daglad(GLAD)
daglad()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'
daglad(profileCGH, mediancenter=FALSE,
normalrefcenter=FALSE, genomestep=FALSE,
OnlySmoothing = FALSE, OnlyOptimCall = FALSE,
smoothfunc="lawsglad", lkern="Exponential",
model="Gaussian", qlambda=0.999, bandwidth=10,
sigma=NULL, base=FALSE, round=2,
lambdabreak=8, lambdaclusterGen=40, param=c(d=6),
alpha=0.001, msize=2, method="centroid", nmin=1, nmax=8, region.size=2,
amplicon=1, deletion=-5, deltaN=0.10, forceGL=c(-0.15,0.15),
nbsigma=3, MinBkpWeight=0.35, DelBkpInAmp=TRUE, DelBkpInDel=TRUE,
CheckBkpPos=TRUE, assignGNLOut=TRUE,
breaksFdrQ = 0.0001, haarStartLevel = 1,
haarEndLevel = 5, weights.name = NULL,
verbose=FALSE, ...)
参数----------Arguments----------
参数：profileCGH
Object of class profileCGH
对象类profileCGH
参数：mediancenter
If TRUE, LogRatio are center on their median.
如果TRUE，对数比是中心，对他们的中位数。
参数：genomestep
If TRUE, a smoothing step over the whole genome is performed and a "clustering throughout the genome" allows to identify a cluster corresponding to the Normal DNA level. The threshold used in the daglad function (deltaN, forceGL, amplicon, deletion) and then compared to the median of this cluster.
如果TRUE，在整个基因组的平滑步骤进行，“整个基因组的聚类”，使正常的DNA水平，以确定相应的聚类。 daglad功能（deltaN, forceGL, amplicon, deletion），然后比较这个星团中位数的阈值。
参数：normalrefcenter
If TRUE, the LogRatio are centered through the median of the cluster identified during the genomestep.
如果TRUE，对数比围绕通过genomestep期间确定的聚类中位数。
参数：OnlySmoothing
If TRUE, only segmentation is performed without optimization of breakpoints and calling.
TRUE如果，只有分割无断点的优化和调用。
参数：OnlyOptimCall
If TRUE, the user can provide data which have been already segmented. In this case, profileCGH\$profileValues must contain a field with the name "Smoothing". The daglad function skip the smoothing step but bith the optimization of breakpoints and calling are performed.
如果TRUE，用户可以提供已经分割的数据。在这种情况下，profileCGH \ $ profileValues必须包含字段名称“平滑”。跳过平滑步骤daglad功能，但bith优化断点和通话。
参数：smoothfunc
Type of algorithm used to smooth LogRatio by a piecewise constant function. Choose either lawsglad, haarseg, aws or laws (aws package).
算法类型用于平滑分段常数函数LogRatio的。选择是lawsglad，haarseg，aws或laws（AWS包）。
参数：lkern
lkern determines the location kernel to be used (see laws in aws package for details).
lkern确定的位置，要使用的内核（见laws在AWS包的详细信息）。
参数：model
model determines the distribution type of LogRatio (see laws in aws package for details).
模型确定的对数比的分布类型（见laws在AWS包的细节）。
参数：qlambda
qlambda determines the scale parameter qlambda for the stochastic penalty (see laws in aws package for details).
qlambda随机处罚决定（见laws在AWS包的详细信息）尺度参数qlambda。
参数：base
If TRUE, the position of clone is the physical position onto the chromosome, otherwise the rank position is used.
如果是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 of aws (see aws package) function or shape of laws (see aws package). If NULL, sigma is calculated from the data.
值要传递两个参数sigma2aws（参阅AWS包）功能或shapelaws（AWS包）。如果NULL，Sigma是从数据计算。
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 of either aws or laws functions (in aws package) are rounded or not depending on the round argument. The round value is passed to the argument digits of the round function.
平滑的结果要么aws或laws函数（AWS包）的圆形或不取决于round参数。 round值传递参数digits的round功能。
参数：lambdabreak
Penalty term (λ') used during the "Optimization of the number of breakpoints" step.
惩罚项（λ'）使用过程中的第一步“优化断点”。
参数：lambdaclusterGen
Penalty term (λ*) used during the "clustering throughout the genome" step.
