Functional Profiling of Microarray Expression Data

Description

FunCluster performs a functional analysis of microarray expression data based on Gene Ontology & KEGG annotations. FunCluster is designed to build functional classes of putatively co-regulated biological processes through a specially designed clustering procedure relying on expression data and functional annotations.

Details

Together with the FunCluster algorithm this package provide also:
1. GO and KEGG annotations (as of June 2008) automatically extracted from their respective web resources

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(date.annot = ""). Under common circumstances these routine will provide up-to-date annotations, stored into environmental variables, directly formatted for FunCluster's use. Some errors may be seen when using this routine related to a lack of availability of the GO annotations for the current month. In case of extraction errors, explained most usually by a delay in updating GO web servers, the release date can be expressly indicated (see annotations).

3. The two test data sets used for the JBCB paper (see examples below). The first data set is related to the dichotomous functional analysis of the genes specifically expressed within adipocytes and stroma vascular fraction (SVF) cells, extracted from adipose tissue of morbidly obese subjects (see submitted paper and cited reference for further details). Two lists of transcripts significantly expressed within adipocytes and SVF cells respectively are provided together with the list of all initial transcripts available for the analysis (necessary for the accurate computation of transcript enrichment during automated annotation of transcript expression data performed by FunCluster). The second data set is structured in a similar way and is containing the hyperinsulinemic muscle clamp expression data.

The format of the data files should be respected in order to perform a successful analysis. All the files are tab separated text files which can be easily obtained from Excel data. The only transcript identification system acceptable for FunCluster analysis is EntrezGene GeneID's. Please see more details on this choice in the JBCB paper. The transcript expression data within the tab separated text files is organized within rows, one for each transcript, and columns with the first one containing the transcript identifiers for each transcript and the rest of them containing the expression level of that transcript in each of the available microarray samples. See test data and JBCB paper for more details.

The results of the FunCluster analysis of transcript expression data are stored as tab separated text files in the "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), FunCluster provides a ranked list with the significant functional clusters observed, stored within a separate text file. Detailed findings on the terminological composition and transcript enrichment significance of the resulting functional clusters are provided. In order to correctly access results files the best approach is to use Microsoft Excel XP or later (English version) as these files were specifically formatted for Excel use. Other tabular data processing software can also be used to read these files, although accessibility will be less optimal. Some difficulties in correctly accessing results files may be observed with older versions of Microsoft Excel (prior to XP version), as well as with Excel versions in other languages than English.

Author(s)

Corneliu Henegar corneliu@henegar.info

References

1. 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.

2. 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.

3. FunCluster website: http://corneliu.henegar.info/FunCluster.htm

See Also

cluster.

Examples

          ## Not run: 
          ## most common use
          FunCluster(go.direct = FALSE, alpha = 0.05, clusterm = "cc",
          		      org = "HS", location = FALSE, compare = 
          		      "common.correl.genes", corr.th = 0.85, 
          		      corr.met = "greedy", two.lists = TRUE, 
          		      restrict = TRUE)
          
          ## when only GO direct annotations are to be used and detailed 
          findings are needed
          FunCluster(go.direct = TRUE, alpha = 0.05, clusterm = "cc",
          		      org = "HS", location = FALSE, compare = 
          		      "common.correl.genes", corr.th = 0.85, 
          		      corr.met = "greedy", two.lists = TRUE, 
          		      restrict = TRUE, details = TRUE)
          
          ## hierarchical agglomerative clustering and Silhouette computations 
          can be used for the preliminary step of building clusters of 
          co-expressed transcripts
          FunCluster(go.direct = TRUE, alpha = 0.05, clusterm = "cc",
          		      org = "HS", location = FALSE, compare = 
          		      "common.correl.genes", corr.th = 0.85, 
          		      corr.met = "hierarchical", two.lists = TRUE, 
          		      restrict = TRUE)

          ## use only common annotated transcripts for the annotation clustering  
          FunCluster(go.direct = FALSE, alpha = 0.05, clusterm = "cc",
          		      org = "HS", location = FALSE, compare = 
          		      "common. genes",
          		      two.lists = TRUE, restrict = TRUE)

          ## the following example forces the use of a previous GO release 
          (e.g. January 2006) for updating annotations
          annotations(date.annot = "200601")
          
## End(Not run)

Results