robloxbioc(RobLoxBioC)
robloxbioc()所属R语言包：RobLoxBioC
Generic Function for Preprocessing Biological Data
预处理生物数据的通用功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generic function for preprocessing biological data using optimally robust (rmx) estimators; confer Rieder (1994), Kohl (2005), Rieder et al (2008).
生物数据的预处理使用最佳的鲁棒性（RMX）的估计;赋予里德尔（1994），科尔（2005年），里德尔等人（2008）的通用功能。
用法----------Usage----------
robloxbioc(x, ...)
## S4 method for signature 'matrix'
robloxbioc(x, eps = NULL, eps.lower = 0, eps.upper = 0.05, steps = 3L,
fsCor = TRUE, mad0 = 1e-4)
## S4 method for signature 'AffyBatch'
robloxbioc(x, bg.correct = TRUE, pmcorrect = TRUE, normalize = FALSE,
add.constant = 32, verbose = TRUE, eps = NULL,
eps.lower = 0, eps.upper = 0.05, steps = 3L, fsCor = TRUE,
mad0 = 1e-4, contrast.tau = 0.03, scale.tau = 10,
delta = 2^(-20), sc = 500)
## S4 method for signature 'BeadLevelList'
robloxbioc(x, log = TRUE, imagesPerArray = 1, what = "G", probes = NULL,
arrays = NULL, eps = NULL, eps.lower = 0, eps.upper = 0.05,
steps = 3L, fsCor = TRUE, mad0 = 1e-4)
参数----------Arguments----------
参数：x
biological data.
生物数据。
参数：...
additional parameters.
附加参数。
参数：eps
positive real (0 < eps <= 0.5): amount of gross errors. See details below.
正实数（0 <eps<= 0.5）：量的严重错误。详见下文。
参数：eps.lower
positive real (0 <= eps.lower <= eps.upper): lower bound for the amount of gross errors. See details below.
正实（0 <=eps.lower<=eps.upper）：下限量的严重错误。详见下文。
参数：eps.upper
positive real (eps.lower <= eps.upper <= 0.5): upper bound for the amount of gross errors. See details below.
正实（eps.lower<=eps.upper<= 0.5）：上界为量的严重错误。详见下文。
参数：steps
positive integer. k-step is used to compute the optimally robust estimator.
正整数。 k步被用于计算最优鲁棒估计。
参数：fsCor
logical: perform finite-sample correction. See function finiteSampleCorrection.
逻辑：执行有限样本校正。请参阅功能finiteSampleCorrection。
参数：mad0
scale estimate used if computed MAD is equal to zero
使用的规模估计，如果计算MAD等于零
参数：bg.correct
if TRUE MAS 5.0 background correction is performed; confer bg.correct.mas.
如果TRUEMAS 5.0进行背景校正;赋予bg.correct.mas。
参数：pmcorrect
method used for PM correction; TRUE calls an algorithm which is comparable to the algorithm of MAS 5.0; confer pmcorrect.mas. If FALSE only the PM intensities are used.
方法用于PM改正; TRUE调用的算法相比，该算法的MAS 5.0;赋予pmcorrect.mas。如果FALSEPM强度使用。
参数：normalize
logical: if TRUE, Affymetrix scale normalization is performed.
逻辑：如果TRUE，Affymetrix公司的规模标准化。
参数：add.constant
constant added to the MAS 5.0 expression values before the normalization step. Improves the variance of the measure one no longer devides by numbers close to 0 when computing fold-changes.
不变的MAS 5.0表达式的值，然后归一化步骤。提高的措施1的方差不再devides计算倍变化时，由数字接近0。
参数：verbose
logical: if TRUE, some messages are printed.
逻辑：如果TRUE，一些消息被打印出来。
参数：contrast.tau
a number denoting the contrast tau parameter; confer the MAS 5.0 PM correction algorithm.
一些表示的对比度头参数赋予MAS下午5时校正算法。
参数：scale.tau
a number denoting the scale tau parameter; confer the MAS 5.0 PM correction algorithm.
一个数字，表示规模的头参数赋予MAS下午5时校正算法。
参数：delta
a number denoting the delta parameter; confer the MAS 5.0 PM correction algorithm.
数表示的Delta的参数;赋予的的MAS下午5点校正算法。
参数：sc
value at which all arrays will be scaled to.
所有的数组值将被缩小。
参数：log
if TRUE, then the log2 intensities for each bead-type are summarized.
如果TRUE，然后LOG2强度为每一个珠式总结。
参数：imagesPerArray
Specifies how many images (strips) there are per array. Normally 1 for a SAM and 1 or 2 for a BeadChip. The images (strips) from the same array will be combined so that each column in the output represents a sample.
指定每个阵列有多少图像（条）。通常情况下的SAM和1或2的BeadChip。结合起来，使在输出中的每一列代表一个样本的图像（测试条）从相同的数组。
参数：what
character string specifying which intensities/values to summarize. See getArrayData for a list of possibilities.
字符串指定的强度/值总结。见getArrayData的列表可能性。
参数：probes
Specify particular probes to summarize. If left NULL then all the probes on the first array are used.
指定特定的探针来概括。如果非NULL然后第一个阵列上的所有探针。
参数：arrays
integer (scalar or vector) specifying the strips/arrays to summarize. If NULL, then all strips/arrays are summarized.
整数（标量或矢量）指定条/阵列总结。如果NULL，那么所有带/阵列总结。
Details
详细信息----------Details----------
The optimally-robust resp. the radius-minimax (rmx) estimator for normal location and scale is used to preprocess biological data. The computation uses a k-step construction with median and MAD as starting estimators; cf. Rieder (1994) and Kohl (2005).
