screen(pint)
screen()所属R语言包：pint
Fits dependency models to chromosomal arm, chromosome or the whole genome.
适合染色体臂染色体或整个基因组的依赖模型。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits dependency models for whole chromosomal arm, chromosome or genome depending on arguments with chosen window size between two data sets.
适用于整个染色体臂染色体或基因组根据两个数据集之间的选择窗口大小参数的依赖模式。
用法----------Usage----------
screen.cgh.mrna(X, Y, windowSize = NULL, chromosome, arm, method =
"pSimCCA", params =
list(), max.dist = 1e7, outputType = "models", useSegmentedData =
TRUE, match.probes = TRUE, regularized = FALSE)
screen.cgh.mir(X, Y, windowSize, chromosome, arm, method = "", params = list(),
outputType = "models")
参数----------Arguments----------
参数：X,Y
Data sets. It is recommended to place gene/mirna expression data in X and copy number data in Y. Each is a list with the following items:
数据集。它被放置在X和Y中的拷贝数的数据，每一个都是与下列项目列表中的基因/ miRNA表达数据：
data Data in a matrix form. Genes are in rows and samples in columnss. e.g. gene copy number.
data数据矩阵形式。基因是在columnss行和样品。例如基因拷贝数。
info Data frame which contains following information about genes in data matrix.
info有关数据矩阵中的基因，其中包含以下信息的数据框。
chr Number indicating the chrosome for the gene: (1 to 24). Characters 'X' or 'Y' can be used also.
chr数说明基因chrosome：（1到24）。也可以使用字符“X”或“Y”。
arm Character indicating the chromosomal arm for the gene ('p' or 'q')
arm表明基因的染色体臂（P或q字符）
loc Location of the gene in base pairs. pint.data can be used to create data sets in this format.
loc碱基对的基因的位置。 pint.data可以用来创建这种格式的数据集。
参数：chromosome
Specify the chromosome for model fitting. If missing, whole genome is screened.
指定的模型拟合的染色体。如果失踪，全基因组筛选。
参数：arm
Specify chromosomal arm for model fitting. If missing, both arms are modeled.
模型拟合指定的染色体臂。如果缺少这两个武器为蓝本。
参数：windowSize
Determine the window size. This specifies the number of nearest genes to be included in the chromosomal window of the model, and therefore the scale of the investigated chromosomal region. If not specified, using the default ratio of 1/3 between features and samples or 15 if the ratio would be greater than 15
确定窗口的大小。这指定最近被包括在该模型的染色体窗口的基因数量，因此调查的染色体区域的规模。如果没有指定，使用功能和样品或之间的1/3的默认比例15如果这个比例将大于15
参数：method
Dependency screening can utilize any of the functions from the package dmt (at CRAN). Particular options include
依赖筛查可以利用从包DMT（CRAN上）的任何功能。特别选项包括
'pSimCCA'probabilistic similarity constrained canonical correlation analysis <CITE>Lahti et al. 2009</CITE>. This is the default method.
“pSimCCAprobabilistic相似制约典型相关分析<CITE>拉赫蒂等。 2009 </引用>。这是默认的方法。
'pCCA'probabilistic canonical correlation analysis <CITE>Bach & Jordan 2005</CITE>
pCCAprobabilistic典型相关分析<CITE>巴赫2005年与约旦</引用>
'pPCA'probabilistic principal component analysis <CITE>Tipping & Bishop 1999</CITE>
pPCAprobabilistic主成分分析<CITE>小费主教1999年</引用>
'pFA'probabilistic factor analysis <CITE>Rubin & Thayer 1982</CITE>
pFAprobabilistic因素的分析<CITE>鲁宾塞耶1982年</引用>
'TPriorpSimCCA'probabilistic similarity constrained canonical correlation analysis with possibility to tune T prior (Lahti et al. 2009) If anything else, the model is specified by the given parameters.
“TPriorpSimCCAprobabilistic相似性约束的可能性调整ţ前（拉提等人，2009年），如果别的，该模型是由给定的参数指定的典型相关分析。
参数：params
List of parameters for the dependency model.
依赖模型的参数列表。
sigmasVariance parameter for the matrix normal prior distribution of the transformation matrix T. This describes the deviation of T from H
sigmasVariance为正常的矩阵变换矩阵T.先验分布参数描述的T从H的偏差
HMean parameter for the matrix normal prior distribution prior of transformation matrix T
HMean前为基质的正常先验分布参数的变换矩阵T
zDimensionDimensionality of the latent variable
zDimensionDimensionality的潜变量
mySeedRandom seed.
mySeedRandom种子。
covLimitConvergence limit. Default depends on the selected method: 1e-3 for pSimCCA with full marginal covariances and 1e-6 for pSimCCA in other cases.
covLimitConvergence限制。默认值取决于所选择的方法：为pSimCCA 1E-3全边际协方差和1E pSimCCA的其他情况6。
参数：max.dist
Maximum allowed distance between probes. Used in automated matching of the probes between the two data sets based on chromosomal location information.
最大允许探针之间的距离。在染色体位置信息为基础的两个数据集之间的探针自动匹配使用。
参数：outputType
Specifies the output type of the function. possible values are "models" and "data.frame"
指定函数的输出类型。可能的值是"models"和"data.frame"
参数：useSegmentedData
Logical. Determines the useage of the method for segmented data
逻辑。确定分割数据的方法的巧用
参数：match.probes
To be used with segmented data, or nonmatched probes in general. Using nonmatched features (probes) between the data sets. Development feature, to be documented later.
