gagePipe(gage)
gagePipe()所属R语言包：gage
GAGE analysis pipeline
仪器分析管道
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function gagePipe runs mutliple rounds of GAGE in a batch without interference, and outputs signcant gene set lists in text format, heatmaps in pdf format, and save the results in RData format.
功能gagePipe运行了一批无干扰复式压力计轮，输出signcant基因中的文本格式，PDF格式的热图，并保存在RDATA格式的结果集列表。
用法----------Usage----------
gagePipe(arraydata, dataname = "arraydata", trim.at=TRUE, sampnames, gsdata = NULL,
gsname = c("kegg.gs", "go.gs"), ref.list, samp.list, weight.list = NULL,
comp.list = "paired", q.cutoff = 0.1, heatmap=TRUE, pdf.size = c(7,
7), p.limit=c(0.5, 5.5), stat.limit=5, ... )
参数----------Arguments----------
参数：arraydata
corresponds to exprs argument for gage function. But can either be a matrix-like data structure when the data has been loaded into R or the full path to the data file in .RData format if the data has not been loaded.
对应exprsgage函数的参数。但可以是一个矩阵类的数据结构时，数据已被加载到R的。RDATA格式的数据文件的完整路径，如果数据尚未加载。
参数：dataname
character, the name of the data to be analyzed. This name will be included as the prefix of the output file names. Default to be "arraydata".
字符，名称的数据进行分析。这个名字将作为输出文件名的前缀。默认为arraydata“。
参数：trim.at
boolean, whether to trim the suffix "_at" from the probe set IDs or row names of the microarray data. Default to be TRUE.
布尔，是否修剪后缀“_at”，从探针组ID或行的微阵列数据的名称。默认为TRUE。
参数：sampnames
character vector, the names of the sample groups, on which the GAGE analysis is done. Each sample groups corresponds to one element of samp.list and the matching element of ref.list. These names will be included in the output file names or object names.
特征向量，样本组的名称，量具分析完成。每个样本组对应一个元素samp.list和匹配元素的ref.list。这些名称将包括在输出文件名或对象名。
参数：gsdata
character, the full path to the gene set data file in .RData format if the data has not been loaded. Default to be NULL, i.e. the gene set data has been loaded. Make sure that the same gene ID system is used for both gsdata and arraydata.
性格，基因组数据文件。RDATA格式的完整路径，如果数据还没有被加载。默认为NULL，即基因组数据已加载。确保相同的基因ID系统是使用两个gsdata和arraydata。
参数：gsname
character, the name(s) of the gene set collections to be used. Default to be c("kegg.gs", "go.gs").
字符，基因组的集合名称（S）要使用。默认为c("kegg.gs", "go.gs")。
参数：ref.list
a list of ref inputs for gage function. In other words, each element of the list is a column number vector for the reference condition or phenotype (i.e. the control group) in the exprs data matrix.
refgage功能输入。换句话说，列表中的每个元素是一个参考条件，或在exprs数据矩阵型（即对照组）的列数向量。
参数：samp.list
a list of samp inputs for gage function. In other words, each element of the list is a column number vector for the target condition or phenotype (i.e. the experiment group) in the exprs data matrix.
sampgage功能输入。换句话说，列表中的每个元素是列数向量为目标的条件，或在exprs数据矩阵型（即实验组）。
参数：weight.list
a list or a vector of weights input(s) for gage function. As a list, the length of weight.list should equal to the length of ref.list and samp.list or 1. The weight.list vector or its element vectors of should match the elements of ref.list and samp.list in length or being NULL. When weight.list is a vector or length 1 list, all analyses will use the same weights setting.
weights函数列表或gage（S）输入向量。作为一个列表，weight.list长度应等于ref.list和samp.list或1的长度。 weight.list向量或其元素的向量，应符合ref.list和samp.list长度或空元素。当weight.list是一个向量或长度1名单，所有的分析，将使用相同的weights设置。
参数：comp.list
a list or a vector of compare input(s) for gage function. The length of the list or vector should equal to the length of ref.list and samp.list or 1. In the latter case, all analyses will use the same comparison scheme. The same as compare, the element value(s) in comp.list can be 'paired', 'unpaired', '1ongroup' or 'as.group'. Default to be 'paired'.
compare函数列表或gage（S）输入向量。列表或向量的长度应该等于ref.list和samp.list或1的长度。在后一种情况下，所有的分析，将使用相同的比较方案。一样的compare，comp.list元素的值（S）可以“配对”，“未成，1 ongroup”的或“as.group”。默认为“配对”。
参数：q.cutoff
numeric, q-value cutoff between 0 and 1 for signficant gene sets selection. Default to be 0.1.
数字，截止Q值介于0和1 signficant基因设置选择。默认为0.1。
参数：heatmap
boolean, whether to plot heatmap for the selected gene data as a PDF file. Default to be FALSE.
