GPRankTargets(tigre)
GPRankTargets()所属R语言包：tigre
Ranking possible target genes or regulators
排名可能的靶基因或监管
译者：生物统计家园网 机器人LoveR
描述----------Description----------
GPRankTargets ranks possible target genes by forming optimized models with a fixed transcription factor, a set of known target genes and targets to be tested. The transcription factor and the known targets are always included in the models while the tested targets are tested by including them in the models one at a time. The function determines itself whether to use GPSIM or GPDISIM based on the input arguments.
GPRankTargets排名可能的靶基因，通过形成一个固定的转录因子，一组已知的靶基因，并进行测试的目标优化模型。转录因子和已知的目标是始终包含在模型中，而测试的目标是测试，包括他们在一次的车型之一。功能决定是否使用输入参数的基础上与gpsim或GPDISIM。
用法----------Usage----------
GPRankTargets(preprocData, TF = NULL, knownTargets = NULL,
testTargets = NULL, filterLimit = 1.8,
returnModels = FALSE, options = NULL,
scoreSaveFile = NULL,
datasetName = "", experimentSet = "")
GPRankTFs(preprocData, TFs, targets,
filterLimit = 1.8, returnModels = FALSE, options = NULL,
scoreSaveFile = NULL, datasetName = "", experimentSet = "")
参数----------Arguments----------
参数：preprocData
The preprocessed data to be used.
预处理后的数据可以使用。
参数：TF
The transcription factor (TF) probe present in all models when TF protein translation model is used. Set to NULL (default) when translation model is not used.
转录因子（TF），在所有型号的探针目前TF蛋白翻译模型时使用。设置为NULL（默认）时不使用翻译模型。
参数：knownTargets
The target genes present in all models.
靶基因存在于所有车型。
参数：testTargets
Target genes that are tested by including them in the models one at a time. Can be names of genes, or a set of indices in preprocData.
目标是基因测试，包括他们在一次的车型之一。可能是基因的名称，或一组preprocData指数。
参数：filterLimit
Genes with an average expression z-score above this figure are accepted after filtering. If this value is 0, all genes will be accepted.
接受过滤后的平均Z-得分高于这个数字表达的基因。如果这个值是0，所有的基因将被接受。
参数：returnModels
A logical value determining whether the function returns the calculated models.
一个逻辑值，决定是否该函数返回的计算模型。
参数：options
A list of additional arguments to pass to GPLearn.
一个额外的参数列表传递到GPLearn的。
参数：scoreSaveFile
Name of file to save the scores to after processing each gene.
文件名称后保存到分数处理每一个基因。
参数：TFs
The transcription factors that are tested by including them in the models one at a time.
测试是在一次的车型之一，包括他们的转录因子。
参数：targets
The target genes present in all models.
靶基因存在于所有车型。
参数：datasetName
For exporting the scores using export.scores: Name of the dataset in the database.
出口使用export.scores：在数据库中的数据集名的成绩。
参数：experimentSet
For exporting the scores using export.scores: Name of the experiment set in the database.
出口使用export.scores：在数据库中设置实验名的成绩。
Details
详情----------Details----------
The models are formed by calling GPLearn. If there is no value given to the transcription factor, a model without protein translation is used. Without protein translation model, some known targets are needed. If known targets are given, a model is first created with only the transcription factor and the known targets. The parameters extracted from this model are used as initial parameters of the models with test targets.
调用GPLearn形成的模型。如果是没有价值的转录因子，没有蛋白质的翻译模型。没有蛋白质的翻译模型，一些已知的目标是必要的。如果已知的目标，首先创建一个模型只转录因子和已知的目标。从这个模型中提取的参数是用来作为测试目标模型的初始参数。
GPRankTFs is very similar to GPRankTargets, except it loops over candidate regulators, not candidate targets.
GPRankTFsGPRankTargets非常相似，但它循环候选人监管机构，而不是候选人的目标。
值----------Value----------
The function returns a scoreList containing the genes, parameters and log-likelihoods of the models If returnModels is true, the function returns a list of the calculated models.
该函数返回一个scoreList包含的基因，参数和log模型的似然性如果returnModels是真实的，函数返回一个列表的计算模型。
作者（S）----------Author(s)----------
Antti Honkela, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence
参见----------See Also----------
GPLearn, scoreList, generateModels, export.scores.
GPLearn, scoreList, generateModels, export.scores。
举例----------Examples----------
# Load a mmgmos preprocessed fragment of the Drosophila developmental[加载的果蝇发育mmgmos预处理片段]
# time series[时间序列]
data(drosophila_gpsim_fragment)
# Get the target probe names[获取目标探测名称]
targets <- c('FBgn0003486', 'FBgn0033188', 'FBgn0035257')
library(annotate)
aliasMapping <- getAnnMap("ALIAS2PROBE",
annotation(drosophila_gpsim_fragment))
twi <- get('twi', env=aliasMapping)
fbgnMapping <- getAnnMap("FLYBASE2PROBE",
annotation(drosophila_gpsim_fragment))
targetProbes <- mget(targets, env=fbgnMapping)
scores <- GPRankTargets(drosophila_gpsim_fragment, TF=twi,
testTargets=targetProbes,
options=list(quiet=TRUE),
filterLimit=1.8)
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 2012-2-26 15:31:55
