glad(GLAD)
glad()所属R语言包：GLAD
Analysis of array CGH data
阵列CGH数据的分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function allows the detection of breakpoints in genomic profiles obtained by array CGH technology and affects a status (gain, normal or lost) to each clone.
此功能允许阵列比较基因组杂交技术获得的基因组剖面的断点检测状态（增益，正常或丢失），并影响到每个克隆。
用法----------Usage----------
## S3 method for class 'profileCGH'
glad(profileCGH, mediancenter=FALSE,
smoothfunc="lawsglad", bandwidth=10, round=1.5,
model="Gaussian", lkern="Exponential", qlambda=0.999,
base=FALSE, sigma,
lambdabreak=8, lambdacluster=8, lambdaclusterGen=40,
type="tricubic", param=c(d=6),
alpha=0.001, msize=5,
method="centroid", nmax=8, assignGNLOut=TRUE,
breaksFdrQ = 0.0001, haarStartLevel = 1, haarEndLevel = 5,
verbose=FALSE, ...)
参数----------Arguments----------
参数：profileCGH
Object of class profileCGH
对象类profileCGH
参数：mediancenter
If TRUE, LogRatio are centered on their median.
如果TRUE，对数比中位数为中心。
参数：smoothfunc
Type of algorithm used to smooth LogRatio by a piecewise constant function. Choose either lawsglad, haarseg, aws or laws in aws package.
算法类型用于平滑分段常数函数LogRatio的。选择要么lawsglad，haarseg，aws或laws在AWS包。
参数：bandwidth
Set the maximal bandwidth hmax in the aws or laws functions in aws package. For example, if bandwidth=10 then the hmax value is set to 10*X_N where X_N is the position of the last clone.
设置最大带宽hmaxaws或laws在AWS包功能。例如，如果bandwidth=10hmax值设置为10 *X_N其中X_N最后克隆的立场。
参数：round
The smoothing results are rounded or not depending on the round argument. The round value is passed to the argument digits of the round function.
平滑结果四舍五入或不取决于round参数。 round值传递参数digits的round功能。
参数：model
Determines the distribution type of the LogRatio. Keep always the model as "Gaussian" (see laws in aws package).
确定的对数比的分布类型。始终保持“高斯”（见laws在AWS包）模型。
参数：lkern
Determines the location kernel to be used (see aws or laws in aws package).
确定的位置要使用的内核（见aws或laws在AWS包）。
参数：qlambda
Determines the scale parameter for the stochastic penalty (see aws or laws in aws package)
确定为随机处罚尺度参数（见aws或laws在AWS包）
参数：base
If TRUE, the position of clone is the physical position on the chromosome, otherwise the rank position is used.
如果TRUE，克隆的位置是在染色体上的物理位置，否则的排名位置。
参数：sigma
Value to be passed to either argument sigma2 ofaws function or shape of laws (see aws package). If NULL, sigma is calculated from the data.
值被传递到任何一个参数sigma2aws函数shapelaws（AWS包）。如果NULL，Sigma是从数据计算。
参数：lambdabreak
Penalty term (λ') used during the Optimization of the number of breakpoints step.
惩罚项（λ'）用于在断点一步优化。
参数：lambdacluster
Penalty term (λ*) used during the MSHR clustering by chromosome step.
惩罚项（λ*）期间，染色体步MSHR的聚类。
参数：lambdaclusterGen
Penalty term (λ*) used during the HCSR clustering throughout the genome step.
惩罚项（λ*）用于在整个基因组步HCSR聚类。
参数：type
Type of kernel function used in the penalty term during the Optimization of the number of breakpoints step, the MSHR clustering by chromosome step and the HCSR clustering throughout the genome step.
在点球术语使用过程中断点一步，通过染色体步的MSHR聚类和整个基因组步HCSR聚类优化的内核函数的类型。
参数：param
Parameter of kernel used in the penalty term.
在点球术语使用的内核的参数。
参数：alpha
Risk alpha used for the Outlier detection step.
风险阿尔法用于离群检测步骤。
参数：msize
The outliers MAD are calculated on regions with a cardinality greater or equal to msize.
疯狂的离群计算与基数更大或等于MSIZE的的区域。
参数：method
The agglomeration method to be used during the MSHR clustering by chromosome and the HCSR clustering throughout the genome clustering steps.
集聚在MSHR聚类染色体和整个基因组聚类步骤HCSR聚类方法。
参数：nmax
Maximum number of clusters (N*max) allowed during the the MSHR clustering by chromosome and the HCSR clustering throughout the genome clustering steps.
期间由染色体的MSHR的聚类和整个基因组聚类步骤HCSR聚类允许的最大簇数目（不适用*最大）。
参数：assignGNLOut
If FALSE the status (gain/normal/loss) is not assigned for outliers.
如果FALSE的状态（增益/正常/亏损）未分配的离群。
参数：breaksFdrQ
breaksFdrQ for HaarSeg algorithm.
breaksFdrQ为HaarSeg算法。
参数：haarStartLevel
haarStartLevel for HaarSeg algorithm.
haarStartLevel HaarSeg算法。
参数：haarEndLevel
for HaarSeg algorithm.
HaarSeg算法。
参数：verbose
If TRUE some information are printed
如果TRUE一些信息打印
参数：...
...
...
Details
详情----------Details----------
The function glad implements the methodology which is described in the article: Analysis of array CGH data: from signal ratio to gain and loss of DNA regions (Hup |
发表于 2012-2-25 20:38:33
