multiscan(multiscan)
multiscan()所属R语言包：multiscan
Combining multiple laser scans of microarray data
结合微阵列数据的多重激光扫描
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates gene expressions from multiple laser scans of microarrays using non-linear functional regression model with additive plus multiplicative errors.
估计使用添加剂加乘错误的非功能的线性回归模型的多芯片激光扫描的基因表达。
用法----------Usage----------
multiscan(data, initial = NULL, na.rm = TRUE, verbose = FALSE, control = list())
参数----------Arguments----------
参数：data
A numeric matrix or data frame containing the intensity data of a single microarray scanned at multiple (two or more) scanner settings. For dual channel arrays, multiscan should be run on each channel of data separately. The number of rows (n) is equal to the number of spots/probes on the array, and the number of columns (m) equals the number of scans. Columns will be arranged in order of scanner's sensitivity before fitting the model. Replicated probes on the array are treated as individual spots.
一个数字矩阵或数据框包含多个（两个或两个以上）的扫描仪设置扫描单一芯片的强度数据。对于双通道阵列，multiscan应运行对每个数据通道分开。 n的行数（）点/探针阵列上的数，列数（等于m）等于扫描的数量。列将被安排在扫描仪的灵敏度为了拟合模型。阵列上复制的探针被视为个别景点。
参数：initial
A vector of length m+2 to be used as initial values for the scanning effects (β_2, \cdots, β_m) and scale (σ_1, σ_2, ν) parameters. If it is NULL (default), the initial values are determined from the data.
一个向量的长度m+2被用作扫描效果(β_2, \cdots, β_m)和规模(σ_1, σ_2, ν)参数的初始值。如果是的话NULL（默认），初始值确定data。
参数：na.rm
Logical. Should missing values be removed? Defaults to TRUE.
逻辑。应被删除缺失值吗？ TRUE默认。
参数：verbose
Logical. If TRUE, some intermediate results are printed as the iteration proceeds.
逻辑。如果TRUE，一些中间结果打印作为迭代收益。
参数：control
A list of control parameters. See "Details".
控制参数的列表。见“详细资料”。
Details
详情----------Details----------
The function implements the method of Khondoker et. al. (2006) for combining multiple laser scans of microarrays. This function is computationally slow and memory-intensive. That is due to the nested iteration loops of the numerical optimization of the likelihood function involving a large number (n+m+2) of parameters. The optimization uses an alternating algorithm with the Nelder-Mead simplex method (Nelder and Mead, 1965) in the inner loops. The function multiscan directly uses the C function nmmin, the internal code used in the general-purpose optimization tool optim, for implementing the Nelder-Mead simplex method. For large data sets with many tens of thousands of probes, it is recommended to consider first fitting the model using a random subset (e.g. 10,000 rows) of the data matrix, and then using the estimated scanning effects and scale parameters obtained as initial values for fitting the model to the full data set.
功能实现Khondoker等的方法。等。 （2006年）相结合的多个芯片激光扫描。此功能是计算速度慢和内存密集型。这是由于涉及大量的参数（n+m+2）的似然函数的数值优化的嵌套迭代循环。优化使用内尔德-Mead单纯形法在内环内尔德和米德（1965）的交替算法。 multiscan直接使用C函数的功能nmmin，通用优化工具使用的内部代码optim，实施内尔德Mead单纯形法。对于大型数据集许多几十成千上万的探针，建议首先考虑的拟合模型，使用一个随机子集（如10,000行）的数据矩阵，然后利用估计的扫描效果和尺度参数为初始值拟合模型，完整的数据集。
The control is a list of arguments. The users can change/supply any of the following components:
control是一个参数列表。用户可以改变/提供任何以下组件：
trace Indicator (0 or 1) of tracing information of Nelder-Mead algorithm. If 1, tracing information on the progress of the optimization is produced. Because Nelder-Mead may be callled thousands of times during the estimation process, setting trace = 1 will print too much
trace指标跟踪信息内尔德米德算法（0或1）。如果1，优化进度的跟踪信息产生的。因为内尔德米德可在估计过程中callled上千次，设置trace = 1将打印太多
gmaxit The maximum number of global iterations. Defaults to 150.
gmaxit全球迭代的最大数量。 150默认。
maxit The maximum number of Nelder-Mead iterations.
maxit内尔德-米德迭代的最大数量。
reltol Relative convergence tolerance of Nelder-Mead. The algorithm stops if it is unable to reduce the value by a factor of
reltol内尔德-米德相对收敛公差。该算法停止，如果它是无法减少的一个因素值
globaltol Convergence tolerance of the outer (alternating) iteration. The estimation process converges if the gain in loglikelihood from one complete cycle
globaltol外（交替）迭代收敛性。估计过程收敛，如果从一个完整的周期增益loglikelihood
alpha, beta, gamma Scaling parameters for the Nelder-Mead method. alpha is the reflection factor (default 1.0), beta the contraction factor (0.5) and
alpha，beta，gamma内尔德米德方法缩放参数。 alpha是反射系数（默认为1.0），beta收缩因子（0.5）
值----------Value----------
Returns an object of class multiscan with components
返回一个对象类multiscan组件
参数：call
The call of the multiscan function.
multiscan函数调用。
参数：beta
A vector of length m containing the maximum likelihood estimates of the scanning effects, the first component fixed at 1.
