cn.mops(cn.mops)
cn.mops()所属R语言包：cn.mops
Performs the cn.mops algorithm for copy number detection in
执行cn.mops拷贝数检测算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs the cn.mops algorithm for copy number detection in NGS data.
执行农工商数据的拷贝数检测cn.mops算法。
用法----------Usage----------
cn.mops(input,
I = c(0.025, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4),
classes = c("CN0", "CN1", "CN2", "CN3", "CN4", "CN5", "CN6", "CN7", "CN8"),
priorImpact = 1, cyc = 20, parallel = 0,
normType = "poisson", normQu = 0.25, norm = TRUE,
upperThreshold = 0.5, lowerThreshold = -0.9,
minWidth = 3, segAlgorithm = "fast", ...)
参数----------Arguments----------
参数：input
Either an instance of "GRanges" or a raw data matrix, where columns are interpreted as samples and rows as genomic regions. An entry is the read count of a sample in the genomic region.
无论是“农庄”或原始数据矩阵，其中列解释作为样本和基因组区域行的一个实例。一个条目是只读计数的样本中的基因组区域。
参数：I
Vector positive real values that contain the expected fold change of the copy number classes. Length of this vector must be equal to the length of the "classes" parameter vector. For human copy number polymorphisms we suggest to use the default I = c(0.05,0.5,1,1.5,2,2.5,3,3.5,4).
媒介积极包含预期倍的拷贝数类变化的实际价值。这个向量的长度必须是平等的“类”参数向量的长度。对于人类拷贝数多态性，我们建议使用默认的I = C（0.05,0.5,1,1.5,2,2.5,3,3.5,4）。
参数：classes
Vector of characters of the same length as the parameter vector "I". One vector element must be named "CN2". The names reflect the labels of the copy number classes. Default = c("CN0","CN1","CN2","CN3","CN4","CN5","CN6","CN7","CN8").
矢量作为参数向量“我”的相同长度的字符。一个向量元素必须被命名为“CN2的”。名字反映的拷贝数类的标签。默认值=
参数：priorImpact
Positive real value that reflects how strong the prior assumption affects the result. The higher the value the more samples will be assumed to have copy number 2. Default = 1.
积极的实际价值，反映多么强大的事先假设影响的结果。数值越高，更多的样本将被假定为有拷贝数2。默认为1。
参数：cyc
Positive integer that sets the number of cycles for the algorithm. Usually after less than 15 cycles convergence is reached. Default = 20.
正整数的算法设置的周期数。一般小于15周期收敛后达到。默认值= 20。
参数：parallel
How many cores are used for the computation. If set to zero than no parallelization is applied. The package "snow" has to be installed for this option. Default = 0.
用于计算有多少个内核。如果设置为零，比没有应用的并行。 “雪”的包，必须安装此选项。默认值= 0。
参数：normType
Mode of the normalization technique. Possible values are "mean","min","median","quant", "poisson" and "mode". Read counts will be scaled sample-wise. Default = "poisson".
标准化的技术模式。可能的值是“是什么意思”，“分”，“中位数”，“定量”，“泊”和“模式”。读取计数缩放样本明智的。默认=“泊松”。
参数：normQu
Real value between 0 and 1. If the "normType" parameter is set to "quant" then this parameter sets the quantile that is used for the normalization. Default = 0.25.
0和1之间的真正价值。如果“normType的”参数设置为“定量”，那么这个参数设置标准化位数。默认值= 0.25。
参数：norm
Logical that indicates whether normalization should be applied or not. Default = TRUE.
逻辑，指出是否应适用或不标准化。默认= TRUE。
参数：upperThreshold
Positive real value that sets the cut-off for copy number gains. All CNV calling values above this value will be called as "gain". The value should be set close to the log2 of the expected foldchange for copy number 3 or 4. Default = 0.5.
正实值设置为拷贝数收益切断。所有CNV的通话值高于此值将被称为“增益”。该值应设置为3或4个拷贝数预计foldchange的log2。默认值= 0.5。
参数：lowerThreshold
Negative real value that sets the cut-off for copy number losses. All CNV calling values below this value will be called as "loss". The value should be set close to the log2 of the expected foldchange for copy number 1 or 0. Default = -0.9.
负实值设置拷贝数损失切断。所有CNV的通话值低于此值将被称为“损失”。预计foldchange拷贝数0或1的log2的值应设置为关闭。默认值= -0.9。
参数：minWidth
Positive integer that is exactly the parameter "min.width" of the "segment" function of "DNAcopy". minWidth is the minimum number of segments a CNV should span. Default = 4.
正整数，是完全的“段”功能的“DNAcopy”的参数“min.width”的。 minWidth段的最低数量1 CNV的跨越。默认值= 4。
参数：segAlgorithm
Which segmentation algorithm should be used. If set to "DNAcopy" circular binary segmentation is performed. Any other value will initiate the use of our fast segmentation algorithm. Default = "fast".
应使用哪个分割算法。如果设置为“DNAcopy”循环二元分割。其他任何值都将开始使用我们的快速分割算法。默认=“快”。
参数：...
Additional parameters will be passed to the "DNAcopy" or the standard segmentation algorithm.
额外的参数将被传递到“DNAcopy”或标准的分割算法。
值----------Value----------
An instance of "CNVDetectionResult".
“CNVDetectionResult”的实例。
作者（S）----------Author(s)----------
Guenter Klambauer <a href="mailto:klambauer@bioinf.jku.at">klambauer@bioinf.jku.at</a>
举例----------Examples----------
data(cn.mops)
cn.mops(XRanges)
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 15:32:55
