BASH(beadarray)
BASH()所属R语言包：beadarray
BASH - BeadArray Subversion of Harshlight
BASH的 - BeadArray颠覆Harshlight
译者：生物统计家园网 机器人LoveR
描述----------Description----------
BASH is an automatic detector of physical defects on an array. It is designed to detect three types of defect - COMPACT, DIFFUSE and EXTENDED.
Bash是一个阵列上的物理缺陷的自动检测。它是用来检测三种类型的缺陷 - 紧凑，弥漫性和扩展。
用法----------Usage----------
BASH(BLData, array, neighbours=NULL, transFun = logGreenChannelTransform,
outlierFun = illuminaOutlierMethod, compn=3, wtsname=NULL, compact = TRUE,
diffuse = TRUE, extended = TRUE, cinvasions = 10, dinvasions = 15,
einvasions = 20, bgcorr = "median", maxiter = 10, compcutoff = 8,
compdiscard = TRUE, diffcutoff = 10, diffsig = 0.0001, diffn = 3,
difftwotail = FALSE, useLocs = TRUE, ...)
参数----------Arguments----------
参数：BLData
BeadLevelList
BeadLevelList
参数：array
integer specifying which section/array to plot.
整数，指定节/绘图阵列。
参数：neighbours
the user may specify the neighbours matrix, rather than have BASH calculate it. Time can be saved if using BASH and HULK, by calculating the neighbours matrix once and passing it to the two functions.
用户可以指定邻居的矩阵，而不是有BASH的计算。可以节省时间，如果使用bash和HULK，邻居矩阵计算一次，并把它传递给这两个函数。
参数：transFun
function to use to transform data prior to running BASH
功能使用数据转换之前运行bash
参数：outlierFun
the choice of outlier calling function to use.
在选择离群通话功能使用。
参数：compn
Numerical - when finding outliers in the compact analysis, how many MADs away from the median (for example) an intensity must be for it to be labelled an outlier.
数值 - 寻找在紧凑分析离群时，许多MADS如何远离中位数（例如）的强度必须为它被标记为离群值。
参数：wtsname
name under which bead weights are stored in the BLData object. It is only necessary to specify this if a) weights have already been set, and b) you wish BASH to observe them.
存储在BLData对象名称下珠重量。它是只需要指定此，如果已经设置）重量，和b）您想bash来观察他们。
参数：compact
Logical - Perform compact analysis?
逻辑 - 执行紧凑分析？
参数：diffuse
Logical - Perform diffuse analysis?
逻辑 - 执行弥漫分析吗？
参数：extended
Logical - Perform extended analysis?
逻辑 - 执行扩展分析？
参数：cinvasions
Integer - number of invasions used whenever closing the image - see BASHCompact
整数 - 关闭图像时使用的入侵 - 看到BASHCompact
参数：dinvasions
Integer - number of invasions used in diffuse analysis, to find the kernel - see BASHDiffuse
整数 - 弥漫性分析使用的入侵，找到内核 - 看到BASHDiffuse
参数：einvasions
Integer - number of invasions used when filtering the error image - see BGFilter.
整数 - 过滤错误的图像时使用的入侵 - 看到BGFilter。
参数：bgcorr
One of "none", "median", "medianMAD" - Used in diffuse analysis, this determines how we attempt to compensate for the background varying across an array. For example, on a SAM array this should be left at "median", or maybe even switched to "none", but if analysing a large beadchip then you might consider setting this to "medianMAD". (this code is passed to the method argument of BGFilter). Note that "none" may be the correct setting if HULK has already been applied.
“无”，“中位数”，“medianMAD”之一 - 在弥漫分析中的应用，这决定了我们如何试图弥补整个数组的不同背景。例如，在SAM阵列应留在“位数”，甚至切换到“无”，但如果分析大beadchip然后你可能会考虑设置这“medianMAD”。 （此代码传递给methodBGFilter参数）。需要注意的是“无”可能是正确的设置HULK如果已经应用。
参数：maxiter
Integer - Used in compact analysis - the max number of iterations allowed. (Exceeding this results in a warning.)