惩罚项（λ*）用于在整个基因组的聚类“一步”。
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”一步。
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 "clustering throughout the genome" steps.
结块的方法将在“整个基因组的聚类”的步骤。
参数：nmin
Minimum number of clusters (N*max) allowed during the "clustering throughout the genome" clustering step.
聚类的最低数量（N *最大）允许在整个基因组的聚类“聚类一步”。
参数：nmax
Maximum number of clusters (N*max) allowed during the "clustering throughout the genome" clustering step.
在整个基因组的聚类“聚类一步”允许的最大簇数目（不适用*最大）。
参数：region.size
The breakpoints which define regions with a number of probes lower or equal to this value are discared.
discared断点定义区域低于或等于该值的探针。
参数：amplicon
Level (and outliers) with a smoothing value (log-ratio value) greater than this threshold are consider as amplicon. Note that first, the data are centered on the normal reference value computed during the "clustering throughout the genome" step.
平滑值（log率值）大于这个阈值水平（和离群），是考虑扩增。请注意，第一，在整个基因组的聚类“一步”的正常参考值计算为中心的数据。
参数：deletion
Level (and outliers) with a smoothing value (log-ratio value) lower than this threshold are consider as deletion. Note that first, the data are centered on the normal reference value computed during the "clustering throughout the genome" step.
平滑值（log率值）低于此阈值水平（和离群）考虑删除。请注意，第一，在整个基因组的聚类“一步”的正常参考值计算为中心的数据。
参数：deltaN
Region with smoothing values in between the interval [-deltaN,+deltaN] are supposed to be normal.
与区域之间的间隔平滑值[DELTAN + DELTAN]应该是正常的。
参数：forceGL
Level with smoothing value greater (lower) than rangeGL[1] (rangeGL[2]) are considered as gain (lost). Note that first, the data are centered on the normal reference value computed during the "clustering throughout the genome" step.
平滑值大于（下）的水平比rangeGL[1]（rangeGL[2]）认为收益（损失）。请注意，第一，在整个基因组的聚类“一步”的正常参考值计算为中心的数据。
参数：nbsigma
For each breakpoints, a weight is calculated which is a function of absolute value of the Gap between the smoothing values of the two consecutive regions. Weight = 1- kernelpen(abs(Gap),param=c(d=nbsigma*Sigma)) where Sigma is the standard deviation of the LogRatio.
对于每个断点，重量计算，这是一个平滑值连续两个区域之间的差距的绝对值的函数。重量= 1 - kernelpen（ABS（GAP），参数= C（D = nbsigma *西格玛）），其中Sigma是标准差的对数比。
参数：MinBkpWeight
Breakpoints which GNLchange==0 and Weight less than MinBkpWeight are discarded.
断点GNLchange== 0和Weight比MinBkpWeight被丢弃。
参数：DelBkpInAmp
If TRUE, the breakpoints identified inside amplicon regions are deleted. For amplicon, the log-ratio values are highly variable which lead to identification of false positive breakpoints.
如果是TRUE，扩增区域内确定的断点被删除。扩增，数比值是高度可变的，从而导致假阳性断点鉴定。
参数：DelBkpInDel
If TRUE, the breakpoints identified inside deletion regions are deleted. For deletion, the log-ratio values are highly variable which lead to identification of false positive breakpoints.
如果是TRUE，删除区域内确定的断点被删除。删除log比率值是高度可变的，从而导致假阳性断点鉴定。
参数：CheckBkpPos
If TRUE, the accuracy position of each breakpoints is checked.
如果TRUE，检查每个断点的准确位置。
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
haarEndLevel for HaarSeg algorithm.
haarEndLevel HaarSeg算法。
参数：weights.name
The name of the fields which contains the weights used for the haarseg algorithm. By default, the value is set to NULL meaning that all the observations have the same weights. If provided, the field must contain positive values.
名称等领域，其中包含用于为haarseg算法的权重。默认情况下，该值设置为NULL，这意味着所有的意见，有相同的重量。如果提供，该字段必须包含正面的价值观。
参数：verbose
If TRUE some information are printed.
如果TRUE打印一些信息。
参数：...
...
...
Details
详情----------Details----------
The function daglad implements a slightly modified version of the methodology 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 20:38:24
