tabulate.top.indep.features(SIM)
tabulate.top.indep.features()所属R语言包：SIM
Lists the mean z-scores for the independent features
列出的平均z分数为独立的功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Lists the mean z-scores for independent features in the analyzed regions, calculated across the significant dependent features. Gives insight in the expression levels most strongly associated with copy number changes.
列出的平均z分数为独立的功能，在分析区域计算，整个显着的相关功能。给人最强烈的拷贝数变化的相关表达水平的洞察力。
用法----------Usage----------
tabulate.top.indep.features(input.regions = "all chrs",
input.region.indep = NULL,
method = c("full", "smooth", "window", "overlap"),
adjust.method = "BY",
significance = 1,
decreasing=TRUE,
z.threshold = c(0, 0),
run.name = "analysis_results")
参数----------Arguments----------
参数：input.regions
vector indicating the dependent regions to be analyzed. Can be defined in four ways: 1) predefined input region: insert a predefined input region, choices are: “all chrs”, “all chrs auto”, “all arms”, “all arms auto” In the predefined regions “all arms” and “all arms auto” the arms 13p, 14p, 15p, 21p and 22p are left out, because in most studies there are no or few probes in these regions. To include them, just make your own vector of arms. 2) whole chromosome(s): insert a single chromosome or a list of chromosomes as a vector: c(1, 2, 3). 3) chromosome arms: insert a single chromosome arm or a list of chromosome arms like c("1q", "2p", "2q"). 4) subregions of a chromosome: insert a chromosome number followed by the start and end position like "chr1:1-1000000" These regions can also be combined, e.g. c("chr1:1-1000000","2q", 3). See integrated.analysis for more information.
vector表明依赖区域进行分析。可以定义在四个方面：1) predefined input region: 插入一个预定义的输入区域，选择是：“所有CHRS”，“所有CHRS汽车”，“武器”，“所有武器的汽车”在预定区域“所有武器”和“所有自动武器”的武器，13P，14P，15P，21P和22P冷落，因为在大多数研究中，有没有在这些区域或几个探针。包括他们，才使自己的武器向量。 2) whole chromosome(s): 插入一个vector:c(1, 2, 3)的一个单一的染色体或染色体列表。 3) chromosome arms: 插入一个单一的染色体或染色体臂像c("1q", "2p", "2q")名单。 4) subregions of a chromosome: 插入一个染色体数目的开始和结束位置，如"chr1:1-1000000"这些区域也可以结合，如c("chr1:1-1000000","2q", 3)。看到integrated.analysis更多信息。
参数：input.region.indep
fill in
填写
参数：method
this must be the either one of “full”, “window”, “overlap” or “smooth” but the data should generated by the same method in integrated.analysis.
这必须是“充分”的一个“窗口”，“重叠”或“平稳”，但数据应产生同样的方法在integrated.analysis。
参数：adjust.method
Method used to adjust the P-values for multiple testing, see p.adjust. Default is "BY" recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values.
用于调整多个测试P值的方法，看到p.adjust“。默认是“加”拷贝数时，建议使用相关数据。关于调整P值的更多信息，请参阅SIM卡。
参数：significance
threshold used to select the significant dependent features. Only the z-scores with these features are used to calculate the mean z-scores across the independent features.
阈值用来选择显着的相关功能。只有这些功能的Z-分数被用来计算平均Z-分数跨越独立的功能。
参数：decreasing
fill in
填写
参数：z.threshold
fill in
填写
参数：run.name
This must be the same a given to integrated.analysis
这必须是相同的一个给定的integrated.analysis
Details
详情----------Details----------
tabulate.top.indep.features can only be run after integrated.analysis with zscores = TRUE.
tabulate.top.indep.features只能被后integrated.analysis运行zscores = TRUE。
Output is a .txt file containing a table with the mean z-scores of all independent features per analyzed region. It includes the ann.indep columns that were read in the assemble.data function.
输出是一个。txt文件，其中包含一个表，平均每分析区域所有独立功能的Z-分数。它包括了ann.indep读在assemble.data功能列。
Additionally it returns a .txt file containing the significant zscores rich regions.
此外，它会传回含有丰富的区域的重大zscores。txt文件。
Depending on the argument "adjust.method", the P-values are first corrected for multiple testing. Next, th e z-scores are filtered to include only those entries that correspond to significant (P-value < "significa nce") dependent features to calculate the mean z-scores.
P值上参数“adjust.method”不同，首先纠正多个测试。接下来，日éZ-分数过滤，包括这些条目对应显著（P值<“significa NCE）相关的功能来计算的平均z分数。
The dependent table can not be generated for diagonal integrated runs.
不能产生依赖表对角线综合运行。
值----------Value----------
Returns a list of data.frame's for each input region. Significant P-value rich regions are returned as a data.frame. This data.frame can be used as an input for getoverlappingregions. Additionally, the results are stored in a subdirectory of run.name as txt.
返回为每个输入区域的list的data.frame。作为一个数据框返回显著P值的丰富的区域。这个数据框，可以用来作为输入getoverlappingregions。此外，结果存储在run.name为txt子目录。
作者（S）----------Author(s)----------
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes <a href="mailto:R.X.Menezes@lumc.nl">R.X.Menezes@lumc.nl</a>
参见----------See Also----------
SIM, tabulate.pvals, tabulate.top.dep.features
SIM卡，tabulate.pvals，tabulate.top.dep.features
举例----------Examples----------
#first run example(assemble.data)[第一次运行的例子（assemble.data）]
#and example(integrated.analysis)[和例如（integrated.analysis）]
table.indep <- tabulate.top.indep.features(input.regions="8q",
adjust.method="BY",
method="full",
significance= 0.05,
z.threshold=c(-1, 1),
run.name="chr8q")
head(table.indep[["8q"]])
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 2012-2-26 14:25:05
