p.vector(maSigPro)
p.vector()所属R语言包：maSigPro
Make regression fit for time series gene expression experiments
使回归拟合时间序列的基因表达实验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
p.vector performs a regression fit for each gene taking all variables present in the model given by a regression matrix and returns a list of FDR corrected significant genes.
p.vector执行的每个基因，以回归矩阵模型中所有变量的回归拟合，并返回FDR纠正显著基因的列表。
用法----------Usage----------
p.vector(data, design = NULL, Q = 0.05, MT.adjust = "BH", min.obs = 3)
参数----------Arguments----------
参数：data
matrix containing normalized gene expression data. Genes must be in rows and arrays in columns
矩阵包含标准化基因表达数据。基因必须是行和列的阵列
参数：design
design matrix for the regression fit such as that generated by the make.design.matrix function
如make.design.matrix函数生成符合设计矩阵的回归
参数：Q
significance level
显着性水平
参数：MT.adjust
argument to pass to p.adjust function indicating the method for multiple testing adjustment of p.value
参数传递p.adjust函数表示p.value多个测试调整方法
参数：min.obs
genes with less than this number of true numerical values will be excluded from the analysis. Default is 3 (minimun value for a quadratic fit)
与真正的数值比这少的基因将被排除在分析之外。默认值是3（最低限度的二次拟合值）
Details
详情----------Details----------
rownames(design) and colnames(data) must be identical vectors and indicate array naming.
rownames(design)和colnames(data)必须是相同的向量和指示阵列命名。
rownames(data) should contain unique gene IDs.
rownames(data)应包含独特的基因标识。
colnames(design) are the given names for the variables in the regression model.
colnames(design)是在回归模型中的变量名。
值----------Value----------
参数：SELEC
matrix containing the expression values for significant genes
基质含有重要基因的表达值
参数：p.vector
vector containing the computed p-values
向量p值计算
参数：G
total number of input genes
输入基因总数
参数：g
number of genes taken in the regression fit
数量的基因在回归拟合
参数：BH.alfa
p-value at FDR Q control when Benajamini & Holderberg (BH) correction is used
p值FDR Q控制Benajamini Holderberg（BH）校正时使用
参数：i
number of significant genes
数的显著基因
参数：dis
design matrix used in the regression fit
在回归拟合的设计矩阵
参数：dat
matrix of expression value data used in the regression fit
矩阵表达式的值使用的数据的回归拟合
参数：...
additional values from input parameters
输入参数的附加价值
作者（S）----------Author(s)----------
Ana Conesa, <a href="mailto:aconesa@ivia.es">aconesa@ivia.es</a>; Maria Jose Nueda,
<a href="mailto:mj.nueda@ua.es">mj.nueda@ua.es</a>
参考文献----------References----------
maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102
参见----------See Also----------
T.fit, lm
T.fit，lm
举例----------Examples----------
#### GENERATE TIME COURSE DATA[＃＃＃生成时间的课程资料]
## generates n random gene expression profiles of a data set with [＃生成N个随机基因的表达谱的设置与数据]
## one control plus 3 treatments, 3 time points and r replicates per time point.[＃一个控制加3个疗程，3个时间点和r每时间点复制。]
tc.GENE <- function(n, r,
var11 = 0.01, var12 = 0.01,var13 = 0.01,
var21 = 0.01, var22 = 0.01, var23 =0.01,
var31 = 0.01, var32 = 0.01, var33 = 0.01,
var41 = 0.01, var42 = 0.01, var43 = 0.01,
a1 = 0, a2 = 0, a3 = 0, a4 = 0,
b1 = 0, b2 = 0, b3 = 0, b4 = 0,
c1 = 0, c2 = 0, c3 = 0, c4 = 0)
{
tc.dat <- NULL
for (i in 1:n) {
Ctl <- c(rnorm(r, a1, var11), rnorm(r, b1, var12), rnorm(r, c1, var13)) # Ctl group[CTL组]
Tr1 <- c(rnorm(r, a2, var21), rnorm(r, b2, var22), rnorm(r, c2, var23)) # Tr1 group[TR1组]
Tr2 <- c(rnorm(r, a3, var31), rnorm(r, b3, var32), rnorm(r, c3, var33)) # Tr2 group[TR2组]
Tr3 <- c(rnorm(r, a4, var41), rnorm(r, b4, var42), rnorm(r, c4, var43)) # Tr3 group[TR3组]
gene <- c(Ctl, Tr1, Tr2, Tr3)
tc.dat <- rbind(tc.dat, gene)
}
tc.dat
}
## Create 270 flat profiles[＃创建270平剖面]
flat <- tc.GENE(n = 270, r = 3)
## Create 10 genes with profile differences between Ctl and Tr1 groups[＃创建10个基因与CTL和TR1组之间的轮廓差异]
twodiff <- tc.GENE (n = 10, r = 3, b2 = 0.5, c2 = 1.3)
## Create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups[＃创建10个基因与CTL，TR2，TR3组之间的轮廓差异]
threediff <- tc.GENE(n = 10, r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
## Create 10 genes with profile differences between Ctl and Tr2 and different variance[＃创建10个基因与CTL和TR2和不同方差之间的轮廓差异]
vardiff <- tc.GENE(n = 10, r = 3, a3 = 0.7, b3 = 1, c2 = 1.3, var32 = 0.03, var33 = 0.03)
## Create dataset[＃创建数据集]
tc.DATA <- rbind(flat, twodiff, threediff, vardiff)
rownames(tc.DATA) <- paste("feature", c(1:300), sep = "")
colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")
tc.DATA [sample(c(1 300*36)), 300)] <- NA # introduce missing values[引进缺失值]
#### CREATE EXPERIMENTAL DESIGN[＃＃＃创建一个实验设计]
Time <- rep(c(rep(c(1:3), each = 3)), 4)
Replicates <- rep(c(1:12), each = 3)
Control <- c(rep(1, 9), rep(0, 27))
Treat1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
Treat2 <- c(rep(0, 18), rep(1, 9), rep(0,9))
Treat3 <- c(rep(0, 27), rep(1, 9))
edesign <- cbind(Time, Replicates, Control, Treat1, Treat2, Treat3)
rownames(edesign) <- paste("Array", c(1:36), sep = "")
tc.p <- p.vector(tc.DATA, design = make.design.matrix(edesign), Q = 0.05)
tc.p$i # number of significant genes[数的显著基因]
tc.p$SELEC # expression value of signficant genes[表达式的值signficant基因]
tc.p$BH.alfa # p.value at FDR control[p.value控制在FDR]
tc.p$p.adjusted# adjusted p.values[调整p.values]
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 00:24:21
