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Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

Tanner SW, Agarwal P - BMC Bioinformatics (2008)

Bottom Line: This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments.We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO.Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics program, University of California, San Diego, La Jolla, CA 92093-0419, USA. stanner@ucsd.edu

ABSTRACT

Background: Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.

Results: We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository.

Conclusion: Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

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Comparison of query accuracy, on the evaluation set, with p-values calibrated against the GEO corpus using Pearson correlation. Queries based upon signed p-value were more effective than just p-value. Cyber-T was also extremely effective especially for N > 60. Using log fold changes as gene values was least effective consistently, perhaps due to the noise in the log fold change for genes with low expression.
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Figure 3: Comparison of query accuracy, on the evaluation set, with p-values calibrated against the GEO corpus using Pearson correlation. Queries based upon signed p-value were more effective than just p-value. Cyber-T was also extremely effective especially for N > 60. Using log fold changes as gene values was least effective consistently, perhaps due to the noise in the log fold change for genes with low expression.

Mentions: In a related experiment, we compared the query precision obtained when using the Cyber-T statistic, Cyber-T p-values, or log fold change as our gene readings (Figure 3). Queries using the Cyber-T statistic or p-value are noticeably more accurate than those driven solely by log fold change. This reflects the large amount of noise in fold-change measurements for genes expressed at a low level. Not surprisingly, precision declines as the significance threshold drops (i.e. N increases).


Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

Tanner SW, Agarwal P - BMC Bioinformatics (2008)

Comparison of query accuracy, on the evaluation set, with p-values calibrated against the GEO corpus using Pearson correlation. Queries based upon signed p-value were more effective than just p-value. Cyber-T was also extremely effective especially for N > 60. Using log fold changes as gene values was least effective consistently, perhaps due to the noise in the log fold change for genes with low expression.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2543031&req=5

Figure 3: Comparison of query accuracy, on the evaluation set, with p-values calibrated against the GEO corpus using Pearson correlation. Queries based upon signed p-value were more effective than just p-value. Cyber-T was also extremely effective especially for N > 60. Using log fold changes as gene values was least effective consistently, perhaps due to the noise in the log fold change for genes with low expression.
Mentions: In a related experiment, we compared the query precision obtained when using the Cyber-T statistic, Cyber-T p-values, or log fold change as our gene readings (Figure 3). Queries using the Cyber-T statistic or p-value are noticeably more accurate than those driven solely by log fold change. This reflects the large amount of noise in fold-change measurements for genes expressed at a low level. Not surprisingly, precision declines as the significance threshold drops (i.e. N increases).

Bottom Line: This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments.We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO.Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics program, University of California, San Diego, La Jolla, CA 92093-0419, USA. stanner@ucsd.edu

ABSTRACT

Background: Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.

Results: We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository.

Conclusion: Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

Show MeSH
Related in: MedlinePlus