simone-package(simone)
simone-package()所属R语言包：simone
Statistical Inference for MOdular NEtworks (SIMoNe)
模块化网络的统计推断（SIMONE）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The R package simone implements the inference of co-expression networks based on partial correlation coefficients from either steady-state or time-course transcriptomic data. Note that with both type of data this package can deal with samples collected in different experimental conditions and therefore not identically distributed. In this particular case, multiple but related graphs are inferred at once.
R包simone实现共表达网络的基础上无论从稳定状态的时间过程转录组数据的偏相关系数的推断。请注意，程序包可以处理两种类型的数据，这在不同的实验条件下收集的样品，因此没有同分布。在这个特殊的情况下，一次多，但相关的图形推断。
The underlying statistical tools enter the framework of Gaussian graphical models (GGM). Basically, the algorithm searches for a latent clustering of the network to drive the selection of edges through an adaptive l1-penalization of the model likelihood.
相关统计工具进入高斯的图形化模型（GGM）的框架。基本上，网络的一个潜在的聚类算法搜索驱动选择的边通过一个自适应l1-模型可能性处罚。
The available inference methods for edges selection and/or estimation include
边缘选择和/或估计可用的推理方法
neighborhood selectionas in Meinshausen and Buhlman (2006), steady-state data only;
的附近selectionas在Meinshausen和Buhlman（2006年），稳定状态数据;
graphical Lassoas in Banerjee et al, 2008 and Friedman et al (2008), steady-state data only;
Banerjee等人，2008年和弗里德曼等人（2008年），稳定状态数据的的图形Lassoas在;
VAR(1) inferenceas in Charbonnier, Chiquet and Ambroise (2010), time-course data only;
在沙博尼耶，Chiquet和安布（2010年），时间过程数据的VAR（1）inferenceas的;
multitask learningas in Chiquet, Grandvalet and Ambroise (preprint), both time-course and steady-state data.
在多任务learningas Chiquet，Grandvalet和安布（预印本），两者的时间过程和稳定状态的数据。
All the listed methods are l1-norm based penalization, with an additional grouping effect for multitask learning (including three variants: "intertwined", "group-Lasso" and "cooperative-Lasso").
所有列出的方法是l1-规范的处罚，与一个额外的分组多任务学习（包括三种变体：“交织”，“组套索”和“合作”套索“”）的影响。
The penalization of each individual edge may be weighted according to a latent clustering of the network, thus adapting the inference of the network to a particular topology. The clustering algorithm is performed by the mixer package, based upon Daudin, Picard and Robin (2008)'s Mixture Model for Random Graphs.
根据网络的一个潜在的聚类，每个单独的边缘可被加权的处罚，从而调整到一个特定的拓扑结构的网络的推理。 mixer包，根据Daudin，Picard和罗宾的（2008）的混合模型的随机图的聚类算法。
Since the choice of the network sparsity level remains a current issue in the framework of sparse Gaussian network inference, the algorithm provides a full path of estimates starting from an empty network and adding edges as the penalty level progressively decreases. Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) are adapted to the GGM context in order to help to choose one particular network among this path of solutions.
既然选择的网络稀疏水平仍然是当前的问题，稀疏高斯网络推理的框架，该算法提供了一个完整路径的估计从一个空的网络和增加边的罚则水平逐渐降低。贝叶斯信息标准（BIC）和赤池信息标准（AIC）适用于的GGM背景下，为了帮助这条道路的解决方案中选择一个特定的网络。
Graphical tools are provided to summarize the results of a simone run and offer various representations for network plotting.
图形化工具提供的结果总结了simone的运行，并提供各种表示网络策划。
Details
详细信息----------Details----------
Index: <pre> cancer Microarray data set for breast cancer coNetwork Random perturbations of a reference network getNetwork Network extraction from a SIMoNe run plot.simone Graphical representation of SIMoNe outputs plot.simone.network Graphical representation of a network rNetwork Simulation of (clustered) Gaussian networks rTranscriptData Simulation of artificial transcriptomic data setOptions Low-level options of the 'simone' function simone SIMoNe algorithm for network inference </pre>
的指数：<PRE>癌症基因芯片数据设置为乳腺癌coNetwork随机扰动提取从西蒙娜运行plot.simone的图形表示西蒙娜参考网络getNetwork的网络输出plot.simone.network的图形表示的一个的网络rNetwork模拟（聚类） “西蒙娜”功能西蒙娜西蒙娜网络推理算法高斯的网络rTranscriptData模拟的人工转录组数据传递setOptions低级别的选项</ pre>
演示提供----------Demos available----------
Beyond the examples of this manual, a good starting point is to have a look at the scripts available via demo(package="simone"). They make use of simone, main function in the package, in various contexts (steady-state or time-course data, multiple sample learning). All these scripts also illustrate the use of the different plot functions.
