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MAID : an effect size based model for microarray data integration across laboratories and platforms.

Borozan I, Chen L, Paeper B, Heathcote JE, Edwards AM, Katze M, Zhang Z, McGilvray ID - BMC Bioinformatics (2008)

Bottom Line: Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models.We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated.Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca

ABSTRACT

Background: Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions.

Results: Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated.

Conclusion: High-throughput genomics data provide a rich and complex source of information that could play a key role in deciphering intricate molecular networks behind disease. Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.

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False discovery rate for the three integration methods. Figure 6a shows the plot of the number of genes selected by each method versus the expected number of false positives (E(F P)), figure 6b shows the same plot as figure 6a with the expected number of false positives E(F P) ≤ 21.
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Figure 6: False discovery rate for the three integration methods. Figure 6a shows the plot of the number of genes selected by each method versus the expected number of false positives (E(F P)), figure 6b shows the same plot as figure 6a with the expected number of false positives E(F P) ≤ 21.

Mentions: In order to compare results obtained from these three different models we compared gene lists selected as significant by each individual method. In order to make a valid comparison the selected gene sets were required to have the same expected number of false positives E(F P), in this way the comparison between results obtained with MAID and results obtained with other models is ensured to be done at the same statistical significance level (see Tables 3, 4 and Figure 6). For the purpose of comparison we chose a reasonably conservative value for E(F P) of 10 (see also Figure 6b). The biological relevance of gene sets selected by each individual model is then evaluated by comparing the significance, the biological relevance and the content (i.e gene number) of enriched GO biological process categories.


MAID : an effect size based model for microarray data integration across laboratories and platforms.

Borozan I, Chen L, Paeper B, Heathcote JE, Edwards AM, Katze M, Zhang Z, McGilvray ID - BMC Bioinformatics (2008)

False discovery rate for the three integration methods. Figure 6a shows the plot of the number of genes selected by each method versus the expected number of false positives (E(F P)), figure 6b shows the same plot as figure 6a with the expected number of false positives E(F P) ≤ 21.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2483727&req=5

Figure 6: False discovery rate for the three integration methods. Figure 6a shows the plot of the number of genes selected by each method versus the expected number of false positives (E(F P)), figure 6b shows the same plot as figure 6a with the expected number of false positives E(F P) ≤ 21.
Mentions: In order to compare results obtained from these three different models we compared gene lists selected as significant by each individual method. In order to make a valid comparison the selected gene sets were required to have the same expected number of false positives E(F P), in this way the comparison between results obtained with MAID and results obtained with other models is ensured to be done at the same statistical significance level (see Tables 3, 4 and Figure 6). For the purpose of comparison we chose a reasonably conservative value for E(F P) of 10 (see also Figure 6b). The biological relevance of gene sets selected by each individual model is then evaluated by comparing the significance, the biological relevance and the content (i.e gene number) of enriched GO biological process categories.

Bottom Line: Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models.We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated.Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca

ABSTRACT

Background: Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions.

Results: Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated.

Conclusion: High-throughput genomics data provide a rich and complex source of information that could play a key role in deciphering intricate molecular networks behind disease. Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.

Show MeSH