maSigPro(maSigPro)
maSigPro()所属R语言包：maSigPro
Wrapping function for identifying significant differential gene expression profiles in micorarray time course experiments
查明在micorarray课程实验时间显着的基因差异表达谱的包装功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
maSigPro performs a whole maSigPro analysis for a times series gene expression experiment. The function sucesively calls the functions make.design.matrix(optional), p.vector, T.fit, get.siggenes and see.genes.
maSigPro执行了一个时代系列基因表达的实验的整体maSigPro分析。功能sucesively调用函数make.design.matrix（可选），p.vector，T.fit，get.siggenes和see.genes。
用法----------Usage----------
maSigPro(data, edesign, matrix = "AUTO", groups.vector = NULL,
degree = 2, time.col = 1, repl.col = 2, group.cols = c(3:ncol(edesign)),
Q = 0.05, alfa = Q, nvar.correction = FALSE, step.method = "backward", rsq = 0.7,
min.obs = 3, vars = "groups", significant.intercept = "dummy", cluster.data = 1,
add.IDs = FALSE, IDs = NULL, matchID.col = 1, only.names = FALSE, k = 9, m = 1.45,
cluster.method = "hclust", distance = "cor", agglo.method = "ward", iter.max = 500,
summary.mode = "median", color.mode = "rainbow", trat.repl.spots = "none",
index = IDs[, (matchID.col + 1)], match = IDs[, matchID.col], rs = 0.7,
show.fit = TRUE, show.lines = TRUE, pdf = TRUE, cexlab = 0.8,
legend = TRUE, main = NULL, ...)
参数----------Arguments----------
参数：data
matrix with normalized gene expression data. Genes must be in rows and arrays in columns. Row names must contain geneIDs (argument of p.vector)
规范化的基因表达数据的矩阵。基因必须是在中的行和列的阵列。行名称必须包含geneIDs（参数p.vector）
参数：edesign
matrix of experimental design. Row names must contain arrayIDs (argument of make.design.matrix and see.genes)
实验设计矩阵。行名称必须包含arrayIDs（make.design.matrix和see.genes参数）
参数：matrix
design matrix for regression analysis. By default design is calculated with make.design.matrix (argument of p.vector and T.fit, by default computed by make.design.matrix)
设计矩阵的回归分析。默认情况下，设计计算与make.design.matrix（p.vector和T.fit，make.design.matrix默认情况下，计算参数）
参数：groups.vector
vector indicating experimental group of each variable (argument of get.siggenes and see.genes, by default computed by make.design.matrix)
矢量显示，实验组的每个变量（get.siggenes和see.genes，make.design.matrix默认情况下，计算参数）
参数：degree
the degree of the regression fit polynome. degree = 1 returns lineal regression, degree = 2 returns quadratic regression, etc... (argument of make.design.matrix)
回归拟合的多项式程度。 degree= 1返回线性回归，degree= 2返回二次回归，等等...... （参数make.design.matrix）
参数：time.col
column in edesign containing time values. Default is first column (argument of make.design.matrix and see.genes)
edesign包含时间值的列。默认是第一列（参数make.design.matrix和see.genes）
参数：repl.col
column in edesign containing coding for replicates arrays. Default is second column (argument of make.design.matrix and see.genes)
在edesign列包含编码复制阵列。默认是第二列（参数make.design.matrix和see.genes）
参数：group.cols
columns in edesign indicating the coding for each group of the experiment (see make.design.matrix) (argument of make.design.matrix and see.genes)
列edesign每个实验组的编码（见make.design.matrix）（make.design.matrix和see.genes参数说明）
参数：Q
level of false discovery rate (FDR) control (argument of p.vector)
错误发现率（FDR）控制水平（p.vector的说法）
参数：alfa
significance level used for variable selection in the stepwise regression (argument of T.fit)
显着性水平逐步回归（T.fit参数）变量选择
参数：nvar.correction
logical for indicating correcting of stepwise regression significance level (argument of T.fit)
逻辑指示纠正逐步回归显着性水平（T.fit参数）
参数：step.method
argument to be passed to the step function. Can be either "backward", "forward", "two.ways.backward" or "two.ways.forward"
要传递给阶跃函数的参数。可以要么"backward"，"forward"，"two.ways.backward"或"two.ways.forward"
参数：rsq
cut-off level at the R-squared value for the stepwise regression fit. Only genes with R-squared greater than rsq are selected