整数 - 紧凑分析 - 迭代允许的最大数量。 （超过警告这个结果。）
参数：compcutoff
Integer - the threshold used to determine whether a group of outliers is in a compact defect. In other words, if a group of at least this many connected outliers is found, then it is labelled as a compact defect.
整数 - 用于确定是否在一个紧凑的缺陷是一组离群的阈值。换句话说，如果发现一组至少有许多连接离群，然后它被标记为一个紧凑的缺陷。
参数：compdiscard
Logical - should we discard compact defect beads before doing the diffuse analyis?
逻辑 - 我们应该抛弃在做弥漫analyis紧凑的缺陷珠？
参数：diffcutoff
Integer - this is the threshold used to determine the minimum size that clusters of diffuse defects must be.
整数 - 这是确定的最小尺寸，弥漫性缺陷的聚类必须使用的阈值。
参数：diffsig
Probability - The significance level of the binomial test performed in the diffuse analysis.
概率 - 二项式测试的意义在弥漫分析执行。
参数：diffn
Numerical - when finding outliers on the diffuse error image, how many MADs away from the median an intensity must be for it to be labelled an outlier.
数值 - 寻找离群上弥漫的错误形象时，许多MADS如何远离中位数强度必须是它被标记为离群值。
参数：difftwotail
Logical - If TRUE, then in the diffuse analysis, we consider the high outlier and low outlier images seperately.
逻辑 - 如果属实，那么在弥漫性分析，我们认为高离群值和低离群图像的分开。
参数：useLocs
Logical - If TRUE then a .locs file corresponding to the array is sought and, if found, used to identify the neighbouring beads. If FALSE the neighbours are infered algorithmically. See generateNeighbours for more details.
逻辑 - 如果TRUE，那么相应的阵列1 LOCS文件要求，如果发现，用来识别邻近的珠。如果为FALSE的邻居算法推导。看到generateNeighbours更多细节。
参数：...
Logical - Perform compact analysis?
逻辑 - 执行紧凑分析？
Details
详情----------Details----------
The BASH pipeline function performs three types of defect analysis on an image.
BASH管道函数的图像上执行的三种类型的缺陷分析。
The first, COMPACT DEFECTS, finds large clusters of outliers, as per BASHCompact. The outliers are found using findAllOutliers(). We then find which outliers are clustered together. This process is iterative - having found a compact defect, we remove it, and then see if any more defects are found.
第一，紧凑的缺陷，发现离群的大型聚类，每BASHCompact。离群点被发现使用findAllOutliers()。然后，我们发现这离群聚集在一起。这个过程是迭代 - 发现了一个紧凑的缺陷，我们将其删除，然后看看，如果发现任何更多的缺陷。
The second, DIFFUSE DEFECTS, finds areas which are densely populated with outliers (which are not necessarily connected), as per BASHDiffuse. To make this type of defect more obvious, we first generate an ERROR IMAGE, and then find outliers based on this image. (The error image is calculated by using method = "median" and bgfilter = "medianMAD" in generateE, unless ebgcorr = FALSE in which case we use bgfilter = "median".) Now we consider a neighbourhood around each bead and count the number of outlier beads in this region. Using a binomial test we determine whether this is more that we would expect if the outliers were evenly spread over the entire array. If so, we mark it as a diffuse defect. (A clustering algorithm similar to the compact defect analysis is run to reduce false positives.)
