get.siggenes(maSigPro)
get.siggenes()所属R语言包：maSigPro
Extract significant genes for sets of variables in time series gene expression experiments
提取变量时间序列中的基因表达实验组的显著基因
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function creates lists of significant genes for a set of variables whose significance value has been computed with the T.fit function.
这个函数创建一组变量，其意义与T.fit函数的计算值已显著基因的名单。
用法----------Usage----------
get.siggenes(tstep, rsq = 0.7, add.IDs = FALSE, IDs = NULL, matchID.col = 1,
only.names = FALSE, vars = c("all", "each", "groups"),
significant.intercept = "dummy",
groups.vector = NULL, trat.repl.spots = "none",
index = IDs[, (matchID.col + 1)], match = IDs[, matchID.col],
r = 0.7)
参数----------Arguments----------
参数：tstep
a T.fit object
T.fit对象
参数：rsq
cut-off level at the R-squared value for the stepwise regression fit. Only genes with R-squared more than rsq are selected
截止水平在逐步回归拟合的R平方值。只有R平方比RSQ更多的基因选择
参数：add.IDs
logical indicating whether to include additional gene id's in the result
逻辑说明是否包括额外的基因ID是结果
参数：IDs
matrix contaning additional gene id information (required when add.IDs is TRUE)
矩阵contaning额外的基因ID信息（需要时add.IDs是TRUE）
参数：matchID.col
number of matching column in matrix IDs for adding genes ids
加入基因IDS匹配矩阵的ID列的数
参数：only.names
logical. If TRUE, expression values are ommited in the results
逻辑。如果TRUE，表达值结果ommited
参数：vars
variables for which to extract significant genes (see details)
提取显着的基因变量（见详情）
参数：significant.intercept
experimental groups for which significant intercept coefficients are considered (see details)
被视为显着的拦截系数为实验组（见详情）
参数：groups.vector
required when vars is "groups".
需要时vars是"groups"。
参数：trat.repl.spots
treatment given to replicate spots. Possible values are "none" and "average"
给复制点处理。可能的值是"none"和"average"
参数：index
argument of the average.rows function to use when trat.repl.spots is "average"
average.rows函数的参数使用时 trat.repl.spots是"average"的
参数：match
argument of the average.rows function to use when trat.repl.spots is "average"
average.rows函数的参数使用时trat.repl.spots是"average"的
参数：r
minimun pearson correlation coefficient for replicated spots profiles to be averaged
复制的景点型材的最低限度的皮尔逊相关系数的平均值
Details
详情----------Details----------
There are 3 possible values for the vars argument:
有3 VARS参数的可能值：
"all": generates one single matrix or gene list with all significant genes.
"all"：生成单一矩阵所有重大的基因或基因的列表。
"each": generates as many significant genes extractions as variables in the general regression model. Each extraction contains the significant genes for that variable.
"each"：产生许多显着的基因作为一般的回归模型中的变量提取。每次提取包含该变量的显着的基因。
"groups": generates a significant genes extraction for each experimental group.
"groups"：生成一个显着的基因提取各实验组。
The difference between "each" and "groups" is that in the first case the variables of the same group (e.g. "TreatmentA" and "time*TreatmentA" ) will be extracted separately and in the second case jointly.
"each"和"groups"之间的差异，在第一种情况相同的组（例如"TreatmentA"和"time*TreatmentA" ）将分别在第二种情况下提取共同变量。
When add.IDs is TRUE, a matrix of gene ids must be provided as argument of IDs, the matchID.col column of which having same levels as in the row names of sig.profiles. The option only.names is TRUE will generate a vector of significant genes or a matrix when add.IDs is set also to TRUE.
当add.IDs是TRUE，矩阵基因IDS必须提供的ID作为参数，matchID.col列，其中有作为在sig.profiles行的名称相同的水平。选项only.names是TRUE将产生的显著基因的向量或当add.IDs设置TRUE矩阵。
When trat.repl.spots is "average", match and index vectors are required for the average.rows function. In gene expression data context, the index vector would contain geneIDs and indicate which spots are replicates. The match vector is used to match these genesIDs to rows in the significant genes matrix, and must have the same levels as the row names of sig.profiles.
当trat.repl.spots是"average"，match和indexaverage.rows函数向量。在基因表达数据的情况下，index向量将包含geneIDs，并指出这点是复制。使用match向量匹配这些genesIDs到显著基因矩阵中的行，必须有sig.profiles行名称相同的水平。
The argument significant.intercept modulates the treatment for intercept coefficients to apply for selecting significant genes when vars equals "groups". There are three possible values: "none", no significant intercept (differences) are considered for significant gene selection, "dummy", includes genes with significant intercept differences between control and experimental groups, and "all" when both significant intercept coefficient for the control group and significant intercept differences are considered for selecting significant genes.
参数significant.intercept调节治疗拦截系数申请选择显著的基因，当vars等于"groups"。有三个可能的值："none"，无显着性的拦截（差异）为显着的基因选择考虑，"dummy"，包括与对照组和实验组之间的显着拦截差异的基因，"all"当两个对照组和显着的拦截差异显着的截距系数被认为是选择显著的基因。
add.IDs = TRUE and trat.repl.spots = "average" are not compatible argumet values. add.IDs = TRUE and only.names = TRUE are compatible argumet values.
add.IDs= TRUE和trat.repl.spots="average"不兼容argumet的值。 add.IDs= TRUE和only.names=TRUE兼容argumet值。
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:
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: expression values of significant genes
sig.profiles的重大基因的表达值
coefficients: regression coefficients of the adjusted models
coefficients：调整后的模型的回归系数
groups.coeffs: regression coefficients of the impiclit models of each experimental group
groups.coeffs：各实验组的impiclit模型回归系数
sig.pvalues: p-values of the regression coefficients for significant genes
sig.pvalues：P-值的回归系数显著基因
g: number of genes
g：基因
...: arguments passed by previous functions
...：由以前的函数传递参数
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. Bioinformatics 22, 1096-1102
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 = "")
tc.p <- p.vector(tc.DATA, design = make.design.matrix(edesign), Q = 0.01)
tc.tstep <- T.fit(data = tc.p , alfa = 0.05)
## This will obtain sigificant genes per experimental group [＃这将获得每sigificant基因实验组]
## which have a regression model Rsquared > 0.9[＃有一个回归模型Rsquared，> 0.9]
tc.sigs <- get.siggenes (tc.tstep, rsq = 0.9, vars = "groups")
## This will obtain all sigificant genes regardless the Rsquared value. [＃这将获得所有sigificant基因无论Rsquared价值。]
## Replicated genes are averaged.[＃复制的基因平均。]
IDs <- rbind(paste("feature", c(1:300), sep = ""),
rep(paste("gene", c(1:150), sep = ""), each = 2))
tc.sigs.ALL <- get.siggenes (tc.tstep, rsq = 0, vars = "all", IDs = IDs)
tc.sigs.groups <- get.siggenes (tc.tstep, rsq = 0, vars = "groups", significant.intercept="dummy")
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。
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发表于 2012-2-26 00:23:49
