1. Preconditions

GExMap is an R package developed for the versions 2.2.1 or newer available at r-project.
The gextest() function uses the multtest package downloadable by R using the “Packages > Install package(s)” function..

2. Upload the package and the appropriate data files.

A web site has been created for technical support and to give data files up to date at any moment:GExMap

You must go to the “downloads” section.

3. Install the package.

Select the zip file “GExMap_1.0.zip” downloaded from the GExMap web site.

The GExMap package is now installed in your library folder. Before start GExMap you have to place the Rdata files (genomic data file and id correspondence file) into the data folder.

GExMap is now ready to analyze your data.

4. Format the gene list.

The gene list to be analyzed must be correctly formatted.

In a first column are placed all the identifier and their expression in the second column: -1 for underexpression and 1 for overexpression.

The titles are very important because they allow GExMap to select the appropriate column and data files to upload.

The title of the first column must be composed by the type of identifier (as Affymetrix, Agilent, Unigene, etc…) and an optionally microarray type.

The microarray type and the title of the correspondence file placed in the data folder at step 3 must have the same format (case, underscore, point, etc…). GExMap use the Microarray type to upload the correspondence data file.

If you do not know the Microarray type you must use a standard file called by the ID type, in this case you must entitle the first column with only the ID type.

The ID type and the microarray type are separated by a coma without any space.

5. Run GExMap.

GExMap is ready to go. Run R and load the GExMap package.

Then call the main GExMap function by the command line “gexmap()”.

You can parameter GExMap with several input variables:

- genome: homosapiens, musmusculus, ratusnorvegicus, etc… (default is homosapiens).

- scale: scale used to format the genome used by GExMap and to produces the gaphics (set to 1 000 000 bp by default).

- source: folder where are located all the Rdata files( by default GExMap will search in the “data” folder of the GExMap package).

- res: results folder where are saved all the resulting files produced by GExMap (by default the resulting folder is placed at “C:/”).

- isGO: TRUE to perform the Gene Ontology analysis of the user gene list, FALSE to do not.

- isMAP: TRUE to perform the genomic density analysis, FALSE to do not.

- lim_chi: minimum number fo genes in each unit allowing to use the CHI squared global test (by the gextest() function) to produces reliable statistical results (set to 5 by default).

- global_test_choice: selection of the chromosomes to analyze depending on the global test results (set to 4 by default):

- pcorrd, pcorrv: type of statistical correction of the pvalues produced by the (pcorrd) global Wilcoxon test and the (pcorrv) local Wilcoxon test (by default, pcorrd is set to 2 and pcorrv is set to 1):

6. Types of results.

By default the results folder is created at “C:/gexmap.results”. The mains folder contains three folders, one for the genomic analysis results produced by the global method of hazard estimation, one other with the specific method, the last for the gene ontology analysis results and two report files one for the gene ontology analysis (resume.txt) and the other is the genome matrix with all data produced by the genomic analysis (report.txt).

6.1. Genomic analysis results.

6.1.1. CHI_results.txt.

This table contains all the significant pvalues produced by the CHI squared test, comparing the expected to the real genomic distributions chromosome by chromosome. These results are then selected according to the minimal number of genes by unit for the tested chromosomes (customizable lim_chi variable) to allow reliable statistical results of this global test. The number of genes in unit for each genomic distribution is used to compute CHI test. The size of the unit is progressively increased, starting from scale*1 to scale*20.

6.1.2. Chromosomes graphics .pdf.

One graphical illustration is produced for each chromosome. It allows visualizing three genomic distributions and all the steps of selection and statistical tests of the regions of interest.

Final result is reported by the green line showing the statistically significant regions of interest.

6.1.3. genome.txt.

Table of all data used to make the graphics: ENSEMBL, real and expected distributions by units, by chromosome, up & down are the number of over & under expressed genes. There are also the results of the local statistical tests for each step.

6.1.4. global_tests_results.txt.

This table resumes all the significant results of (CHI_results.txt) the global tests for the lower scale used to select the chromosomes.

6.1.5. test_global.pdf.

This is the graphical representation of the significant results (pval < 0.05) of the CHI_results.txt. It shows the evolution of the resulting pvalue according to the progressive scale concatenation.

6.2. Gene Ontology analysis results.

6.2.1. GO_**.txt.

This file contains all the scored GO identifier of the type ** extracted and computed from the user gene list.

For each GO id is associated: the detailed GO name, the score (number of genes associated with this GO id and the proportion of this score seeing all the others id of this GO type.

6.2.2. detail.**.txt.

This table associates all the genes (woth their ENSEMBL id) correspondig to one GO identifier by column.

6.2.3. GO_**.pdf.

Pie chart of the GO ID which represents more than 1 percent of the total analysed genes..