Limits...
Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.

Hu P, Greenwood CM, Beyene J - BMC Bioinformatics (2005)

Bottom Line: We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation.We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes.Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Hospital for Sick Children Research Institute, 555 University Ave,, Toronto, ON, M5G 1X8, Canada. phu@sickkids.ca

ABSTRACT

Background: With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.

Results: We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously.

Conclusion: Data integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

Show MeSH
Relationship between number of significantly expressed genes and different delta levels, obtained from fitting the random effects model.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC1173085&req=5

Figure 4: Relationship between number of significantly expressed genes and different delta levels, obtained from fitting the random effects model.

Mentions: To identify a list of potentially "significant" genes, we adapted the false discovery rate (FDR) algorithm implemented in [30]. We first calculated the adjusted z statistics for all genes based on random-effects model (REM). Genes were then ranked by the magnitude of their z statistic values. A permutation-based approach was used to obtain the corresponding expected ordered z statistic. The potentially "significant" genes are genes with a distance between the ordered z statistic from the observed data and that of the permuted data exceeding a given threshold (delta). Figure 4 shows the relationship between the number of significantly differentially expressed genes and different delta levels. As we see in this figure, the quality-adjusted REM can identify many more significant genes than the quality-unadjusted REM model at any fixed level of delta.


Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.

Hu P, Greenwood CM, Beyene J - BMC Bioinformatics (2005)

Relationship between number of significantly expressed genes and different delta levels, obtained from fitting the random effects model.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1173085&req=5

Figure 4: Relationship between number of significantly expressed genes and different delta levels, obtained from fitting the random effects model.
Mentions: To identify a list of potentially "significant" genes, we adapted the false discovery rate (FDR) algorithm implemented in [30]. We first calculated the adjusted z statistics for all genes based on random-effects model (REM). Genes were then ranked by the magnitude of their z statistic values. A permutation-based approach was used to obtain the corresponding expected ordered z statistic. The potentially "significant" genes are genes with a distance between the ordered z statistic from the observed data and that of the permuted data exceeding a given threshold (delta). Figure 4 shows the relationship between the number of significantly differentially expressed genes and different delta levels. As we see in this figure, the quality-adjusted REM can identify many more significant genes than the quality-unadjusted REM model at any fixed level of delta.

Bottom Line: We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation.We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes.Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Hospital for Sick Children Research Institute, 555 University Ave,, Toronto, ON, M5G 1X8, Canada. phu@sickkids.ca

ABSTRACT

Background: With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.

Results: We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously.

Conclusion: Data integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

Show MeSH