最佳强大的RESP账户。的半径极大极小（RMX）估计的正常位置和规模用于进行预处理的生物数据。的计算采用一个k步建设，中位数和MAD开始估计;比照。里德尔（1994）和科尔（2005年）。
If the amount of gross errors (contamination) is known, it can be specified by eps. The radius of the corresponding infinitesimal contamination neighborhood (infinitesimal version of Tukey's gross error model) is obtained by multiplying eps by the square root of the sample size.
如果严重的错误（污染）的量是已知的，它可以指定eps。相应的无穷小的污染附近的半径（无穷版本，Tukey的严重错误模式）乘以eps的样本规模的平方根。
If the amount of gross errors (contamination) is unknown, which is typically the case, try to find a rough estimate for the amount of gross errors, such that it lies between eps.lower and eps.upper.
如果严重的错误（污染）是未知的，这是通常的情况下，试图找到一个粗略的估计量的严重错误，它位于之间eps.lower和eps.upper。
If eps is NULL, the radius-minimax (rmx) estimator in sense of Rieder et al. (2001, 2008), respectively Section 2.2 of Kohl (2005) is used.
如果eps是NULL，半径极小极大（RMX）估计的意义，Rieder等人。 （2001年，2008年），科尔（2005年）第2.2节。
The algorithm used for Affymetrix data is similar to MAS 5.0 (cf. Affymetrix (2002)). The main difference is the substitution of the Tukey one-step estimator by our rmx k-step (k >= 1) estimator in the PM/MM correction step. The optional scale normalization is performed as given in Affymetrix (2002).
Affymetrix数据所使用的算法是类似于MAS 5.0（参见Affymetrix公司（2002年））。主要的区别是由我们的RMX的k-步骤（k> = 1）中的PM / MM校正步骤估计替代杜克一个步骤估计。可选的规模进行归一化，Affymetrix公司（2002）中给出。
In case of Illumina data, the rmx estimator is used to summarize the bead types. The implementation for the most part was taken from function createBeadSummaryData.
Illumina的数据的情况下，RMX估计是用来总结了珠类型。在大多数情况下的实施是取自函数createBeadSummaryData。
For sample size <= 2, median and MAD are used for estimation.
样本大小<= 2，中位数和MAD用于估计。
If eps = 0, mean and sd are computed.
如果eps = 0，均值和方差的计算。
值----------Value----------
Return value depends on the class of x. In case of "matrix" a matrix with columns "mean" and "sd" is returned. In case of "AffyBatch" an object of class "ExpressionSet" is returned.
返回值取决于类的x。 "matrix"矩阵列“的意思是”和“SD”的情况下，被返回。的情况下，"AffyBatch"对象的类"ExpressionSet"返回。
（作者）----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>
参考文献----------References----------
Affymetrix, Santa Clara.
Bayreuth: Dissertation.
the Radius. Statistical Methods and Applications 17(1) 13-40.
the Radius. Submitted. Appeared as discussion paper Nr. 81. SFB 373 (Quantification and Simulation of Economic Processes), Humboldt University, Berlin; also available under www.uni-bayreuth.de/departments/math/org/mathe7/RIEDER/pubs/RR.pdf
参见----------See Also----------
roblox, rowRoblox, AffyBatch-class, generateExprVal.method.mas, ExpressionSet-class,
roblox，rowRoblox，AffyBatch-class，generateExprVal.method.mas，ExpressionSet-class，
实例----------Examples----------
set.seed(123) # to have reproducible results for package checking[有重复性的结果包检查]
## similar to rowRoblox of package RobLox[＃类似rowRoblox包ROBLOX。]
ind <- rbinom(200, size=1, prob=0.05)
X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2)
robloxbioc(X)
robloxbioc(X, steps = 5)
robloxbioc(X, eps = 0.05)
robloxbioc(X, eps = 0.05, steps = 5)
## the function is designed for large scale problems[＃功能是专为大规模问题]
X <- matrix(rnorm(50000*20, mean = 1), nrow = 50000)
system.time(robloxbioc(X))
## using Affymetrix-Data[＃利用Affymetrix数据]
## confer example to generateExprVal.method.mas[＃赋予的例子来generateExprVal.method.mas]
## A more worked out example can be found in the scripts folder[＃A的工作的例子可以发现在脚本文件夹]
## of the package.[＃的包。]
data(SpikeIn)
probes <- pm(SpikeIn)
mas <- generateExprVal.method.mas(probes)
rl <- 2^robloxbioc(log2(t(probes)))
concentrations <- as.numeric(colnames(SpikeIn))
plot(concentrations, mas$exprs, log="xy", ylim=c(50,10000), type="b",
ylab = "expression measures")
points(concentrations, rl[,1], pch = 20, col="orange", type="b")
legend("topleft", c("MAS", "roblox"), pch = c(1, 20))
## Not run: [＃不运行：]
## "Not run" just because of computation time[“无法运行”，只是因为计算时间]
require(affydata)
data(Dilution)
eset <- robloxbioc(Dilution)
## Affymetrix scale normalization[＃Affymetrix公司的规模标准化]
eset1 <- robloxbioc(Dilution, normalize = TRUE)
## End(Not run)[＃（不执行）]
## using Illumina-Data[＃利用Illumina数据]
## Not run: [＃不运行：]
## "Not run" just because of computation time[“无法运行”，只是因为计算时间]
data(BLData)
BSData <- robloxbioc(BLData, eps.upper = 0.5)
## End(Not run)[＃（不执行）]
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。
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发表于 2012-9-27 21:06:02