除了本手册的例子，一个良好的出发点是有脚本可以通过demo(package="simone")看看。 simone，主要功能包中的使用，在各种情况下（稳态或时间过程的数据，多个样本学习）。所有这些脚本还说明了使用不同的图功能。
demo(cancer_multitask) example on the cancer data set of the multitask approach with a cooperative-Lasso grouping effect across tasks. Patient responses to the chemiotherapy (pCR or not-pCR) split the data set into two distinct samples. Network inference is performed jointly on these samples and graphical comparison is made between the two networks.
demo(cancer_multitask)cancer的数据集的多任务的方法与跨任务的合作套索分组效果的例子。病人的化疗方案的反应（PCR或-PCR）分裂成两个不同的样本数据集。网络推理共同进行对这些样品，并在两个网络之间进行比较的图形。
demo(cancer_pooled) example on the cancer data set which is designed to compare network inference when a clustering prior is used or not. Graphical comparison between the two inferred networks (with/without clustering prior) illustrates how inference is driven to a particular network topology when clustering is relevant (here, an affiliation structure).
demo(cancer_pooled)例如cancer数据集，其目的是比较前的聚类网络推理时使用或不。推断两者之间的网络（带/不带聚类之前）的图形比较说明，如何推论时，被驱动到一个特定的网络拓扑聚类是相关的（在这里，从属结构）。
demo(check_glasso, echo=FALSE) example that basically checks the consistency between the glasso package of Friedman et al and the simone package to solve the l1-penalized Gaussian likelihood criterion suggested by Banerjee et al in the n>p settings. In the n<p settings, simone provides sparser solutions than the glasso package since the underlying Lasso problems are solved with an active set algorithm instead of the shooting/pathwise coordinate algorithm.
demo(check_glasso, echo=FALSE)的例子，主要检查之间的一致性glasso弗里德曼等人的包和simone处罚高斯似然准则的建议Banerjee等人在l1一揽子解决n>p设置。在n<p设置simone提供稀疏的解决方案，积极集算法，因为底层的套索问题都解决了，而不是拍摄/路径协调算法比glasso包。
demo(simone_multitask) example of multitask learning on simulated, steady-state data: two networks are generated by randomly perturbing a common ancestor with the coNetwork function. These two networks are then used to generate two multivariate Gaussian samples. Multitask learning is applied and a simple illustration of the use of the setOptions function is given.
demo(simone_multitask)多任务学习，稳态模拟数据的例子：两个网络产生的，由随机扰动共同的祖先，与coNetwork功能。然后，这两个网络的用于生成两个多元高斯样本。多任务应用学习setOptions函数使用一个简单的例子给出。
demo(simone_steadyState) example of how to learn a single network from steady-state data. A sample is first generated with the rNetwork and rTranscriptData functions. Then the path of solutions of the neighborhood selection method (default for single task steady-state data) is computed.
demo(simone_steadyState)例如如何学习一个单一的网络，从稳态数据。 rNetwork和rTranscriptData函数产生一个样本。路径计算的的附近选择方法（默认情况下，对于单任务稳态数据）的解决方案。
demo(simone_timeCourse) example of how to learn a single network from time-course data. A sample is first generated with the rNetwork and rTranscriptData functions and the path of solutions of the VAR(1) inference method is computed, with and without clustering prior.
demo(simone_timeCourse)例如如何学习的时间过程数据从一个单一的网络。首先产生的一个样本rNetwork和rTranscriptData功能和解决方案的VAR（1）推理方法的路径计算和聚类之前。
（作者）----------Author(s)----------
参考文献----------References----------
J. Chiquet, Y. Grandvalet, and C. Ambroise (preprint). Inferring multiple graphical structures. preprint available on ArXiv. http://arxiv.org/abs/0912.4434.
C. Charbonnier, J. Chiquet, and C. Ambroise (2010). Weighted-Lasso for Structured Network Inference from Time Course Data. Statistical Applications in Genetics and Molecular Biology, vol. 9, iss. 1, article 15. http://www.bepress.com/sagmb/vol9/iss1/art15/
C. Ambroise, J. Chiquet, and C. Matias (2009). Inferring sparse Gaussian graphical models with latent structure. Electronic Journal of Statistics, vol. 3, pp. 205–238. http://dx.doi.org/10.1214/08-EJS314
O. Banerjee, L. El Ghaoui, A. d'Aspremont (2008). Model Selection Through Sparse Maximum Likelihood Estimation. Journal of Machine Learning Research, vol. 9, pp. 485–516. http://www.jmlr.org/papers/volume9/banerjee08a/banerjee08a.pdf
J. Friedman, T. Hastie and R. Tibshirani (2008). Sparse inverse covariance estimation with the graphical Lasso. Biostatistics, vol. 9(3), pp. 432–441. http://www-stat.stanford.edu/~tibs/ftp/graph.pdf
N. Meinshausen and P. Buhlmann (2006). High-dimensional graphs and variable selection with the Lasso. The Annals of Statistics, vol. 34(3), pp. 1436–1462. http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&id=pdfview_1&handle=euclid.aos/1152540754
J.-J. Daudin, F.Picard and S. Robin, S. (2008). Mixture model for random graphs. Statistics and Computing, vol. 18(2), pp. 173–183. http://www.springerlink.com/content/9v6846342mu82x42/fulltext.pdf
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注：
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发表于 2012-9-30 02:52:08
