Annotations(FunNet)
Annotations()所属R语言包：FunNet
Integrative Functional Analysis of Transcriptional Networks
基因调控网络的综合功能分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This routine is close to a similar one belonging to the package FunCluster. The actual one, provided with the package FunNet, performs a slightly different automated extraction and update of the Gene Ontology & KEGG annotations which are needed for FunNet analysis. The difference relies in the fact that additionally to the gene annotation data, extracted from the NCBI ressources, this routine provides also the ontological lattice of GO required by FunNet enrichment computation routine for specificity, terminological or decorrelated annotation.
此例程是接近类似的一个属于的包FunCluster的。 ，提供的包FunNet，的实际执行略有不同的自动提取和的基因本体和KEGG注释所需要的FunNet分析的更新。不同之处在于，在另外的基因注释数据，提取NCBI资源的整合，这个程序还提供了本体论的晶格GO所需的FunNet富集计算程序的特殊性，术语或去相关的注释。
用法----------Usage----------
annotations(cust.specs=NULL)
参数----------Arguments----------
参数：cust.specs
this parameter allows to specify a custom list of organisms for extracting GO and KEGG annotations from respective databases. It is experimental at this moment and therefore no specific documentation is provided at this time about its use (although an example of usage is available within the "Annotations.R" script included in the package).
此参数可以指定自定义列表的生物中提取GO从各自的数据库和KEGG注释。在这一刻，它是实验性的，因此没有提供具体的资料在这个时候它的使用（虽然使用的一个例子是在“Annotations.R”的脚本包中包含）。
Details
详细信息----------Details----------
For details concerning the utilization of the annotations data by the FunNet package please see FunNet help or man page help(FunNet). The "Annotations" routine is allowing the automated extraction and update of the functional annotations from their respective web resources. Under common circumstances this routine will provide up-to-date annotations, stored into environmental variables and directly formatted for FunNet use. Some errors may be seen when using this routine to update annotations provided within the package in relation to frequent changes in the format of annotation files provided by the NCBI. Please update your package with the last version available as this should correct such issues in most cases.<br>
有关的注解的数据由FunNet包的使用情况的详细信息，请参阅帮助FunNet或手册页help(FunNet)。 “注释”程序被允许的自动提取和更新的功能注释从各自的网络资源。常见的情况下，这个程序提供了最新的注解，存储到环境变量和直接格式化为FunNet使用。可能会出现一些错误时，使用此程序来更新的频繁变动由NCBI提供的注释文件的格式提供包内的注释。请更新你的包的最后一个版本可用，因为这在大多数情况下，应该纠正这些问题。<BR>
The annotation data is provided as a unique R data archive which should be manualy loaded into R after loading the FunNet package in order to be able to use the updated annotations for your analysis.<br>
注释数据提供了一个独特的R数据归档，应手动％加载到R之后加载的FunNet的包，以便能够使用更新后的注释分析。<BR>
Important note for Microsoft Windows users: in order to use this routine you will need additional software for handling TAR and GZIP archives. This software is freely available for Windows under the GNU license.<br>
微软Windows用户的重要注意事项：为了使用这个程序，你会需要额外的软件处理TAR，GZIP档案。这个软件是免费提供的适用于Windows下的GNU许可证。<BR>
For TAR packages please go to: <br> http://gnuwin32.sourceforge.net/packages/tar.htm. For GZIP you can go to: <br> http://gnuwin32.sourceforge.net/packages/gzip.htm. The TAR and GZIP executable and their dependencies (DLL's) should be placed somewhere into the PATH (like "C:/Windows" for example) in order to be available for R calls.
对于TAR包，请去：<BR> http://gnuwin32.sourceforge.net/packages/tar.htm。 ，GZIP，你可以去到：<BR> http://gnuwin32.sourceforge.net/packages/gzip.htm。应放在tar和gzip的可执行文件和它们的依赖关系（DLL）到该路径（如“C :/ Windows的”例子）中的R调用。
参考文献----------References----------
1. Prifti E, Zucker JD, Clement K, Henegar C. Interactional and functional centrality in transcriptional co-expression networks. Bioinformatics. 2010 Oct 19. [Epub ahead of print]
2. Prifti E, Zucker JD, Clement K, Henegar C. FunNet: an integrative tool for exploring transcriptional interactions. Bioinformatics. 2008 Nov 15;24(22):2636-8.
3. Henegar C, Tordjman J, Achard V, Lacasa D, Cremer I, Guerre-Millo M, Poitou C, Basdevant A, Stich V, Viguerie N, Langin D, Bedossa P, Zucker J-D, Clement K. Adipose tissue transcriptomic signature highlights the pathologic relevance of extracellular matrix in human obesity. Genome Biology 2008, 9(1):R14.
4. Henegar C, Clement K, and Zucker JD (2006). Unsupervised multiple-instance learning for functional profiling of genomic data. Lecture Notes in Computer Science: ECML 2006. Springer Berlin / Heidelberg, 4212/2006 : 186-197.
5. Henegar C, Cancello R, Rome S, Vidal H, Clement K, Zucker JD. Clustering biological annotations and gene expression data to identify putatively co-regulated biological processes. J Bioinform Comput Biol. 2006 Aug;4(4):833-52.
6. Cancello R, Henegar C, Viguerie N, Taleb S, Poitou C, Rouault C, Coupaye M, Pelloux V, Hugol D, Bouillot JL, Bouloumie A, Barbatelli G, Cinti S, Svensson PA, Barsh GS, Zucker JD, Basdevant A, Langin D, Clement K. Reduction of macrophage infiltration and chemoattractant gene expression changes in white adipose tissue of morbidly obese subjects after surgery-induced weight loss. Diabetes 2005; 54(8):2277-86.
7. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4 (2005) Article17.
8. FunNet websites: http://corneliu.henegar.info/FunNet.htm, http://www.funnet.ws, http://www.funnet.info
参见----------See Also----------
FunNet.
FunNet。
实例----------Examples----------
## Not run: [＃不运行：]
annotations()
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 2012-9-18 07:15:08
