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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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Principal variation pattern in the Toxicogenomics dataset. The pattern is captured by the first component of submodel b+ab (treatment + timextreatment) of ASCA-functional analysis. The plot shows the score values of this first component.
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Figure 8: Principal variation pattern in the Toxicogenomics dataset. The pattern is captured by the first component of submodel b+ab (treatment + timextreatment) of ASCA-functional analysis. The plot shows the score values of this first component.

Mentions: Finally the ASCA-functional method gave an intermediate result between the two previous approaches. Analysis by ASCA indicated three main independent patterns of variation within the transcriptomics signal. As in the other approaches, the first component, which collects 46% of the gene expression variability, represents the pattern of change (induction or repression) by high BB at 24 hours (Figure 8). The second component, with 10% associated explained variance, represents the change of medium BB at 24 hours. The third component (9% explained variance) captures the early responses at medium and high BB. As the first principal component represents mostly the toxicological response, this was the one subjected to FatiScan that resulted in the identification of 15 BP 20 MF and 8 CC significant features (Table 2a and Additional file 2). Significant processes included ribosome, ferric ion binding, rRNA binding, energy and electron transport at the upper end of the gene rank, indicating that these functions are positively correlated with the pattern provided by the first ASCA-genes component of submodel b+ab, i.e, induction by high BB at 24 h. GO terms such as retinoic metabolic process, fatty acid beta oxidation, glutamine family amino-acid metabolism, oxidorreductase activity were found significantly enriched at the bottom end of the gene rank, indicating their opposite correlated pattern of change.


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)

Principal variation pattern in the Toxicogenomics dataset. The pattern is captured by the first component of submodel b+ab (treatment + timextreatment) of ASCA-functional analysis. The plot shows the score values of this first component.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Principal variation pattern in the Toxicogenomics dataset. The pattern is captured by the first component of submodel b+ab (treatment + timextreatment) of ASCA-functional analysis. The plot shows the score values of this first component.
Mentions: Finally the ASCA-functional method gave an intermediate result between the two previous approaches. Analysis by ASCA indicated three main independent patterns of variation within the transcriptomics signal. As in the other approaches, the first component, which collects 46% of the gene expression variability, represents the pattern of change (induction or repression) by high BB at 24 hours (Figure 8). The second component, with 10% associated explained variance, represents the change of medium BB at 24 hours. The third component (9% explained variance) captures the early responses at medium and high BB. As the first principal component represents mostly the toxicological response, this was the one subjected to FatiScan that resulted in the identification of 15 BP 20 MF and 8 CC significant features (Table 2a and Additional file 2). Significant processes included ribosome, ferric ion binding, rRNA binding, energy and electron transport at the upper end of the gene rank, indicating that these functions are positively correlated with the pattern provided by the first ASCA-genes component of submodel b+ab, i.e, induction by high BB at 24 h. GO terms such as retinoic metabolic process, fatty acid beta oxidation, glutamine family amino-acid metabolism, oxidorreductase activity were found significantly enriched at the bottom end of the gene rank, indicating their opposite correlated pattern of change.

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