goodSamplesGenesMS(WGCNA)
goodSamplesGenesMS()所属R语言包：WGCNA
Iterative filtering of samples and genes with too many missing entries across multiple data sets
迭代有太多的遗漏的项目在多个数据集的样本和基因筛选
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function checks data for missing entries and zero variance across multiple data sets and returns a list of samples and genes that pass criteria maximum number of missing values. If necessary, the filtering is iterated.
此功能检查遗漏的项目和跨多个数据集的方差为零的数据，并返回一个列表，通过标准缺失值的最大数量的样本和基因。如果有必要，过滤被重复。
用法----------Usage----------
goodSamplesGenesMS(
multiExpr,
minFraction = 1/2,
minNSamples = ..minNSamples,
minNGenes = ..minNGenes,
verbose = 2, indent = 0)
参数----------Arguments----------
参数：multiExpr
expression data in the multi-set format (see checkSets). A vector of lists, one per set. Each set must contain a component data that contains the expression data, with rows corresponding to samples and columns to genes or probes.
表达在多集的格式的数据（见checkSets）。一个向量的列表，每一个组。每个集必须包含一个组件data包含的表达数据，与对应的基因或探针的样品和列的行。
参数：minFraction
minimum fraction of non-missing samples for a gene to be considered good.
非缺失的基因要考虑好样的最低分数。
参数：minNSamples
minimum number of non-missing samples for a gene to be considered good.
被视为良好的基因非缺失样本的最小数目。
参数：minNGenes
minimum number of good genes for the data set to be considered fit for analysis. If the actual number of good genes falls below this threshold, an error will be issued.
良好的数据集被认为是合适的分析基因的最小数目。如果实际的优良基因数目低于此阈值时，会发出错误。
参数：verbose
integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
整数的详细程度。零表示沉默，较高的值使输出越来越多，更详细。
参数：indent
indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.
缩进诊断消息。零表示无压痕，每个单元增加两个空格。
Details
详细信息----------Details----------
This function iteratively identifies samples and genes with too many missing entries, and genes with zero variance. Iterations may be required since excluding samples effectively changes criteria on genes and vice versa. The process is repeated until the lists of good samples and genes are stable. The constants ..minNSamples and ..minNGenes are both set to the value 4.
此功能反复识别样本和基因有太多的遗漏的项目，并与零的基因变异。迭代可能是必需的，因为不包括样品有效地改变标准的基因，反之亦然。重复该过程，直到列表良好范例和基因的是稳定的。的常量..minNSamples和..minNGenes都设置为值4。
值----------Value----------
A list with the foolowing components:
列表的foolowing组件：
参数：goodSamples
A list with one component per given set. Each component is a logical vector with one entry per sample in the corresponding set that is TRUE if the sample is considered good and FALSE otherwise.
给定的一个组成部分，每一个列表。每个组件是一个逻辑向量与每一个条目中的相应的一组，即样品TRUE，如果样品被认为是良好的和FALSE否则。
参数：goodGenes
A logical vector with one entry per gene that is TRUE if the gene is considered good and FALSE otherwise.
与每一个条目的基因，是一个逻辑矢量TRUE，如果基因被认为是好，FALSE否则。
（作者）----------Author(s)----------
Peter Langfelder
参见----------See Also----------
goodGenes, goodSamples, goodSamplesGenes for cleaning individual sets separately;
goodGenes，goodSamples，goodSamplesGenes为单独清洁个体套;
goodSamplesMS, goodGenesMS for additional cleaning of multiple data
goodSamplesMS，goodGenesMS多个数据清洗
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 2012-10-1 21:14:10
