification of up-regulated and down-regulated genes in class 2 compared to class 1, respectively.
一个确定的结果，两个类之间的差异表达的基因。由两部分组成，分别比1级2级上调和下调的基因鉴定，鉴定。
参数：pfp
estimated percentage of false positive predictions (pfp) up to the position of each gene under two identificaiton each
估计比例的假阳性预测（亲民党）在两个identificaiton每个基因的位置每个
参数：pval
estimated pvalue for each gene being up- and down-regulated
估计每个基因pvalue和下调
参数：RPs
Original rank-product of each genes for two i dentificaiton each
原来的每一个基因的两个产品排名，我dentificaiton每个
参数：RPrank
rank of the rank products of each gene in ascending order
排名产品的每一个基因的排名升序
参数：Orirank
original ranks in each comparison, which is used to compute rank product
原来的队伍，在每个比较，这是用来计算排名产品
参数：AveFC
fold change of average expression under class 1 over that under class 2, if multiple origin, than avraged across all origin. log-fold change if data is in log scaled, original fold change if data is unlogged.
倍下1级比2级下的平均表达变化，如果有多个起源，比avraged所有起源。log倍的变化，如果在log中的数据规模，原倍的变化，如果数据未记录。
参数：all.FC
fold change of class 1/class 2 under each origin. log-fold change if data is in log scaled
根据每个源类1/class 2倍的变化。如果数据是在扩展log的log倍的变化
注意----------Note----------
Percentage of false prediction (pfp), in theory, is equivalent of false discovery rate (FDR), and it is possible to be large than 1.
在理论上，是虚假的预测（PFP）的百分比，相当于假发现率（FDR），比1大，它是可能的。
The function looks for up- and down- regulated genes in two seperate steps, thus two pfps are computed and used to identify gene that belong to each group.
功能看起来和下调基因在两个单独的步骤，从而PFPS计算和用于确定属于每个组的基因。
The function is able to replace function RP in the same library. it is a more general version, as it is able to handle data from differnt origins.
该功能是能够取代在同一个库函数的RP。它是一个更普遍的版本，因为它是能够处理来自不同的充起源的数据。
作者（S）----------Author(s)----------
Fangxin Hong <a href="mailto:fhong@salk.edu">fhong@salk.edu</a>
参考文献----------References----------
P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
参见----------See Also----------
topGene RP plotRP RSadvance
topGeneRPplotRPRSadvance
举例----------Examples----------
# Load the data of Golub et al. (1999). data(golub) [Golub等装入的数据。 （1999年）。数据（戈卢布）]
# contains a 3051x38 gene expression[包含一个的3051x38基因表达]
# matrix called golub, a vector of length called golub.cl [矩阵称为戈卢布，向量的长度称为golub.cl]
# that consists of the 38 class labels,[由38类标签，]
# and a matrix called golub.gnames whose third column [矩阵称为其第三列golub.gnames]
# contains the gene names.[包含的基因名称。]
data(golub)
##For data with single origin[＃数据与单一起源]
subset <- c(1:4,28:30)
origin <- rep(1,7)
#identify genes [确定基因]
RP.out <- RPadvance(golub[,subset],golub.cl[subset],
origin,plot=FALSE,rand=123)
#For data from multiple origins[对于从多个来源的数据]
#Load the data arab in the package, which contains [阿拉伯加载数据包，其中包含]
# the expression of 22,081 genes[22,081基因的表达]
# of control and treatment group from the experiments [从实验的控制和治疗组]
#indenpently conducted at two [indenpently进行两个]
#laboratories.[实验室。]
data(arab)
arab.origin #1 1 1 1 1 1 2 2 2 2[1 1 1 1 1 1 2 2 2 2]
arab.cl #0 0 0 1 1 1 0 0 1 1[0 0 0 1 1 1 0 0 1 1]
RP.adv.out <- RPadvance(arab,arab.cl,arab.origin,
num.perm=100,gene.names=arab.gnames,logged=TRUE,rand=123)
attributes(RP.adv.out)
head(RP.adv.out$pfp)
head(RP.adv.out$RPs)
head(RP.adv.out$AveFC)
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。
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发表于 2012-2-26 11:57:26
