significantMicroRna(AgiMicroRna)
significantMicroRna()所属R语言包：AgiMicroRna
Summarize Differential Expression Results
总结的差异表达结果
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function summarizes the results from the differential expression analysis using the different objects that are obtained after 'limma' has been used, such as the 'MArrayLM' object with the statistics and the 'TestResults' object highlighting the significant features.
功能总结从差异表达分析的结果，使用后得到“limma已使用，，如”MArrayLM“的统计对象和”TestResults的“对象，突出了显着的特点，不同的对象。
用法----------Usage----------
significantMicroRna(eset, ddset, targets, fit2,
CM, DE, DEmethod, MTestmethod, PVcut, Mcut,verbose=FALSE)
参数----------Arguments----------
参数：eset
ExpressionSet containing the Total Gene processed data
ExpressionSet含有共有的基因数据处理
参数：ddset
An uRNAList object containing the Total Gene proceseed data
共有基因uRNAList对象包含proceseed数据
参数：targets
data.frame with the target structure
数据框与目标结构
参数：fit2
MArrayLM object from eBayes 'limma' function
MArrayLM对象eBayeslimma“的功能
参数：CM
Contrast matrix
对比矩阵
参数：DE
TestResults object
TestResults的对象
参数：DEmethod
method used in decideTests, only 'separate' or 'nestedF' are implemented
在decideTests使用方法，只有独立或nestedF“实施
参数：MTestmethod
method for multiple test
多种测试方法
参数：PVcut
p value threshold to declare significant features
p值阈值申报显着的特点
参数：Mcut
M value threshold to select within significant features
M值阈值内显着的特点来选择
参数：verbose
logical, if TRUE prints out output
逻辑，如果TRUE打印出输出
Details
详情----------Details----------
A list containing the genes with their statistics is generated. The significant genes above the PVcut p values are also given in a html file that links the selected miRNAS to the miRBase http://microrna.sanger.ac.uk/. A MA plots indicating the differentially expressed genes are also displayed.
生成一个列表，其中包含的基因，他们的统计。以上的PVcut p值的显着的基因也给出了一个HTML文件，链接到的miRBase http://microrna.sanger.ac.uk/的选定的miRNA。也显示一个主图显示的差异表达基因。
When multiple contrasts are done, the method for the selection of the significant genes can be either 'separated' or 'nestedF'. See decideTests in package limma <CITE>limma</CITE> for a detailed description on these two methods. When 'separated' is used a list with all the genes that have been analized in limma is given. The list includes de following columns:
当多个对比，选择的重要基因的方法可以是分隔或nestedF“。看到包decideTests limma <CITE> limma </>引用了这两种方法的详细描述。当分离用于所有基因已在limma analized的列表。这份名单包括去下面的列：
PROBE - Probe name (one of the probes interrogating the gene) GENE - miRNA name PROBE chr\_coord - Agilent chromosomal location M - Fold change A - Mean of the intensity for that miRNA t - moderated t-statistic pval - p value of the t-statistic adj.pval - p value adjusted by 'MTestmethod' fdr.pval - p value adjusted by fdr
探针 - 探针名称（询问基因探针）基因 - miRNA的名称探针CHR \ _coord - 安捷伦的染色体定位中号 - 倍数变化 - 平均强度为miRNA的牛逼 - 主持t-统计的pval - p值T-有统计adj.pval - p值调整，由“MTestmethod fdr.pval - P值调整由FDR
Some times, the user can be set 'MTestmethod = none', in this case, it might be interesting to still see the fdr value, despite of the fact that the user has decided not apply any multiple testing correction.
有些时候，用户可以设置“MTestmethod =无”，在这种情况下，它可能是有趣的FDR值仍然可以看到，尽管该用户已决定其实并不适用于任何多个测试校正。
If the 'nestedF' is used, then two lists are provided for each contrasts. A first containing the selected significant genes, and a second list containing the rest of the genes that have been analyzed. The columns given in this case is:
如果“nestedF”，然后每个对比两份名单。第一个包含选定的显着的基因，和第二个列表，其中包含其余的基因进行了分析。在这种情况下，给定的列是：
PROBE - Probe name (one of the probes interrogating the gene) GENE - miRNA name PROBE chr\_coord - Agilent chromosomal location M - Fold change A - Mean of the intensity for that miRNA t - moderated t-statistic t pval - p value of the t-statistic F - F statistic (null hypothesis: Ci = Cj, for all contrasts i, j) adj.F.pval - F p value adjusted by 'MTestmethod' fdr.F.pval - F p value adjusted by fdr
探针 - 探针名称（询问基因探针）基因 - miRNA的名称探针CHR \ _coord - 安捷伦的染色体定位中号 - 倍数变化 - 平均强度为miRNA的牛逼 - 放缓T-统计量T pval的 - p值t-统计的F - F统计量（零假设：CI = CJ，所有对比I，J）adj.F.pval - F P价值“MTestmethod”fdr.F.pval调整的 - F P值调整FDR
The html files, both for the 'separated' and 'nestedF' method, includes only the selected as significant genes.
HTML文件，同时为“分隔”和“nestedF”的方法，包括只选定为显著的基因。
作者（S）----------Author(s)----------
Pedro Lopez-Romero
参考文献----------References----------
'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397–420.
http://microrna.sanger.ac.uk/
参见----------See Also----------
A 'uRNAList' example containing proccesed data is in ddPROC and an overview of how the processed data is produced is given in filterMicroRna. The ExpressionSet object can be generated using esetMicroRna An overview of miRNA differential expression analysis is given in basicLimma An example of how to get the 'TestResults' object is in getDecideTests
一个“uRNAList含有proccesed数据的例子是在ddPROC和处理的数据是如何产生的概述filterMicroRna。的ExpressionSet对象可以使用esetMicroRnamiRNA的差异表达分析概述basicLimma如何获得“TestResults的”对象的一个例子是getDecideTests产生
举例----------Examples----------
data(targets.micro)
data(ddPROC)
esetPROC=esetMicroRna(ddPROC,targets.micro,makePLOT=FALSE)
levels.treatment=levels(factor(targets.micro$Treatment))
treatment=factor(as.character(targets.micro$Treatment),
levels=levels.treatment)
levels.subject=levels(factor(targets.micro$Subject))
subject=factor(as.character(targets.micro$Subject),
levels=levels.subject)
design=model.matrix(~ -1 + treatment + subject )
CM=cbind(MSC_AvsMSC_B=c(1,-1,0,0),
MSC_AvsMSC_C=c(1,0,-1,0))
fit2=basicLimma(esetPROC,design,CM,verbose=TRUE)
DE=getDecideTests(fit2,
DEmethod="separate",
MTestmethod="BH",
PVcut=0.10)
significantMicroRna(esetPROC,
ddPROC,
targets.micro,
fit2,
CM,
DE,
DEmethod="separate",
MTestmethod="BH",
PVcut=0.10,
Mcut=0,
verbose=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。
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 2012-2-25 11:39:49
