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A shortcut for multiple testing on the directed acyclic graph of gene ontology.

Saunders G, Stevens JR, Isom SC - BMC Bioinformatics (2014)

Bottom Line: Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect.The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.The Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ~15,000 times faster).

View Article: PubMed Central - PubMed

Affiliation: Utah State University, Department of Mathematics & Statistics, Logan, Utah, USA. saundersg@byui.edu.

ABSTRACT

Background: Gene set testing has become an important analysis technique in high throughput microarray and next generation sequencing studies for uncovering patterns of differential expression of various biological processes. Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level.

Results: The Short Focus Level procedure, which performs a shortcut of the full Focus Level procedure, is achieved by extending the reach of graphical weighted Bonferroni testing to closed testing situations where restricted hypotheses are present, such as in the Gene Ontology graphs. The Short Focus Level multiplicity adjustment can perform the full top-down approach of the original Focus Level procedure, overcoming a significant disadvantage of the otherwise powerful Focus Level multiplicity adjustment. The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.

Conclusions: The Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ~15,000 times faster). The Short Focus Level should be used in place of the Focus Level procedure whenever the logical assumptions of the Gene Ontology graph structure are appropriate for the study objectives and when either no a priori focus level of interest can be specified or the focus level is selected at a higher level of the graph, where the Focus Level procedure is computationally intractable.

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A toy GO graph example illustrating the difference between the current Focus Level method and the proposed Short Focus Level method.(a) The full closure of the example toy GO graph depicted in panel (b) that is currently utilized by the Focus Level method. (c) The graph (α,G) corresponding to the example toy GO graph depicted in panel (b) that is utilized by the proposed Short Focus Level procedure.
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Fig7: A toy GO graph example illustrating the difference between the current Focus Level method and the proposed Short Focus Level method.(a) The full closure of the example toy GO graph depicted in panel (b) that is currently utilized by the Focus Level method. (c) The graph (α,G) corresponding to the example toy GO graph depicted in panel (b) that is utilized by the proposed Short Focus Level procedure.

Mentions: The following simulation based on the toy GO graph depicted in Figure 7 panel (b) demonstrates the advantages and disadvantages of moving to the newly proposed graphical shortcut of [19] in the top-down portion of the Focus Level procedure. The simulation was performed with the phenotype Y as a dichotomous class variable (say, treatment and control) and the data X representing an RNA-Seq counts matrix with rows as genes (m) and columns as samples (n). The number of samples belonging to the treatment group was simulated according to a binomial(n, 0.5) distribution, where n is the total number of samples, with the added rule that at least two samples were in each group. This allowed for unbalanced data, with the tendency towards fairly balanced designs. Separate simulations for sample sizes of n=5, 20, and 100 were performed.Figure 7


A shortcut for multiple testing on the directed acyclic graph of gene ontology.

Saunders G, Stevens JR, Isom SC - BMC Bioinformatics (2014)

A toy GO graph example illustrating the difference between the current Focus Level method and the proposed Short Focus Level method.(a) The full closure of the example toy GO graph depicted in panel (b) that is currently utilized by the Focus Level method. (c) The graph (α,G) corresponding to the example toy GO graph depicted in panel (b) that is utilized by the proposed Short Focus Level procedure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4232707&req=5

Fig7: A toy GO graph example illustrating the difference between the current Focus Level method and the proposed Short Focus Level method.(a) The full closure of the example toy GO graph depicted in panel (b) that is currently utilized by the Focus Level method. (c) The graph (α,G) corresponding to the example toy GO graph depicted in panel (b) that is utilized by the proposed Short Focus Level procedure.
Mentions: The following simulation based on the toy GO graph depicted in Figure 7 panel (b) demonstrates the advantages and disadvantages of moving to the newly proposed graphical shortcut of [19] in the top-down portion of the Focus Level procedure. The simulation was performed with the phenotype Y as a dichotomous class variable (say, treatment and control) and the data X representing an RNA-Seq counts matrix with rows as genes (m) and columns as samples (n). The number of samples belonging to the treatment group was simulated according to a binomial(n, 0.5) distribution, where n is the total number of samples, with the added rule that at least two samples were in each group. This allowed for unbalanced data, with the tendency towards fairly balanced designs. Separate simulations for sample sizes of n=5, 20, and 100 were performed.Figure 7

Bottom Line: Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect.The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.The Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ~15,000 times faster).

View Article: PubMed Central - PubMed

Affiliation: Utah State University, Department of Mathematics & Statistics, Logan, Utah, USA. saundersg@byui.edu.

ABSTRACT

Background: Gene set testing has become an important analysis technique in high throughput microarray and next generation sequencing studies for uncovering patterns of differential expression of various biological processes. Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level.

Results: The Short Focus Level procedure, which performs a shortcut of the full Focus Level procedure, is achieved by extending the reach of graphical weighted Bonferroni testing to closed testing situations where restricted hypotheses are present, such as in the Gene Ontology graphs. The Short Focus Level multiplicity adjustment can perform the full top-down approach of the original Focus Level procedure, overcoming a significant disadvantage of the otherwise powerful Focus Level multiplicity adjustment. The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.

Conclusions: The Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ~15,000 times faster). The Short Focus Level should be used in place of the Focus Level procedure whenever the logical assumptions of the Gene Ontology graph structure are appropriate for the study objectives and when either no a priori focus level of interest can be specified or the focus level is selected at a higher level of the graph, where the Focus Level procedure is computationally intractable.

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