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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.

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Related in: MedlinePlus

Detection p-values for a sample probe set (38249_at). H1 and H2 denote the detection p-values in normal and lung cancer groups, respectively, for the Harvard study; whereas M1 and M2 denote the detection p-values in normal and lung cancer groups, respectively, for the Michigan study.
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Figure 2: Detection p-values for a sample probe set (38249_at). H1 and H2 denote the detection p-values in normal and lung cancer groups, respectively, for the Harvard study; whereas M1 and M2 denote the detection p-values in normal and lung cancer groups, respectively, for the Michigan study.

Mentions: We show examples of quality scores for selected probe sets in Table 1. The two datasets may have very different detection p-value distributions, which are reflected in the quality scores. Figure 2 shows a box plot of the detection p-values for one probe set from Table 1. When the two datasets give small p-values (e.g., last line in Table 1), the minimum p-value may be much smaller in one dataset than another. Both, however, will give high quality scores with an appropriate choice of the sensitivity parameter s that adjusts how the quality measure interprets the detection p-values.


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

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

Detection p-values for a sample probe set (38249_at). H1 and H2 denote the detection p-values in normal and lung cancer groups, respectively, for the Harvard study; whereas M1 and M2 denote the detection p-values in normal and lung cancer groups, respectively, for the Michigan study.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Detection p-values for a sample probe set (38249_at). H1 and H2 denote the detection p-values in normal and lung cancer groups, respectively, for the Harvard study; whereas M1 and M2 denote the detection p-values in normal and lung cancer groups, respectively, for the Michigan study.
Mentions: We show examples of quality scores for selected probe sets in Table 1. The two datasets may have very different detection p-value distributions, which are reflected in the quality scores. Figure 2 shows a box plot of the detection p-values for one probe set from Table 1. When the two datasets give small p-values (e.g., last line in Table 1), the minimum p-value may be much smaller in one dataset than another. Both, however, will give high quality scores with an appropriate choice of the sensitivity parameter s that adjusts how the quality measure interprets the detection p-values.

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
Related in: MedlinePlus