截止水平在逐步回归拟合的R平方值。只有比rsq被选中的更大的R-平方基因
参数：min.obs
genes with less than this number of true numerical values will be excluded from the analysis (argument of p.vector and T.fit)
将被排除在分析与真正的数值比这少的基因（p.vector和T.fit参数）
参数：vars
variables for which to extract significant genes (argument of get.siggenes)
变量提取显著基因（get.siggenes的参数）
参数：significant.intercept
experimental groups for which significant intercept coefficients are considered (argument of get.siggenes)
被视为显着的拦截系数为实验组（参数get.siggenes）
参数：cluster.data
Type of data used by the cluster algorithm (argument of see.genes)
聚类算法（see.genes参数）所使用的数据类型
参数：add.IDs
logical indicating whether to include additional gene id's in the significant genes result (argument of get.siggenes)
逻辑说明是否包括额外的基因ID在显着的基因，结果（get.siggenes参数）是
参数：IDs
matrix contaning additional gene id information (required when add.IDs is TRUE) (argument of get.siggenes)
矩阵contaning额外的基因ID信息（需要时add.IDs是TRUE）（get.siggenes的参数）
参数：matchID.col
number of matching column in matrix IDs for adding genes ids (argument ofget.siggenes)
添加基因IDS（get.siggenes参数）匹配的ID矩阵列数
参数：only.names
logical. If TRUE, expression values are ommited in the significant genes result (argument of get.siggenes)
逻辑。如果是TRUE，表达值ommited显著基因的结果（get.siggenes的参数）
参数：k
number of clusters (argument of see.genes)
数字聚类（see.genes参数）
参数：m
m parameter when "mfuzz" clustering algorithm is used. See mfuzz (argument of see.genes)
m参数时"mfuzz"使用聚类算法。看到mfuzz（see.genes）参数
参数：cluster.method
clustering method for data partioning (argument of see.genes)
聚类方法数据partioning（参数see.genes）
参数：distance
distance measurement function used when cluster.method is "hclust" (argument of see.genes)
距离测量功能使用时cluster.method是"hclust"（see.genes参数）
参数：agglo.method
aggregation method used when cluster.method is "hclust" (argument of see.genes)
聚合方法用时cluster.method是"hclust"（see.genes参数）
参数：iter.max
number of iterations when cluster.method is "kmeans" (argument of see.genes)
迭代次数cluster.method是"kmeans"（see.genes参数）
参数：summary.mode
the method to condensate expression information when more than one gene is present in the data. Possible values are "representative" and "median" (argument of PlotGroups)
凝表达信息的方法，当一个以上的基因是在目前的数据。可能的值是"representative"和"median"（PlotGroups参数）
参数：color.mode
color scale for plotting profiles. Can be either "rainblow" or "gray" (argument of PlotProfiles)
绘制剖面的颜色规模。可以要么"rainblow"或"gray"（PlotProfiles的说法）
参数：trat.repl.spots
treatment givent to replicate spots. Possible values are "none" and "average" (argument of get.siggenes)
处理givent到复制点。可能的值是"none"和"average"（get.siggenes参数）
参数：index
argument of the average.rows function to use when trat.repl.spots is "average" (argument of get.siggenes)
average.rows函数的参数使用时trat.repl.spots是"average"（get.siggenes参数）
参数：match
argument of the link{average.rows} function to use when trat.repl.spots is "average" (argument of get.siggenes)
link{average.rows}函数的参数使用时trat.repl.spots是"average"（get.siggenes参数）
参数：rs
minimun pearson correlation coefficient for replicated spots profiles to be averaged (argument of get.siggenes)
复制的景点型材的最低限度的皮尔逊相关系数平均（get.siggenes的参数）
参数：show.fit
logical indicating whether regression fit curves must be plotted (argument of see.genes)
逻辑说明回归拟合曲线是否必须绘制（参数see.genes）
参数：show.lines
logical indicating whether a line must be drawn joining plotted data points for reach group (argument of see.genes)
逻辑指示行是否必须制定加盟绘制河段组数据点（see.genes的参数）
参数：pdf
logical indicating whether a pdf results file must be generated (argument of see.genes)
逻辑表示结果PDF文件是否必须生成（see.genes参数）
参数：cexlab
graphical parameter maginfication to be used for x labels in plotting functions
X标签使用绘图功能的图形参数maginfication
参数：legend
logical indicating whether legend must be added when plotting profiles (argument of see.genes)
逻辑表明是否图型材（see.genes参数时，必须添加传奇）
参数：main
title for pdf results file
结果PDF文件的标题
参数：...