布尔值，是否为PDF文件选定的基因数据绘制的热图。默认为FALSE。
参数：pdf.size
a numeric vector to specify the the width and height of PDF graphics region in inches. Default to be c(7, 7).
一个数值向量PDF图形区域的高度和宽度，以英寸为单位指定。默认为C（7，7）。
参数：stat.limit
numeric vector of length 1 or 2 to specify the value range of gene set statistics to visualize using the heatmap. Statistics beyong will be reset to equal the proximal limit. Default to 5, i.e. plot all gene set statistics within (-5, 5) range. May also be NULL, i.e. plot all statistics without limit. This argument allows optimal differentiation between most gene set statistic values when extremely positive/negative values exsit and squeeze the normal-value region.
数字矢量的长度为1或2到指定的基因组统计值的范围，可视化使用的热图。统计beyong将重置等于近端的限制。默认为5，即小区所有的基因组（-5，5）范围内的统计资料。也可能是空的，即积的所有统计信息没有限制。这种说法使大多数基因组统计值之间的最佳分化时，正/负值exsit和挤压的正常价值的区域。
参数：p.limit
numeric vector of length 1 or 2 to specify the value range of gene set -log10(p-values) to visualize using the heatmap. Values beyong will be reset to equal the proximal limit. Default to c(0.5,5.5), i.e. plot all -log10(p-values) within this range. This argument is similar to argument stat.limit.
数字矢量的长度为1或2到指定的基因组LOG10（p值）值范围的可视化使用的热图。值beyong将重置等于近端限制。默认为C（0.5,5.5），即图在此范围内所有的LOG10（p值）。这种说法是类似说法stat.limit。
参数：...
other arguments to be passed into gage or gs.heatmap function, which is a wrapper of the heatmap2 function.
其他参数被传递到gage或gs.heatmap函数，这是一个heatmap2函数的包装。
Details
详情----------Details----------
gagePipe carries two rounds of GAGE analysis on each sample groups for each gene set collection specified in gsnames: one test for 1-direction changes (up- or down-regualted gene sets), one test for 2-direction changes (two-way perturbed gene sets). Correspondingly, the gage result p-value matrices for the signficant gene sets are written into two tab-delimited text files, named after the dataname and sampnames. Note that the text file for 1-direction changes tests combines results for both up- and down-regulated gene sets. By default, heatmaps in pdf format are also produced to show the gene set perturbations using either -log10(p-value) or statistics. Meanwhile, the full gage analysis result objects (named lists of p-value or statistics matrices) are saved into a .RData file. The result objects are name after the sampnames and gsnames.
gagePipe进行两个轮压力计分析每个基因组中指定的集合，每个样本组gsnames：一个测试1改变方向（向上或向下的regualted基因组），一个测试2改变方向（双向扰动基因集）。相应地，gage结果p值的signficant基因组矩阵写入两个制表符分隔的文本文件，命名后dataname和sampnames。注1方向变化测试的文本文件和下调的基因组相结合的结果。默认情况下，PDF格式的热图，也有生产，使用或者LOG10（p值）或统计显示基因组的扰动。同时，充分gage分析结果对象（命名为P-值或统计矩阵列表）到。RDATA文件保存。结果对象名后sampnames和gsnames。
值----------Value----------
The function returns invisible 1 when successfully executed.
成功执行时，该函数返回无形1。
作者（S）----------Author(s)----------
Weijun Luo <luo_weijun@yahoo.com>
参考文献----------References----------
Generally Applicable Gene Set Enrichment for Pathways Analysis. BMC Bioinformatics 2009, 10:161
参见----------See Also----------
gage the main function for GAGE analysis; heter.gage GAGE analysis for heterogeneous data
gage压力计分析的主要功能;heter.gage压力计异构数据分析
举例----------Examples----------
data(gse16873)
cn=colnames(gse16873)
hn=grep('HN',cn, ignore.case =TRUE)
dcis=grep('DCIS',cn, ignore.case =TRUE)
data(kegg.gs)
library(gageData)
data(gse16873.2)
cn2=colnames(gse16873.2)
hn2=grep('HN',cn2, ignore.case =TRUE)
dcis2=grep('DCIS',cn2, ignore.case =TRUE)
#multiple GAGE analysis in a batch with the combined data[在分析与综合数据批处理多个压力计]
gse16873=cbind(gse16873, gse16873.2)
dataname='gse16873' #output data prefix[输出数据的前缀]
sampnames=c('dcis.1', 'dcis.2')
refList=list(hn, hn2+12)
sampList=list(dcis, dcis2+12)
gagePipe(gse16873, gsname = "kegg.gs", dataname = "gse16873",
sampnames = sampnames, ref.list = refList, samp.list = sampList,
comp.list = "paired")
#follow up comparison between the analyses[跟进之间的比较分析]
load('gse16873.gage.RData')
#list gage result objects[列表量具结果对象]
objects(pat = "[.]p$")
gageComp(sampnames, dataname, gsname = "kegg.gs",
do.plot = TRUE)
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 2012-2-25 18:23:20
