FunNet.R-package(FunNet)
FunNet.R-package()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----------
FunNet is an integrative tool for analyzing gene co-expression networks built from microarray expression data. The analytic model implemented in this library involves two abstraction layers: transcriptional and functional (biological roles). A functional profiling technique using Gene Ontology & KEGG annotations is applied to extract a list of relevant biological themes from microarray expression profiling data. Afterwards multiple-instance representations are built to relate significant themes to their transcriptional instances (i.e. the two layers of the model). An adapted non-linear dynamical system model is used to quantify the proximity of relevant genomic themes based on the similarity of the expression profiles of their gene instances. Eventually an unsupervised multiple-instance clustering procedure, relying on the two abstraction layers, is used to identify the structure of the co-expression network composed from modules of functionally related transcripts. Functional and transcriptional maps of the co-expression network are provided separately together with detailed information on the network centrality of related transcripts and genomic themes.
FunNet是一个综合性的基因共表达网络构建基因芯片表达数据分析工具。在库中实现分析模型包括两个抽象层：转录的角色和功能（生物）。使用基因本体论和KEGG注释的功能分析技术，应用基因芯片表达谱数据中提取相关的生物主题列表。之后的多个实例表示他们的的转录情况下（即两个层次的模型）的建立与重大主题。一种适于非线性动力系统模型被用来量化相关的基因的表达谱的相似性，它们的基因实例基于主题接近。最终无人监管的多个实例聚类过程中，依靠两个抽象层，用于识别功能相关的转录由模块组成的共表达网络的结构。共表达网络的功能和转录的图提供单独的网络中心相关的转录和基因组的主题的详细信息。
Details
详细信息----------Details----------
Together with the FunNet algorithm this package provides also:<br> 1. GO and KEGG annotations automatically extracted from their respective web resources and updated on a regular basis<br><br> 2. The routine for the automated extraction and update of the functional annotations from their respective web resources. The use of this routine is simple: annotations(). Under common circumstances these routine will provide up-to-date annotations, stored into environmental variables, directly formatted for FunNet's use.<br><br> 3. Four test data sets (see examples below and the dedicated man pages). Two of these datasets are related to adipose tissue expression profiling in obese subjects at baseline and after a bariatric surgery. The other two are yeast datasets related to the cell cycle and DNA repairing processes induced by irradiation.<br>
的FunNet算法这个软件包提供：1， GO和KEGG从各自的网络资源注释自动提取和更新，定期参考参考2。常规的自动提取和更新的功能注释从各自的网络资源。此例行程序的使用很简单：annotations()。常见的情况下，这些程序将提供最新的注解，存储到环境变量中，直接格式化为FunNet的使用。参考参考。 4个测试数据集（见下面的例子和专用的手册页）。这些数据集的在基线和减重手术后肥胖者的脂肪组织表达谱。其他两个是酵母的数据集有关的单元周期和通过照射诱导的DNA修复过程。<br>物理化学学报
The format of the data should be respected in order to perform a successful analysis. The only transcript identification system acceptable for FunNet analysis is EntrezGene GeneID's. The transcript expression data should be organized in dataframes within one row for each transcript. The first column contains the transcript identifiers for each transcript and the rest of them the expression level of that transcript in each of the available microarray samples. See the provided test data for more details.<br>
为了成功执行分析的数据格式应该得到尊重。唯一的成绩单识别系统可以接受的FunNet分析是EntrezGene GeneID。中的转录表达数据应内一列在dataframes组织的转录。第一列包含每个转录和其余的人在每个可用的芯片样品，转录表达水平的转录标识符。有关详细信息，请参阅提供的测试数据。<BR>
The results of the FunNet analysis of transcript expression data are stored as HTML, tab separated text or R data files in a "Results" subfolder of the working folder. For each type of available biological annotations and for each list of transcript expression data to be analyzed (one or two), FunNet provides a ranked list with the significantly enriched annotating categories, as well as network structures as text files designed to be imported in Cytoscape for graphical analysis. Detailed findings on the terminological composition and transcript enrichment significance of the resulting functional clusters, as well as various network centrality measures are equally provided.
的FunNet的转录表达数据分析的结果保存为HTML，制表符分隔的文本或R数据文件中的“结果”的工作文件夹中的子文件夹。对于每一种可用的生物学注解，并为每个列表的转录表达数据进行分析的（一个或两个），FunNet提供了显著丰富的注解类，以及网络结构的分级列表的文本文件，旨在在Cytoscape的进口图形分析。同样提供的用语的组合物的详细调查结果和成绩单富集意义所得官能聚类，以及各种网络的核心措施。
（作者）----------Author(s)----------
Corneliu Henegar <a href="mailto:corneliu@henegar.info">corneliu@henegar.info</a>
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----------
cluster.
cluster。
实例----------Examples----------
## Not run: [＃不运行：]
## most common use[＃最常见的用途]
data(obese)
FunNet(org="HS", two.lists=TRUE, up.frame=up.frame, down.frame=down.frame,
genes.frame=NULL, restrict=TRUE, ref.list=ref.list, logged=TRUE,
discriminant=TRUE, go.bp=TRUE, go.cc=TRUE, go.mf=TRUE, kegg=TRUE,
annot.method="specificity", annot.details=TRUE,
direct=FALSE, enriched=TRUE, fdr=NA, build.annot.net=TRUE,
coexp.matrix=NULL, coexp.method="spearman", estimate.th=FALSE,
hard.th=0.8, soft.th=NA, topological = FALSE, keep.sign=FALSE, level=1,
annot.clust.method="umilds", annot.prox.measure="dynamical",
test.recovery=FALSE, test.robust=FALSE, replace.annot=NA,
build.gene.net=TRUE, gene.clust.method="hclust", gene.net.details=TRUE,
gene.clusters=NA, alpha=0.05, RV=0.90, sigma=NA, keep.rdata=FALSE, zip=TRUE)
## updating annotations[＃更新注释]
annotations()
## End(Not run)[＃（不执行）]
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 2012-9-18 07:15:19