other graphical function arguments
其他图形函数的参数
Details
详情----------Details----------
maSigPro finds and display genes with significant profile differences in time series gene expression experiments. The main, compulsory, input parameters for this function are a matrix of gene expression data (see p.vector for details) and a matrix describing experimental design (see make.design.matrix or p.vector for details). In case extended gene ID information is wanted to be included in the result of significant genes, a third IDs matrix containing this information will be required (see get.siggenes for details).
maSigPro发现和显着的剖面差异，在时间序列基因表达的实验显示基因。此功能为主，义务教育，输入参数是一个基因表达数据矩阵（见p.vector细节）和矩阵描述实验设计（详情见make.design.matrix或p.vector）。的情况下延长基因ID信息被通缉的重大基因的结果，第三IDS矩阵包含此信息将被要求（见详情get.siggenes）。
Basiscally in the function calls subsequent steps of the maSigPro approach which is:
basiscally的maSigPro的方法是调用的后续步骤中的函数：
Make a general regression model with dummies to indicate different experimental groups.
做一个假人一般回归模型来表示不同的实验组。
Select significant genes on the basis of this general model, applying fdr control.
选择这个一般模型的基础上显着的基因，应用FDR控制。
Find significant variables for each gene, using stepwise regression.
找到每个基因，采用逐步回归，显着的变量。
Extract and display significant genes for any set of variables or experimental groups.
任何变量或实验组的提取和显示显着的基因。
值----------Value----------
参数：summary
a vector or matrix listing significant genes for the variables given by the function parameters
一个向量或矩阵列表功能参数的变量显着的基因
参数：sig.genes
a list with detailed information on the significant genes found for the variables given by the function parameters. Each element of the list is also a list containing: sig.profiles: expression values of significant genes.The cluster assingment of each gene is given in the last column coefficients: regression coefficients for significant genes t.score: value of the t statistics of significant genes sig.pvalues: p-values of the regression coefficients for significant genes g: number of genes ... :arguments passed by previous functions
功能参数的变量中发现的重大基因上的详细信息列表。列表中的每个元素也是一个列表，其中包含：sig.profiles：重大每个基因genes.The的聚类assingment表达式的值是在最后一列coefficients：回归系数显著基因t.score ：重要基因t统计值sig.pvalues：P-值的回归系数显著基因g：基因的数量... ：由前面的函数传递参数
参数：input.data
input analysis data
输入数据分析
参数：G
number of input genes
输入基因的数量
参数：edesign
matrix of experimental design
实验设计矩阵
参数：dis
regression design matrix
回归设计矩阵
参数：min.obs
imputed value for minimal number of true observations
真正的观测数量最少的估算值
参数：p.vector
vector containing the computed p-values of the general regression model for each gene
向量的每一个基因的一般回归模型计算p值
参数：variables
variables in the general regression model
一般回归模型中的变量
参数：g
number of signifant genes
signifant基因数
参数：p.vector.alfa
p-vlaue at FDR = Q control
P-vlaueFDR=Q控制
参数：step.method
imputed step method for stepwise regression
估算步骤逐步回归方法
参数：Q
imputed value for false discovery rate (FDR) control
错误发现率（FDR）的控制估算值
参数：step.alfa
inputed significance level in stepwise regression
设算显着性水平逐步回归
参数：influ.info
data frame of genes containing influencial data
含有期影响数据的基因数据框
作者（S）----------Author(s)----------
Ana Conesa, aconesa@ivia.es; Maria Jose Nueda, mj.nueda@ua.es
参考文献----------References----------
maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.
参见----------See Also----------
make.design.matrix, p.vector, T.fit, get.siggenes, see.genes
make.design.matrix，p.vector，T.fit，get.siggenes，see.genes
举例----------Examples----------
#### GENERATE TIME COURSE DATA[＃＃＃生成时间的课程资料]
## generate n random gene expression profiles of a data set with [＃生成N个随机设置数据基因表达谱]
## 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, c3 = 1.2, 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 = "")
#### RUN maSigPro[＃＃运行maSigPro的]
tc.test <- maSigPro (tc.DATA, edesign, degree = 2, vars = "groups", main = "Test")
tc.test$g # gives number of total significant genes[给人总重大的基因数目]
tc.test$summary # shows significant genes by experimental groups[显示实验组的显著基因]
tc.test$sig.genes$Treat1$sig.pvalues # shows pvalues of the significant coefficients [重要系数的表演pvalues]
# in the regression models of the significant genes [在回归模型的重要基因]
# for Control.vs.Treat1 comparison[Control.vs.Treat1比较]
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 00:24:13
