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Learning dysregulated pathways in cancers from differential variability analysis.

Afsari B, Geman D, Fertig EJ - Cancer Inform (2014)

Bottom Line: Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes.EVA inferred similar pathways as DIRAC at reduced computational costs.Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.

View Article: PubMed Central - PubMed

Affiliation: Postdoctoral Fellow, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.

ABSTRACT
Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.

No MeSH data available.


Related in: MedlinePlus

P-value comparison of DIRAC and EVA: Each circle represents a pathway. x-axis and y-axis represent DIRAC and EVA P-values, respectively.
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f3-cin-suppl.5-2014-061: P-value comparison of DIRAC and EVA: Each circle represents a pathway. x-axis and y-axis represent DIRAC and EVA P-values, respectively.

Mentions: DIRAC and EVA have been shown mathematically similar.36 The main advantages of the EVA are efficiency in calculation and a more straightforward interpretation that does not involve a “template” but rather is simply the average distance between two samples. To illustrate the computational advantage, for the head and neck cancer data, using a Lenovo Think-Pad with Core™ i7–3720QM Intel CPU at 2.6 GHz and only 1000 permutations of phenotype labels, the DIRAC analysis required 207 seconds while the EVA analytical computation only took 0.3 seconds. Figure 3 compares the corresponding P-values of the differential variability measure generated by DIRAC and EVA. These P-values are highly correlated, with a 0.88 Pearson correlation coefficient (P-value <2 × 10−16). Taken together, these results indicate that EVA can be used as a more efficient alternative for DIRAC analysis.


Learning dysregulated pathways in cancers from differential variability analysis.

Afsari B, Geman D, Fertig EJ - Cancer Inform (2014)

P-value comparison of DIRAC and EVA: Each circle represents a pathway. x-axis and y-axis represent DIRAC and EVA P-values, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4218688&req=5

f3-cin-suppl.5-2014-061: P-value comparison of DIRAC and EVA: Each circle represents a pathway. x-axis and y-axis represent DIRAC and EVA P-values, respectively.
Mentions: DIRAC and EVA have been shown mathematically similar.36 The main advantages of the EVA are efficiency in calculation and a more straightforward interpretation that does not involve a “template” but rather is simply the average distance between two samples. To illustrate the computational advantage, for the head and neck cancer data, using a Lenovo Think-Pad with Core™ i7–3720QM Intel CPU at 2.6 GHz and only 1000 permutations of phenotype labels, the DIRAC analysis required 207 seconds while the EVA analytical computation only took 0.3 seconds. Figure 3 compares the corresponding P-values of the differential variability measure generated by DIRAC and EVA. These P-values are highly correlated, with a 0.88 Pearson correlation coefficient (P-value <2 × 10−16). Taken together, these results indicate that EVA can be used as a more efficient alternative for DIRAC analysis.

Bottom Line: Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes.EVA inferred similar pathways as DIRAC at reduced computational costs.Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.

View Article: PubMed Central - PubMed

Affiliation: Postdoctoral Fellow, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.

ABSTRACT
Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.

No MeSH data available.


Related in: MedlinePlus