selectFeaturesSlimPLS(SlimPLS)
selectFeaturesSlimPLS()所属R语言包：SlimPLS
Selects and constructs a set of features using the SlimPLS algorithm methods from a given expression matrix
选择并构建了一套功能使用SlimPLS算法从一个给定的表达矩阵的方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function constructs a small number of features, each of them is a linear combination of basic features from the given expression matrix with respect to a given labeling of the samples. The new features extracted can later be used for reducing the dimension of an expression matrix in order to learn a classification model more efficiently. While learning, either the extracted features (linear combinations, aka components) which are output of this function, or merely the list of features that are members of any component can be used.
函数构造一个小数量的功能，其中每一个是从给定的相对于一个给定的标记的样品的表达矩阵的基本功能的一个线性组合。新的特征提取后可用于降维的表达矩阵，以更有效地学习一个分类模型。在学习过程中，无论是提取的特征（线性组合，也称为组件）是这个函数的输出，或者仅仅是列表中的成员的任何组件的功能都可以使用。
用法----------Usage----------
selectFeaturesSlimPLS(exp_mat, num_class_a=0, num_class_b=0, class_a="",
class_b="", num_features, component_size, p_value_threshold)
参数----------Arguments----------
参数：exp_mat
exp_mat is an expression matrix of type expMat, usually created by reading an expression matrix from a file using readExpMat. The matrix is supposed to have all samples belonging to class 1 grouped together in the first columns, following all samples from class 2 grouped together as well.
exp_mat是一个表达式，矩阵型expMat，通常创建从文件读取表达矩阵readExpMat。矩阵应该有属于组合在一起，在第一列的第1类，2类组合在一起，并从所有样品所有样品。
参数：num_class_a
num_class_a is the number of samples belonging to class 1
num_class_a是属于1类的样本数
参数：num_class_b
num_class_b is the number of samples belonging to class 2
num_class_b是属于第2级的数目的样本
参数：class_a
class_a is the class label of class a. May be used if the expression matrix has labels in its second row.
class_a是一类类的标签。表达矩阵可以使用，如果在第二行有标签。
参数：class_b
class_b is the class label of class b. May be used if the expression matrix has labels in its second row.
class_b是类B类的标签。表达矩阵可以使用，如果在第二行有标签。
参数：num_features
The maximal number of basic features that will be selected
基本功能，将被选择的最大数目的
参数：component_size
The number of features in each constructed component.
功能，在每个构造的组件的数量。
参数：p_value_threshold
An optional p-value threshold on the features selected. If 0, this parameter is ignored. If not, only features below the given p-value will be selected.
可选的p值阈值选择的功能。如果为0时，此参数将被忽略。如果没有，只有以下功能的给定的p-值将被选中。
Details
详细信息----------Details----------
The function selects up to num_features basic features, grouped together as a linear combination of them in components of the given component_size. If p_value_threshold is not 0, it selects only features with a p-value lower than the given threshold. See references for method details. The selection is supervised, based on two classes, so the user must supply a class parameter for every sample. This is done by providing a sorted matrix, where all the samples from class a precede all the samples from class b. In addition to the sorted matrix, the user must provide the number of samples from class a and b. Alternatively the user may provide an unsorted matrix, with labels in its second row, denoting the class of each sample. In this case the user must also provide two labels to the selection function - one for class a and one for class b.
功能选择num_features基本功能，组合在一起作为它们的线性组合的元件的给定的component_size。如果p_value_threshold是不是0，则仅选择的功能与p值低于给定的阈值。方法的详细内容见参考文献。的选择负责监督，两班的基础上，因此，用户必须为每个样品提供一类参数。这是提供一个排序矩阵，其中所有样品从A类之前所有的样品B类。除了排序条件矩阵时，用户必须提供从类a和b的样本的数目。另外，用户可以提供一个未排序的矩阵，在其第二行中的标签，表示各样品的类。在这种情况下，用户也必须提供两个标签，以选择功能 - 类A和一个B类。
值----------Value----------
The function returns an object of type featureSet, which holds the selected features, and can later be used for learning a classification model by trainClassifier. The object holds both the features, and their weighted grouping into components. Each component is a linear combination of basic features.
该函数返回一个类型的对象featureSet，持有选定的功能，并可以在以后用于学习一个分类模型trainClassifier。对象持有的功能，其加权分组到组件。每个组件是一个线性组合的基本功能。
（作者）----------Author(s)----------
Michael Gutkin
参考文献----------References----------
1. Gutkin, M, MSc thesis, April 2008, Feature selection methods for classification of gene expression profiles, Tel Aviv Univetsity, Tel Aviv.
2. Gutkin, M., Shamir, R., Dror, G., 2009 SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification. PLoS ONE 4(7): e6416. doi:10.1371/journal.pone.0006416
参见----------See Also----------
selectFeatures, trainClassifier,getClassification,readExpMat
selectFeatures，trainClassifier，getClassification，readExpMat
实例----------Examples----------
# reads an expression matrix with no class labels into exp_mat[到exp_mat没有类标签读取表达矩阵]
## Not run: [＃不运行：]
exp_mat <- readExpMat("golub_leukemia_data_training.csv", FALSE)
## End(Not run)[＃（不执行）]
# selects a set of features into the features variable. The matrix we read is[选择一组功能的功能变数。矩阵，我们读的是]
# sorted by classes, and [排序类，]
# it is known to have 41 samples of class a and 20 samples of class b. The selection is[它是已知的有41个样品B类和第20类样品。该选择是]
# done using the SlimPLS method. Up to two components with 25 features in each component[做使用SlimPLS方法。多达两个组件在每个组件25的功能]
# will be selected.[将被选中。]
## Not run: [＃不运行：]
features <- selectFeaturesSlimPLS(exp_mat, num_class_a=41, num_class_b=20, class_a="",
class_b="", num_features=50, component_size=25,
p_value_threshold=0)
## End(Not run)[＃（不执行）]
# reads an expression matrix with class labels into exp_mat2[矩阵类标签读取表达exp_mat2]
## Not run: [＃不运行：]
exp_mat2 <- readExpMat("golub_leukemia_data_with_classes_training.csv", FALSE)
## End(Not run)[＃（不执行）]
# selects a set of features into the features2 variable. The matrix we read has a class[选择一套功能到features2变量。的矩阵，我们有一个类]
# label for each sample in its second row. Labels are either "AML" or "ALL". [每个样品在其第二行中的标签。标签是“反垄断法”或“ALL”。]
# Selection is done using the SlimPLS method. Up to two components with 25 features in[选择做使用SlimPLS方法。两部件25功能]
# each component will be selected.[每个组件将被选中。]
## Not run: [＃不运行：]
features2 <- selectFeaturesSlimPLS(exp_mat, class_a="AML", class_b="ALL",
num_features=50, component_size=25,
p_value_threshold=0)
## End(Not run)[＃（不执行）]
# the found list of features:[发现的功能列表：]
## Not run: [＃不运行：]
features2@features
## End(Not run)[＃（不执行）]
# the found components (weighted matrix for each feature and each component, of size[找到的组件（加权矩阵的每个功能，并且每个组件的大小]
# 2X50 - 25 non-zero feature weights for each feature:[2X50 - 25非零特征权重的每个功能：]
## Not run: [＃不运行：]
features2@w_mat
## 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。
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 2012-9-30 10:09:32