第二，弥漫性的缺陷，认为这是人口稠密的离群点（不一定是连接），每BASHDiffuse区域。为了使这种类型的缺陷越来越明显，我们首先生成一个错误，然后根据这个图片找到离群。 （计算错误图像通过使用method = "median"和bgfilter = "medianMAD"generateE，除非ebgcorr = FALSE在这种情况下，我们使用bgfilter = "median"）。现在我们考虑周围邻里每个珠子，并指望在这一区域的的离群珠数量。使用二项式测试，我们确定这是否是更多，我们希望如果离群被均匀地分布在整个阵列蔓延。如果是这样，我们纪念它作为一种弥漫性缺陷。 （运行一个类似紧凑的缺陷分析的聚类算法，以减少误报。）
After each of these two analyses, we "close" the image, filling in gaps.
这两个分析后，我们的“关闭”的形象，填补空白。
The third, EXTENDED DEFECTS, returns a score estimating how much the background is changing across an array, as per BASHExtended. To estimate the background intensity, we generate an error image using the median filter (i.e. generateE with method = "median" and bgfilter = "median"). We divide the variance of this by the variance of an error image without using the median filter, to obtain our extended score.
第三，扩展的缺陷，估计多少的背景正在改变整个数组返回一个得分，每BASHExtended。要估计的背景强度，我们产生一个错误使用中值滤波的图像（即generateEmethod = "median"和bgfilter = "median"）。我们划分这个错误图像的方差方差不使用中值滤波，以获得我们扩展的得分。
It should be noted that to avoid repeated computation of distance, a "neighbours" matrix is used in the analysis. This matrix describes which beads are close to other beads. If a large number of beads are missing (for example, if beads with ProbeID = 0 were discarded) then this algorithm may be affected.
应当指出的距离，以避免重复计算，“邻居”矩阵分析中使用。这个矩阵描述接近其他珠的珠。如果大量的珠子丢失（例如，如果被丢弃的珠子ProbeID = 0），那么这种算法可能会受到影响。
For more detailed descriptions of the algorithms, read the help files of the respective functions listed in "see also".
对于算法的更详细的说明，请阅读“又见”中列出了各自的职能，帮助文件。
值----------Value----------
The output is a list with four attributes:
输出的是一个具有四个属性的列表：
wts: A vector of weights for the matrix.
WTS：矩阵的权重向量。
ext: A vector of extended scores (null if the extended analysis was disabled).
分机：扩展分数的一个向量（null，如果被禁用的扩展分析）。
QC: A summary of the extended score and the number of beads masked.
质量控制：一个扩展的得分和屏蔽珠的总结。
call: The function you used to call BASH.
检测：您使用的功能调用的BASH。
作者（S）----------Author(s)----------
Jonathan Cairns
参考文献----------References----------
参见----------See Also----------
BASHCompact, BASHDiffuse, BASHExtended, generateNeighbours, HULK
BASHCompact，BASHDiffuse，BASHExtended，generateNeighbours，HULK
举例----------Examples----------
## Not run: [＃无法运行：]
if(require(beadarrayExampleData)){
data(exampleBLData)
output <- BASH(exampleBLData,array=1:2,useLocs=FALSE)
boxplot(output$ext) #view spread of extended scores[查看蔓延扩展分数]
for(i in 1:2)
{
exampleBLData <- setWeights(exampleBLData, output$wts[[i]], i) #apply BASH weights to exampleBLData[BASH权申请到exampleBLData]
}
#diffuse test is stricter[弥漫性测试是严格]
output <- BASH(exampleBLData, diffsig = 0.00001,array=1, useLocs=FALSE)
#more outliers on the error image are used in the diffuse analysis[更离群上的错误形象，在弥漫性分析]
output <- BASH(exampleBLData, diffn = 2,array=1, useLocs=FALSE)
#only perform compact & diffuse analyses (we will only get weights)[只有执行紧凑型及弥漫性分析（我们将只得到权重）]
output <- BASH(exampleBLData, extended = FALSE,array=1, useLocs=FALSE)
#attempt to correct for background.[尝试更正为背景。]
output <- BASH(exampleBLData, bgcorr = "median",array=1, useLocs=FALSE)
}
else{
stop("You will need the beadarrayExampleData package to run this example")
}
## End(Not run)[＃结束（不运行）]
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 12:38:54
