olin(OLIN)
olin()所属R语言包：OLIN
Optimised local intensity-dependent normalisation of two-colour microarrays
优化本地依赖强度两色芯片标准化
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This functions performs optimised local intensity-dependent normalisation (OLIN) and
此功能进行优化当地依赖强度标准化（奥林）和
用法----------Usage----------
scale=c(0.05,0.1,0.5,1,2,10,20),OSLIN=FALSE,weights=NA,
参数----------Arguments----------
参数：object
object of class “marrayRaw” or “marrayNorm” corresponding to a single array or a batch of arrays.
类“marrayRaw”或的“marrayNorm”对应的单个数组或数组的一批对象。
参数：X
matrix with x-coordinates of spots of the arrays in object. Each column includes the x-coodinates for the spots of one array. If X=NA, columns on array are used as proxies for the location in x-direction
矩阵与X-在object阵列点的坐标。每一列包含一个数组点的X-coodinates。如果X =不适用，对数组的列作为代理，在x方向的位置
参数：Y
matrix with y-coordinates of spots. Each column includes the y-coodinates for the spots of one array.If Y=NA, rows on array are used as proxies for the location in y-direction
矩阵与y坐标的点。每一列包含一个array.IfŸ=无斑点的Y-coodinates，对数组的行作为代理，在y方向的位置
参数：alpha
vector of alpha parameters that are tested in the GCV procedure
在GCV的过程中测试矢量的α参数
参数：iter
number of iterations in the OLIN procedure
在奥林过程的迭代次数
参数：scale
vector of scale parameters that are tested in a GCV procedure for spatial regression. This define the amount of smoothing in X-direction with respect to smoothing in Y-direction.
尺度参数的测试，在GCV的空间回归过程的向量。这个定义在Y方向的平滑滤波在X方向的金额。
参数：OSLIN
If OSLIN=TRUE, subsequent scaling of the range of M accross the array is performed.
如果OSLIN = TRUE，缩放随后并购accross的范围进行阵列。
参数：weights
matrix of (non-negative) weights for local regression (see locfit). Rows correspond to the spotted probe sequences, columns to arrays in the batch. If the weight of the corresponding spot equals zero, the spot is not used in the normalisation procedures (except the genepix argument is set to TRUE.) If the weight matrix include negative values, these will be set to zero. These weight matrix may be derived from the matrix of spot quality weights as defined for “maRaw” objects (weights=maW(object). Weights can be also used if the normalisation should be based on a set of selected genes that are assumed to be not differentially expressed.
局部回归（非负）的权重矩阵（见locfit）。行对应的斑点探针序列，列在批次阵列。如果相应的现货重量为零，当场的标准化过程中不使用（除genepix）。如果权重矩阵包括负值TRUE参数设置，这些设置到零。可能源自这些权重矩阵的定义为“maRaw”对象（weights=maW(object)。重量也可用于标准化应根据一组选定的基因被假定为现场质量权重矩阵没有差异表达。
参数：genepix
If genepix is set to TRUE, spot weights equal zero or larger are set to one for the local regression whereas negative spot with negative weights are not used for the regression. The argument genepix should be set to TRUE, if weights=maW(object) is set and spot quality weights derived by GenePix are stored in maW(object).
如果genepix设置为TRUE，现货权重为零或更大局部回归而负负重量当场无法回归。应设置为的说法genepix TRUE如果weights=maW(object)设置和现场质量GenePix派生的权重存放在maW(object)。
参数：bg.corr
backcorrection method (for “marrayRaw” objects) : “none”, “subtract”, “half”, “minimum”, “movingmin”, “edwards” or “normexp”.
backcorrection法（“marrayRaw”对象）：“无”，“减”，“一半”，“最低”，“movingmin”，“爱德华兹”或“normexp”。
参数：...
Further arguments for locfit function.
locfit功能的进一步论据。
Details
详情----------Details----------
OLIN and OSLIN are based on iterative local regression and incorporate optimisation of model parameters. Local regression is performed using LOCFIT, which requires the user to choose a specific smoothing parameter alpha that controls the neighbourhood size h of local fitting. The parameter alpha specifies the fraction of points that are included in the neighbourhood and thus has a value between 0 and 1. Larger alpha values lead to smoother fits. Additionally, the setting of scale parameters controls for distinct amount of smoothing in Y-direction compared to smoothing in X-direction. The parameter scale can be of arbitrary value. The choice of model parameters alpha and scale for local regression is crucial for the efficiency and quality of normalization. To optimize the model parameters, a general cross-validation procedure (GCV) is applied. The arguments alpha and scale define the parameters values which are tested in the GCV. OSLIN comprises the OLIN procedure with a subsequent optimized scaling of the range of logged intensity ratios across the spatial dimensions of the array. Details concerning the background correction methods can be found in the help page for backgroundCorrect2.