一个向量的长度m包含扫描效果的最大似然估计，第一部分固定在1。
参数：scale
A vector of length 3 containing the maximum likelihood estimates of the scale parameters σ_1, σ_2, \mbox{and} ν.
3包含了σ_1, σ_2, \mbox{and} ν尺度参数的最大似然估计的长度的向量。
参数：mu
A vector of length n containing the estimated gene expressions.
长度的向量n估计包含的基因表达。
参数：data
A matrix of the input data with columns rearranged in order of scanner's sensitivity.
在扫描仪的灵敏度的顺序重新排列列输入数据矩阵。
参数：fitted
A matrix of the fitted model on the data.
的上data的拟合模型的矩阵。
参数：sdres
A matrix of the standardised residuals.
标准化残差矩阵。
参数：outerit
Number of global iterations completed.
全球迭代完成。
参数：gconv, conv, convmu
Integer convergence codes.
整数收敛代码。
gconvIndicator of global convergence. 0 indicates successful convergence, 1 indicates premature termination.
gconv全局收敛性的指标。 0表示成功收敛，1表示提前终止。
convConvergence codes for the Nelder-Mead simplex method in the last global iteration while updating scanning effects and scale parameters. 0 for successful convergence, 1 indicates that the iteration limit maxit had been reached, 10 indicates degeneracy of the Nelder-Mead simplex method.
conv内尔德-Mead单纯形法在去年全球迭代收敛代码，同时更新扫描效果和尺度参数。 0成功收敛，1表明，迭代限制maxit已经达成，10表示内尔德-Mead单纯形法的简。
convmuConvergence codes for the Nelder-Mead simplex method in the last global iteration while updating the gene expression parameters. This is an integer vector of length n where each component takes the value 0, 1, or 10 depending on whether the Nelder-Mead simplex method successfully converged, reached iteration limit maxit or produced degeneracy respectively while updating the corresponding gene expression parameter.
convmu内尔德Mead单纯形法，在去年全球迭代收敛代码，同时更新基因表达的参数。这是一个整数向量长度n其中每个组件的价值0，1或10取决于是否内尔德-Mead单纯形法成功地融合，达到迭代限制maxit或分别产生退化，同时更新相应的基因表达参数。
参数：<code>outerit</code>
Number of global iterations completed.
全球迭代完成。
参数：<code>loglf</code>
Value of the loglikelihood function at convergence (gconv=0). NA if not converged (gconv=1).
值收敛loglikelihood功能（gconv=0）。 NA如果不融合（gconv=1）。
参考文献----------References----------
Statistical estimation of gene expression using multiple laser scans of microarrays. Bioinformatics 22, 215–219.
A simplex method for function minimization. The Computer Journal 7 308–313.
参见----------See Also----------
A web interface, created by David Nutter of Biomathematics & Statistics Scotland (BioSS), based on the original Fortran code written by Khondoker et al. (2006) is available at http://www.bioss.ac.uk/ktshowcase/create.cgi. Although it uses the same algorithm, results from the web interface may not be exactly identical to that of
Web界面，由大卫Nutter生物数学与统计苏格兰（BIOS中）的基础上，对原有的Fortran编写的代码由Khondoker等创建。 （2006年）是在http://www.bioss.ac.uk/ktshowcase/create.cgi提供。虽然它使用相同的算法，从Web界面的结果可能不完全相同的
举例----------Examples----------
## load the multiscan library [＃加载的MultiScan库]
library(multiscan)
## load the murine data set included in multiscan package[＃载入小鼠的数据集中包含的MultiScan包]
data(murine)
murine[1:10, ] ## see first few rows of data[＃看到前几行数据。]
## fit the model on murine data with default options[＃适合对小鼠数据模型与默认选项]
fit <- multiscan(murine)
fit
## plot the fitted model[＃绘制拟合模型]
plot(fit)
## get the estimated gene expressions[＃得到的估计的基因表达]
gene.exprs <- fit$mu
## see more details as iteration progresses[＃看到更多的细节，迭代进步]
fit1 <- multiscan(murine, verbose = TRUE)
fit1
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。
|
发表于 2012-2-26 07:37:14
