screening(rHVDM)
screening()所属R语言包：rHVDM
Fits the optimal kinetic parameter values for several genes.
适合几个基因的最佳动力学参数值。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This method fits the three kinetic parameter values for each gene in a user-supplied vector. It returns a list containing the results.
这种方法适合每个基因的三个动力学参数值用户提供向量。它返回一个列表，其中包含的结果。
用法----------Usage----------
screening(eset,genes,HVDM,transforms,cl1zscorelow,cl1modelscorehigh,cl1degraterange)
参数----------Arguments----------
参数：eset
an ExpressionSet object (Biobase)
ExpressionSet对象（BIOBASE）
参数：genes
a vector containing the genes identifiers to be screened (in character format)
矢量含有筛选基因标识符（字符格式）
参数：transforms
a vector containing the kintetic parameter identifiers that have to be transformed during optimisation (optional)
矢量包含kintetic参数标识符（可选）在优化过程中，必须转化
参数：HVDM
a HVDM object (see details)
1 HVDM对象（见详情）
参数：cl1zscorelow
the sensitivity Z-score cutoff value for a gene to be classified as a putative target
Z-score模型的灵敏度为一个基因的临界值，被归类为假定目标
参数：cl1modelscorehigh
the model score cutoff value for a gene to be classified as a putative target
被列为一个假定的目标模式得分为临界值的基因
参数：cl1degraterange
the degradation rate bounds applied for a gene to be classified as a putative target
申请一个基因的降解率界限被列为一个假定目标
Details
详情----------Details----------
This screening step can only be applied if a training() step has already been run. The output to the training() step can be given as the "HVDM" argument. A fit of each gene in the "genes" argument is then performed.
只能套用这个筛选步骤，如果一个training()一步已经运行。 training()一步的输出可以作为的“HVDM”的说法。然后执行一个适合每个基因在“基因”的说法。
Alternatively an output to a previously run screening() step can be supplied as an "HVDM" argument. In this case, the fit is not performed once again. Feeding a previous output of screening() to that same function again is useful only if the various bounds altered in the secon run. In the case this option is used, the "eset" and "genes" arguments do not have to be supplied (they will be ignored anyway).
另外一个输出先前运行可以作为一个的“HVDM”的说法提供screening()一步。在这种情况下，不适合再次执行。喂screening()前面的输出相同的功能，又是有用的各种边界，只有改变在SECON运行。使用此选项的情况下，“ESET”和“基因”的论点不须提供（反正他们将被忽略）。
The output of that function is a list containing the results. The relevant data frame is in the "results" member of the output. Putative targets can be identified using the "class1" field of that data frame (see example).
该函数的输出是一个列表，其中包含的结果。有关数据框是在输出成员的“结果”。假定的目标可以用“Class1的”数据框的领域（见例子）。
Bounds determining whether a gene is a target of the transcription factor under review have to be supplied. They are:
界确定是否一个基因是一个转录因子审查的目标，必须提供。它们分别是：
- cl1zscorelow: lower bound for the sensitivity Z-score (default: 2.5)
。 - cl1zscorelow：下界为Z-score模型的灵敏度（默认值：2.5）
- cl1modelscore: upper bound for the model score (default: 100.0). This default will have to be changed in most cases. As a rule of thumb, 5x the model score for the genes in the training set can be used.
- cl1modelscore：模型评分（默认值：100.0）上的约束。在大多数情况下，必须更改此默认。作为一个经验法则，5倍的基因在训练集模型的得分都可以使用。
- cl1degraterange: lower and upper bounds for the degradation rate (default: c(0.05,5.0)). This is to exclude those genes with an absurd degradation rate, measured in (unit time)\^(-1). In our example the unit time is an hour. In the case the unit time is different, these default bounds will have to be altered accordingly.
- cl1degraterange：降解率的上限和下限（默认是：C（0.05,5.0））。这是为了排除这些基因与测量，荒谬的降解率（单位时间）\ ^（-1）。在我们的例子中，单位时间是一个小时。在单位时间内的情况不同的是，这些默认的界限，将有相应的改变。
An exponential transform is set by default for both the basal (Bj) and degradation (Dj) rates (through the transforms argument). This forces the values for both these parameters to be positive. It also helps to reach a better fit. To turn this off let transforms=c(). Even in this case the degradation rate will not be allowed to take non positive values as it causes problems with the differential operator used internally. The value in the vector indicates the parameter to be transformed: "Bj": basal rate of transcription, "Sj": sensitivity, "Dj": degradation rate. The entry label indicates the transform to be applied; presently, only log-tranforms are implemented (ie "exp").
默认设置的指数变换为基础（北京）和降解率（DJ）（通过转换参数）。这迫使这两个参数的值是积极的。它还有助于达到一个更适合。要关闭这个功能让变换= C（）。即使在这种情况下，降解率不得采取非正面的价值观，因为它会导致内部使用的微分算子的问题。在向量的值表示要转换的参数：“BJ”：基础的转录率，“SJ”的敏感性，“DJ”：降解率。条目标签指示转化应用;目前只记录tranforms实施（即“地契”）。
值----------Value----------
a list containing the results (see documentation for more details).
一个列表，其中包含的结果（详情请参阅文档）。
注意----------Note----------
Obviously, the expression set given as a eset argument has to be the same as the one used for the training set.
显然，作为表达式设置一个eset的说法是作为一个用于训练集相同。
作者（S）----------Author(s)----------
Martino Barenco
参考文献----------References----------
of p53 targets using Hidden Variable Dynamic Modelling. Genome Biology, V7(3), R25.
参见----------See Also----------
training,HVDMreport,fitgene
training，HVDMreport，fitgene
举例----------Examples----------
data(HVDMexample)
tHVDMp53<-training(eset=fiveGyMAS5,genes=p53traingenes,degrate=0.8,actname="p53")
screenp53<-screening(eset=fiveGyMAS5,genes=genestoscreen[1:10],HVDM=tHVDMp53)
#extracting a list of putative p53 targets[的假定P53目标列表提取]
p53targets<-screenp53$results[screenp53$results$class1,]
#shifting the goal posts[转移的目标职位]
screenp53B<-screening(HVDM=screenp53,cl1zscorelow=3.5)
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 2012-2-26 12:55:48
