sigGeneSet(gage)
sigGeneSet()所属R语言包：gage
Significant gene set from GAGE analysis
从压力计分析的显著基因
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function sorts and counts signcant gene sets based on q- or p-value cutoff.
此功能的种类和数量signcant基因设置基于Q-p值截止。
用法----------Usage----------
sigGeneSet(setp, cutoff = 0.1, dualSig = (0:2)[2], qpval = c("q.val",
"p.val")[1],heatmap=TRUE, outname="array", pdf.size = c(7,7),
p.limit=c(0.5, 5.5), stat.limit=5, ...)
参数----------Arguments----------
参数：setp
the result object returned by gage function, either a numeric matrix or a list of two such matrices. Check gage help information for details.
gage函数返回结果对象，无论是数字矩阵或一两个这样的矩阵列表。检查gage有关详细信息，帮助信息。
参数：cutoff
numeric, q- or p-value cutoff, between 0 and 1. Default 0.1 (for q-value). When p-value is used, recommended cutoff value is 0.001 for data with more than 2 replicates per condition or 0.01 for les sample sizes.
数字，Q-p值截止，0和1之间。默认0.1（Q值）。当p值，建议的临界值是0.001 LES样本量超过2％的条件复制或0.01数据。
参数：dualSig
integer, switch argument controlling how dual-signficant gene sets should be treated. This argument is only useful when Stouffer method is not used in gage function (use.stouffer=FALSE), hence makes no difference normally. 0: discard such gene sets from the final significant gene set list; 1: keep such gene sets in the more significant direction and remove them from the less significant direction; 2: keep such gene sets in the lists for both directions. default to 1. Dual-signficant means a gene set is called significant simultaneously in both 1-direction tests (up- and down-regulated). Check the details for more information.
整数，开关参数控制，如何双-signficant的基因组应治疗。时斯托弗方法不使用（use.stouffer = FALSE），量具功能，因此通常不差，这种说法是非常有用的。 0：丢弃最终显着的基因组列表这样的基因组; 1：保持这样的基因组，并在更重要的方向，他们从不太重要的方向; 2：保持这样的基因组，在两个方向的名单。默认为1。的双signficant意味着一个基因组被称为显著同时在两个1方向测试（上升和下调）。有关详细信息，查看详情。
参数：qpval
character, specifies the column name used for gene set selection, i.e. what type of q- or p-value to use in gene set selection. Default to be "q.val" (q-value using BH procedure). "p.val" is the unadjusted global p-value and may be used as selection criterion sometimes.
字符，用于基因组选择指定的列名，即Q-p值在基因组的选择使用的是什么类型。默认为“q.val”（波黑过程的Q-值）。 “p.val”是未经调整的全球p值，可作为选择标准有时使用。
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：outname
a character string, to be used as the prefix of the output data files. Default to be "array".
一个字符串，可用于输出数据文件的前缀。默认为“阵列”。
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 the inside gs.heatmap function, which is a wrapper of the heatmap2 function.
其他参数被传递到内gs.heatmap功能，这是一个heatmap2函数的包装。
Details
详情----------Details----------
By default, heatmaps are produced to show the gene set perturbations using either -log10(p-value) or statistics.
默认情况下，热图生产，使用或者LOG10（p值）或统计显示基因组的扰动。
Since gage package version 2.2.0, Stouffer's method is used as the default procedure for more robust p-value summarization. With the original p-value summarization, i.e. negative log sum following a Gamma distribution as the Null hypothesis, the global p-value could be heavily affected by a small subset of extremely small individual p-values from pair-wise comparisons. Such sensitive global p-value leads to the "dual signficance" phenomenon. In other words, Gene sets are signficantly up-regulated in a subset of experiments, but down-regulated in another subset. Note that dual-signficant gene sets are not the same as gene sets called signficant in 2-directional tests, although they are related.
自计包版本2.2.0，斯托弗的方法是使用默认为更强大的P-值总结的过程。与原来的p值的总结，即负对数的总和，作为零假设的Gamma分布，全球的P-值可以严重影响一个非常小的个人成对比较p值的一小部分。这种敏感的全球p值导致“双建设的重大意义”的现象。换句话说，基因组signficantly上调，在实验的一个子集，但在另一个子集下调。请注意，双signficant基因组是不一样的设置在2定向测试signficant，虽然他们有关的基因。
值----------Value----------
sigGeneSet function returns a named list of the same structure as gage result. Check gage help information for details.
sigGeneSet函数返回一个名为gage结果相同的结构列表。检查gage有关详细信息，帮助信息。
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; esset.grp non-redundant signcant gene set list; essGene essential member genes in a gene set;
gage压力计分析的主要功能;esset.grp的非冗余signcant的基因组名单;essGene在基因组的重要成员的基因;
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)
#kegg test for 1-directional changes[KEGG测试为1方向变化]
gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs,
ref = hn, samp = dcis)
#kegg test for 2-directional changes[KEGG为2方向变化的测试]
gse16873.kegg.2d.p <- gage(gse16873, gsets = kegg.gs,
ref = hn, samp = dcis, same.dir = FALSE)
gse16873.kegg.sig<-sigGeneSet(gse16873.kegg.p, outname="gse16873.kegg")
str(gse16873.kegg.sig)
gse16873.kegg.2d.sig<-sigGeneSet(gse16873.kegg.2d.p, outname="gse16873.kegg")
str(gse16873.kegg.2d.sig)
#also check the heatmaps in pdf files named "*.heatmap.pdf".[检查PDF文件名为“*。heatmap.pdf”热图。]
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 18:24:14
