essGene(gage)
essGene()所属R语言包：gage
Essential member genes in a gene set
在基因组的基本成员的基因
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function extracts data for essential member genes in a gene set. Essential genes are genes that have changes over noise level.
此功能为在基因组的重要成员的基因中提取的数据。重要基因的基因，有超过噪音水平的变化。
用法----------Usage----------
essGene(gs, exprs, ref = NULL, samp = NULL, gsets = NULL, compare
= "paired", use.fold = TRUE, rank.abs = FALSE, use.chi = FALSE, chi.p =
0.05, ...)
参数----------Arguments----------
参数：gs
character, either the name of an interesting gene set in a gene set collection passed by gsets argument, or a vector of gene IDs. Make sure that the same gene ID system is used for both gs and exprs.
字符，要么gsets的说法，或基因标识的向量，通过在基因组收集了一个有趣的基因的名称。确保相同的基因ID系统是使用两个gs和exprs。
参数：exprs
an expression matrix or matrix-like data structure, with genes as rows and samples as columns.
表达矩阵或矩阵类似的数据结构，行和列的样本的基因。
参数：ref
a numeric vector of column numbers for the reference condition or phenotype (i.e. the control group) in the exprs data matrix. Default ref = NULL, all columns are considered as target experiments.
一个参考条件，或在exprs数据矩阵型（即对照组）的列数的数字向量。默认REF = NULL，所有列被视为靶实验。
参数：samp
a numeric vector of column numbers for the target condition or phenotype (i.e. the experiment group) in the exprs data matrix. Default samp = NULL, all columns other than ref are considered as target experiments.
一列数为目标的条件或在exprs数据矩阵型（即实验组）的数字向量。默认SAMP = NULL，比文献中的所有列被视为靶实验。
参数：gsets
a named list, each element contains a gene set that is a character vector of gene IDs or symbols. For example, type head(kegg.gs). A gene set can also be a "smc" object defined in PGSEA package. Make sure that the same gene ID system is used for both gsets and exprs. Default to be NULL, then argument gs needs to be a vector of gene IDs.
一个名为列表，每个元素包含一个基因组，基因ID或符号，是一个特征向量。例如，键入head(kegg.gs)。 A基因组也可以是“SMC”PGSEA包中定义的对象。确保相同的基因ID系统是使用两个gsets和exprs。默认为空，然后参数gs需要一个向量基因标识。
参数：compare
character, which comparison scheme to be used: 'paired', 'unpaired', '1ongroup', 'as.group'. 'paired' is the default, ref and samp are of equal length and one-on-one paired by the original experimental design; 'as.group', group-on-group comparison between ref and samp; 'unpaired' (used to be '1on1'), one-on-one comparison between all possible ref and samp combinations, although the original experimental design may not be one-on-one paired; '1ongroup', comparison between one samp column at a time vs the average of all ref columns.
性格，比较计划将用于：配对，未成，1 ongroup，as.group“。 “配对”是默认，ref和桑普是平等的长度和原来的实验设计配对的一对“as.group”，组组ref和桑普之间的比较“未成”（用于1 ON1“），一对所有可能的ref和桑普的组合，虽然比较原始的实验设计可能没有一对配对; 1 ongroup”，一个桑普列比平均时间之间的比较所有文献列。
参数：use.fold
Boolean, whether the input gage results used fold changes or t-test statistics as per gene statistics. Default use.fold= TRUE.
布尔，是否输入gage结果倍变更或每个基因统计t检验统计。默认use.fold = TRUE。
参数：rank.abs
boolean, whether to sort the essential gene data based on absoluate changes. Default to be FALSE.
布尔值，是否基于absoluate变化的重要基因数据进行排序。默认为FALSE。
参数：use.chi
boolean, whether to use chi-square test to select the essential genes. Default to be FALSE, use the mean plus standard deviation of all gene changes instead. Check details for more information.
布尔值，是否使用卡方检验，以选择重要基因。默认是假的，使用的平均值加上标准差，而不是所有的基因变化。检查以获得更多信息的细节。
参数：chi.p
numeric value between 0 and 1, cutoff p-value for the chi-square test to select the essential genes. Default to 0.05. <tr valign="top"><td>...</td>
0和1之间的数值，截止p值的卡方检验，选择的重要基因。默认为0.05。 <tr valign="top"> <TD>...</ TD>
other arguments to be passed into the inside gagePrep function.
其他参数被传递到内gagePrep函数。
Details
详情----------Details----------
There are two different criteria for essential gene selection. One uses a chi-square test to determin whether the change of a gene is more than noise. A second considers any changes beyond 1 standard deviation from mean of all genes as real.
必需的基因选择有两种不同的标准。一个使用卡方检验，以determin一个基因的变化是否是比噪音更。第二个认为从实际所有基因的平均标准偏差超过1任何变化。
Note that essential genes are different from core genes considered in esset.grp function. Essential genes may change in a different direction than the overall change of a gene set. But core genes need to change in the in the interesting direction(s) of the gene set test.
请注意，重要基因是esset.grp功能考虑的核心基因不同。比整体变化的一个基因组，重要基因可能在不同的方向改变。但核心的基因需要在改变基因组测试（S）的有趣的方向。
值----------Value----------
A expression data matrix extracted for the essential genes, with similar structure as exprs.
表达数据矩阵中提取的重要基因，为exprs类似的结构。
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; geneData output and visualization of expression data for selected genes; esset.grp non-redundant signcant gene set list;
gage压力计分析的主要功能; geneData输出和选择的基因表达数据的可视化;esset.grp非冗余signcant基因组名单;
举例----------Examples----------
data(gse16873)
cn=colnames(gse16873)
hn=grep('HN',cn, ignore.case =TRUE)
dcis=grep('DCIS',cn, ignore.case =TRUE)
#kegg test for 1-directional changes[KEGG测试为1方向变化]
data(kegg.gs)
gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs,
ref = hn, samp = dcis)
rownames(gse16873.kegg.p$greater)[1:3]
gs=unique(unlist(kegg.gs[rownames(gse16873.kegg.p$greater)[1:3]]))
essData=essGene(gs, gse16873, ref =hn, samp =dcis)
head(essData)
ref1=1:6
samp1=7:12
#generated text file for data table, pdf files for heatmap and scatterplot[为数据表生成文本文件，PDF文件热图和散点图]
for (gs in rownames(gse16873.kegg.p$greater)[1:3]) {
outname = gsub(" |:|/", "_", substr(gs, 10, 100))
geneData(genes = kegg.gs[[gs]], exprs = essData, ref = ref1,
samp = samp1, outname = outname, txt = TRUE, heatmap = TRUE,
Colv = FALSE, Rowv = FALSE, dendrogram = "none", limit = 3, scatterplot = 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。
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发表于 2012-2-25 18:22:54
