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Functional assessment of time course microarray data.

Nueda MJ, Sebastián P, Tarazona S, García-García F, Dopazo J, Ferrer A, Conesa A - BMC Bioinformatics (2009)

Bottom Line: Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information.Results were compared to alternative methodologies.The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es

ABSTRACT

Motivation: Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.

Methods: We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.

Results: Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

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Results of simulation study B with the maSigFun method. Changes in sensitivity with the size of the class at three levels of percentage of changing genes (co-expression) in the class. One plot is provided for each level of the goodness of fit R2 of the regression models. Data points correspond to the mean value of 50 simulations. Confidence intervals were omitted due to their negligible size.
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Figure 3: Results of simulation study B with the maSigFun method. Changes in sensitivity with the size of the class at three levels of percentage of changing genes (co-expression) in the class. One plot is provided for each level of the goodness of fit R2 of the regression models. Data points correspond to the mean value of 50 simulations. Confidence intervals were omitted due to their negligible size.

Mentions: Regarding class size, simulation study B showed that this factor is of little relevance when a sufficient level of co-expression and R2 cut-off value are used, as the sensitivity of the method is more dependent on the amount of regulated genes in the class (Figure 3, panels b), c) and d)). However, when functional classes have a lower level of co-expression and a permissive R2 is used, maSigFun revealed a dependency on the class size, because the method is more sensitive for classes with a large number of members (Figure 3, panel a)). Again, specificity was high in all cases (see Additional file 1).


Functional assessment of time course microarray data.

Nueda MJ, Sebastián P, Tarazona S, García-García F, Dopazo J, Ferrer A, Conesa A - BMC Bioinformatics (2009)

Results of simulation study B with the maSigFun method. Changes in sensitivity with the size of the class at three levels of percentage of changing genes (co-expression) in the class. One plot is provided for each level of the goodness of fit R2 of the regression models. Data points correspond to the mean value of 50 simulations. Confidence intervals were omitted due to their negligible size.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2697656&req=5

Figure 3: Results of simulation study B with the maSigFun method. Changes in sensitivity with the size of the class at three levels of percentage of changing genes (co-expression) in the class. One plot is provided for each level of the goodness of fit R2 of the regression models. Data points correspond to the mean value of 50 simulations. Confidence intervals were omitted due to their negligible size.
Mentions: Regarding class size, simulation study B showed that this factor is of little relevance when a sufficient level of co-expression and R2 cut-off value are used, as the sensitivity of the method is more dependent on the amount of regulated genes in the class (Figure 3, panels b), c) and d)). However, when functional classes have a lower level of co-expression and a permissive R2 is used, maSigFun revealed a dependency on the class size, because the method is more sensitive for classes with a large number of members (Figure 3, panel a)). Again, specificity was high in all cases (see Additional file 1).

Bottom Line: Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information.Results were compared to alternative methodologies.The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es

ABSTRACT

Motivation: Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.

Methods: We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.

Results: Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

Show MeSH
Related in: MedlinePlus