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GEDI: a user-friendly toolbox for analysis of large-scale gene expression data.

Fujita A, Sato JR, Ferreira CE, Sogayar MC - BMC Bioinformatics (2007)

Bottom Line: GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us.It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations.Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

View Article: PubMed Central - HTML - PubMed

Affiliation: Chemistry Institute, University of São Paulo, Av, Lineu Prestes, 748 - São Paulo, 05508-900, SP, Brazil. fujita@ime.usp.br

ABSTRACT

Background: Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.

Results: Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.

Conclusion: GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

Show MeSH
SVAR output. The gene expression regulatory network generated by SVAR, where each node is a gene and the oriented edges represent the Granger causalities.
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Figure 3: SVAR output. The gene expression regulatory network generated by SVAR, where each node is a gene and the oriented edges represent the Granger causalities.

Mentions: • Gene expression regulatory networks: GEDI plots graphs which represent the regulatory networks (Figure 3). Each node of the graph represents the gene, and the edges represent the Granger causalities (VAR, DVAR and SVAR) and correlations (Pearson and Spearman). It also plots the time-varying connectivity graphic, time × connectivity plot, to visualize how the connectivity changes with time in the DVAR method (Figure 4).


GEDI: a user-friendly toolbox for analysis of large-scale gene expression data.

Fujita A, Sato JR, Ferreira CE, Sogayar MC - BMC Bioinformatics (2007)

SVAR output. The gene expression regulatory network generated by SVAR, where each node is a gene and the oriented edges represent the Granger causalities.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194737&req=5

Figure 3: SVAR output. The gene expression regulatory network generated by SVAR, where each node is a gene and the oriented edges represent the Granger causalities.
Mentions: • Gene expression regulatory networks: GEDI plots graphs which represent the regulatory networks (Figure 3). Each node of the graph represents the gene, and the edges represent the Granger causalities (VAR, DVAR and SVAR) and correlations (Pearson and Spearman). It also plots the time-varying connectivity graphic, time × connectivity plot, to visualize how the connectivity changes with time in the DVAR method (Figure 4).

Bottom Line: GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us.It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations.Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

View Article: PubMed Central - HTML - PubMed

Affiliation: Chemistry Institute, University of São Paulo, Av, Lineu Prestes, 748 - São Paulo, 05508-900, SP, Brazil. fujita@ime.usp.br

ABSTRACT

Background: Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.

Results: Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.

Conclusion: GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

Show MeSH