Olin和OSLIN基于迭代局部回归和整合优化模型参数。使用LOCFIT，这就要求用户选择一个特定的平滑参数alpha控制当地拟合的邻里长h进行局部回归。 alpha参数指定点附近的一小部分，因而具有0和1之间的值。较大的alpha值导致更流畅的配合。此外，设置不同的金额在Y方向平滑尺度参数控制相比，在X方向的平滑。 scale参数可以是任意值。模型参数的选择alpha和scale是局部回归标准化的效率和质量的关键。要优化模型参数，一般的交叉验证程序（GCV），被应用。论据alpha和scale定义的参数值，在GCV的测试。 OSLIN包括随后整个数组的空间维度记录的强度比范围内的优化缩放的奥林过程。有关背景校正方法的细节，可以发现在backgroundCorrect2的帮助页面。
Detailed information about OLIN and OSLIN can be found in the package documentation and in the reference stated below. The weights argument specifies the influence of the single spots on the local regression. To exclude spots being used for the local regression (such as control spots), set their corresponding weight to zero. Note that OLIN and OSLIN are based on the assumptions that most genes are not differentially expressed (or up- and down-regulation is balanced) and that genes are randomly spotted across the array. If these assumptions are not valid, local regression can lead to an underestimation of differential expression. OSLIN is especially sensitive to violations of these assumptions. However, this sensitivity can be decreased if the minimal alpha-value is increased. Minimal alpha defines the smallest scale used for local regression. Increasing alpha can reduce the influence of localised artifacts as a larger fraction of data points is included. Alternative normalisation functions such as oin, lin and ino might also be used for a more conservative fit.
奥林和OSLIN有关的详细信息，可以发现在包文件，并在下文所述的参考。权重参数指定的单点局部回归的影响。为了排除局部回归（如控制点）的点，其相应的权重设置为零。请注意，奥林和OSLIN上，大部分基因差异表达的假设（或向上和向下调节是平衡的）和基因为基础的随机发现整个阵列。如果这些假设不正确，可导致局部回归到差异表达的低估。 OSLIN是特别敏感，违反这些假设。然而，这种敏感性可以减少最小alpha值增加。最小的alpha定义用于局部回归的最小规模。包括增加alpha可以降低本地化文物的影响，作为一个较大的数据点的分数。替代标准化的功能，如oin，lin和ino也可能会使用较为保守的合适。
If the normalisation should be based on set of genes assumed to be not differentially expressed (house-keeping genes), weights can be used for local regression. In this case, all weights are set to zero except for the house-keeping genes for which weights are set to one. In order to achieve a reliable regression, it is important, however, that there is a sufficient number of house-keeping genes that are distributed over the whole expression range and spotted accross the whole array.
如果标准化应根据假定不差异表达（看家基因）的基因组，重量可用于局部回归。在这种情况下，所有的重量都设置为零，除了看家基因为权重设置一个。为了实现可靠的回归，它是重要的，但是，有足够数量的看家基因分布在整个表达的范围和斑点accross整个阵列。
It is also important to note that OLIN/OSLIN is fairly efficient in removing intensity- and spatial-dependent dye bias, so that normalised data will look quite “good” after normalisation independently of the true underlying data quality. Normalisation by local regression assumes smoothness of bias. Therefore, localised artifacts such as scratches, edge effects or bubbles should be avoided. Spots of these areas should be flagged (before normalisation is applied) to ensure data integrity. To stringently detect artifacts, the OLIN functions fdr.int, fdr.spatial, p.int and p.spatial can be used.
同样重要的是要注意，奥林/ OSLIN是相当有效的清除力度和空间依赖的染料偏见，因此，规范化的数据看起来相当“好”关系标准化之后独立的真实的基础数据的质量。局部回归标准化假设平滑的偏见。因此，本地化的文物，如划痕，边缘效应或气泡，应尽量避免。这些区域的景点应该是标记（适用于标准化之前），以确保数据的完整性。要严格检测工件，奥林功能fdr.int, fdr.spatial, p.int和p.spatial可以使用。
值----------Value----------
Object of class “marrayNorm” with normalised logged ratios
对象类的“marrayNorm”归登录比率
作者（S）----------Author(s)----------
Matthias E. Futschik (<a href="http://itb.biologie.hu-berlin.de/~futschik">http://itb.biologie.hu-berlin.de/~futschik</a>)
参考文献----------References----------
<STRONG>Genome Biology</STRONG>, 5:R60
normalization, visualization and quality testing for two-channel microarray data, <STRONG>Bioinformatics</STRONG>, 21(8):1724-6
参见----------See Also----------
maNorm, locfit, gcv, oin,
maNorm，locfit，gcv，oin
举例----------Examples----------
# LOADING DATA[加载数据]
data(sw)
data(sw.xy)
# OPTIMISED LOCAL INTENSITY-DEPENDENT NORMALISATION OF FIRST ARRAY[优化局部依赖强度标准化，第一个数组]
norm.olin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1])
# MA-PLOT OF NORMALISATION RESULTS OF FIRST ARRAY[主图标准化的第一个数组结果]
plot(maA(norm.olin),maM(norm.olin),main="OLIN")
# CORRESPONDING MXY-PLOT[通讯MXY-图]
mxy.plot(maM(norm.olin)[,1],Ngc=maNgc(norm.olin),Ngr=maNgr(norm.olin),
Nsc=maNsc(norm.olin),Nsr=maNsr(norm.olin),main="OLIN")
# OPTIMISED SCALED LOCAL INTENSITY-DEPENDENT NORMALISATION[优化规模的局部强度依赖标准化]
norm.oslin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1],OSLIN=TRUE)
# MA-PLOT[MA - 图]
plot(maA(norm.oslin),maM(norm.oslin),main="OSLIN")
# MXY-PLOT[MXY-图]
mxy.plot(maM(norm.oslin)[,1],Ngc=maNgc(norm.oslin),Ngr=maNgr(norm.oslin),
Nsc=maNsc(norm.oslin),Nsr=maNsr(norm.oslin),main="OSLIN")
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。
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发表于 2012-2-26 08:21:41
