R语言 rqubic包 quantileDiscretize()函数中文帮助文档(中英文对照)

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quantileDiscretize(rqubic)
quantileDiscretize()所属R语言包：rqubic

                                         Discretize expression matrix for qualitative biclustering
                                         离散表达矩阵定性双分群

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Performs recursive quantilizations on gene expression data across samples, to quantileDiscretize gene expression matrix. The quantile parameter q determines the estimated proportion of differentially expressed genes (2q as for both up- and down-regulatons). The rank parameter r determines how many discrete levels should differentially expressed genes (or outliers) have. See details below.
执行的递归quantilizations对样本之间的基因表达数据，，quantileDiscretize基因表达矩阵。位数参数q决定的差异表达基因的估计比重（无论是向上和向下regulatons为2Q）。排名参数r决定多少离散水平差异表达的基因（或离群值）。详见下文。


用法----------Usage----------


quantileDiscretize(x, ...)



参数----------Arguments----------

参数：x
It can be an object of the eSet class or inheriting it. The most commonly used form is an linkS4class{ExpressionSet} class. Alternatively, it can be a numeric matrix.
它可以是一个eSet类或继承它的对象。最常用的形式是一个linkS4class{ExpressionSet}类。另外，它可以是一个数字矩阵。


参数：...
Currently, the ... accepts two parameter: q and rank, explained below.
目前，...接受两个参数：q和rank，解释如下。


Details

详情----------Details----------

Parameter q corresponds to the command line option -q in the QUBIC command line tool, and the rank option corresponds to -r.
参数q对应的命令行选项-q在QUBIC命令行工具，的rank选项对应-r。

For each gene, the algorithm applies quantile discretization first to divide conditions into negative (lower), un-changed and positive (higher) expressions. Negative and positive expressed conditions are considered as outliers. For outliers in each direction, the algorithm tries to further quantileDiscretize the expression values in case rank>1.
对于每一个基因，该算法适用于位数离散分为负（低），未变更和积极（高）表达式的条件。表示积极和消极条件被视为离群。在每个方向上的离群值，该算法尝试，进一步quantileDiscretizerank>1表达式的值。

This second discretization step is performed by dividing the sorted outliers into rank tandom groups with equal conditions. A label is assigned to each of these tandom groups, in the following order:
这第二个离散步骤进行由分成rank喷雾多级串联组同等条件下的排序离群。一个标签被分配到每个这些喷雾多级串联组，按下列顺序：

for outliers with negative expression, from the most negative group to the least negative group (not the other way around!).
为阴性表达，从最消极的组至少阴性组（而不是相反！）离群。

Similarly, for positive outliers, labels in the order of
同样，对于积极离群，标签的顺序

are assigned to tandom groups from the least positive group to the most positive group.
被分配到喷雾多级串联组从至少阳性对照组，以最积极的群体。

That is, signs of labels indicate the direction of gene expression change, and the absolute value represents the quantileDiscretized rank in the outliers.
也就是说，标签的迹象表明，基因表达的变化方向，绝对值表示在离群quantileDiscretized排名。


值----------Value----------

An object of the same class as the input parameter, with the exprs slot replaced by the quantileDiscretized matrix, which is a matrix of integer.
作为输入参数，exprs取代的的矩阵quantileDiscretized，这是一个整数矩阵插槽，同一类的一个对象。


注意----------Note----------

Note that the resulting discrete matrix of this implementation can be slighly different from the one used by the QUBIC command line tool.
请注意，此实现所产生的离散矩阵可以由QUBIC命令行工具使用的性能稍微不同。

The main reason for this is the internal data type: while QUBIC uses float to represent expression matrix, we use double to represent the matrix.
这种情况的主要原因是内部数据类型：同时QUBIC使用float代表表达矩阵，我们可以使用double代表的矩阵。

It has the advantages of interfacing to R, having higher precision and avoiding errors caused by floating presentation. It is implemented with potential larger costs of memory, however for test data sets (for example the ALL dataset with more than 120 samples and 12000 genes) the peak memory use (<100M) as well as the execution time (CPU time 0.028s) are well under control.
它连接到R的优点，具有较高的精度和避免浮动演示引起的错误。这是实施潜力较大的内存成本，但是测试数据集（例如与120多个样本和12000个基因的所有数据集）的峰值内存使用（<100M），以及执行时间（CPU时间0.028s ）是很好的控制。

The differentially is especially often observed when there are many tied values. These cases however are very rare cases and we assume they will not affect the results to a large extent.
的差异，特别是经常观察时，有许多并列值。然而，这些情况是非常罕见的情况下，我们假设他们不会在很大程度上影响结果。


作者（S）----------Author(s)----------



Jitao David Zhang &lt;jitao_david.zhang@roche.com&gt;




参考文献----------References----------

analyses of gene expression data Nucleic Acids Research 37:e101

参见----------See Also----------

parseQubicChars parses the quantileDiscretized matrix by the QUBIC command line tool into a data frame.
parseQubicChars解析到一个数据框的QUBIC命令行工具quantileDiscretized矩阵。


举例----------Examples----------


data(sample.ExpressionSet, package="Biobase")
sample.disc <- quantileDiscretize(sample.ExpressionSet)
exprs(sample.disc)[1:6, 1:6]

## Equivalent to pass a numeric matrix[＃相当于通过数字矩阵]
sample.mat.disc <- quantileDiscretize(exprs(sample.ExpressionSet))
sample.mat.disc[1:6, 1:6]
## Not run: identical(exprs(sample.disc),sample.mat.disc)[＃无法运行：相同（exprs（sample.disc），sample.mat.disc）]

## with multiple ranks[＃与多个行列]
sample.rank3 <- quantileDiscretize(sample.ExpressionSet, rank=3)
exprs(sample.rank3)[1:6, 1:6]

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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