要使用分割数据，或一般的nonmatched探针。使用nonmatched功能（探针）之间的数据集。发展的特点，记录。
参数：regularized
Regularization by nonnegativity constraints on the projections. Development feature, to be documented later.
预测非负约束的正规化。发展的特点，记录。
Details
详情----------Details----------
Function screen.cgh.mrna assumes that data is already paired. This can be done with pint.match. It takes sliding gene windows with fixed.window and fits dependency models to each window with fit.dependency.model function. If the window exceeds start or end location (last probe) in the chromosome in the fixed.window function, the last window containing the given probe and not exceeding the chromosomal boundaries is used. In practice, this means that dependency score for the last n/2 probes in each end of the chromosome (arm) will be calculated with an identical window, which gives identical scores for these end position probes. This is necessary since the window size has to be fixed to allow direct comparability of the dependency scores across chromosomal windows.
功能screen.cgh.mrna假设数据已经配对。这可以用pint.match。它采用滑动基因与fixed.window窗口和适合fit.dependency.model函数依赖模型的每一个窗口。如果窗口超过在染色体上的开始或结束位置（最后一个探针）fixed.window函数，最后一个窗口，其中包含给定的探针和不超过染色体的界限。在实践中，这意味着依赖得分为最后n / 2探针在每个染色体（臂）年底将有相同的窗口，它提供了相同的分数为这些最终地位探针计算。这是必要的，因为窗口的大小加以固定，使依赖跨染色体窗口的分数直接可比性。
Function screen.cgh.mir calculates dependencies around a chromosomal window in each sample in X; only one sample from X will be used. Data sets do not have to be of the same size andX can be considerably smaller. This is used with e.g. miRNA data.
功能screen.cgh.mir计算X染色体窗口围绕在每个样品的依赖;X只有一个样品将用于。不必是相同的大小和X可以是相当小的数据集。这是用来与如miRNA的数据。
If method name is specified, this overrides the corresponding model parameters, corresponding to the modeling assumptions of the specified model. Otherwise method for dependency models is determined by parameters.
如果指定方法名称，这将覆盖相应的模型参数，对应指定模型的建模假设。否则依赖模型的方法确定参数。
Dependency scores are plotted with dependency score plotting.
依赖分数依赖得分图策划。
值----------Value----------
The type of the return value is defined by the the function argument outputType.
返回值的类型被定义为函数的参数outputType。
With the argument outputType = "models", the return value depends on the other arguments; returns a ChromosomeModels which contains all the models for dependencies in chromosome or a GenomeModels which contains all the models for dependencies in genome.
与参数outputType = "models"，返回值取决于其他参数，返回一个包含所有染色体的依赖或GenomeModels其中包含所有的基因组中的依赖关系模型模型ChromosomeModels这。
With the argument outputType = "data.frame", the function returns a data frame with eachs row representing a dependency model for one gene. The columns are: geneName,dependencyScore,chr,arm,loc.
与参数outputType = "data.frame"，该函数返回一个eachs行数据框代表一个基因的依赖模型。栏目有：geneName，dependencyScore，chr，arm，loc。
作者（S）----------Author(s)----------
Olli-Pekka Huovilainen <a href="mailto huovila@gmail.com">ohuovila@gmail.com</a> and Leo Lahti <a href="mailto:leo.lahti@iki.fi">leo.lahti@iki.fi</a>
参考文献----------References----------
Proc. MLSP'09 IEEE International Workshop on Machine Learning for Signal Processing, See http://www.cis.hut.fi/lmlahti/publications/mlsp09_preprint.pdf
Francis R. and Jordan Michael I. 2005 Technical Report 688. Department of Statistics, University of California, Berkley. http://www.di.ens.fr/~fbach/probacca.pdf
Bishop Christopher M. 1999. Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611–622. http://research.microsoft.com/en-us/um/people/cmbishop/downloads/Bishop-PPCA-JRSS.pdf
D. 1982. Psychometrika, vol. 47, no. 1.
参见----------See Also----------
To fit a dependency model: fit.dependency.model. ChromosomeModels holds dependency models for chromosome, GenomeModels holds dependency models for genome. For plotting, see: dependency score plotting
为了适应一个依赖模型：fit.dependency.model。 ChromosomeModels持有染色体依赖模型，GenomeModels持有依赖模型的基因组。绘图，请参阅：依赖得分图
举例----------Examples----------
data(chromosome17)
## pSimCCA model on chromosome 17[17号染色体上的＃pSimCCA模型]
models17pSimCCA <- screen.cgh.mrna(geneExp, geneCopyNum,
windowSize = 10, chr = 17)
plot(models17pSimCCA)
## pCCA model on chromosome 17p with 3-dimensional latent variable z[3维潜变量z＃PCCA模型对染色体17p]
models17ppCCA <- screen.cgh.mrna(geneExp, geneCopyNum,
windowSize = 10,
chromosome = 17, arm = 'p',method="pCCA",
params = list(zDimension = 3))
plot(models17ppCCA)
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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 11:09:43
