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Inferring pathway dysregulation in cancers from multiple types of omic data.

MacNeil SM, Johnson WE, Li DY, Piccolo SR, Bild AH - Genome Med (2015)

Bottom Line: Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common.Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level.GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncological Sciences, University of Utah, Salt Lake City, UT USA ; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT USA.

ABSTRACT
Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.

No MeSH data available.


Related in: MedlinePlus

Correlation between GSOA output for microarray and RNA-Sequencing data. We used GSOA to compared HER2+ breast cancer samples against HER2− samples from TCGA for either microarrays or RNA-Sequencing data. GSOA output values correlated strongly for a) AUC scores and b) P values. Spearman correlation coefficients were used to quantify similarity in ranks between the values
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Fig3: Correlation between GSOA output for microarray and RNA-Sequencing data. We used GSOA to compared HER2+ breast cancer samples against HER2− samples from TCGA for either microarrays or RNA-Sequencing data. GSOA output values correlated strongly for a) AUC scores and b) P values. Spearman correlation coefficients were used to quantify similarity in ranks between the values

Mentions: We sought to characterize pathway-level effects resulting from HER2 amplification in breast tumors from TCGA [42]. We used GSOA to compare HER2 positive samples against HER2 negative samples (including normal controls). Using 1,320 canonical pathways [29], we first tested the robustness of our method to inter-platform differences by applying GSOA to microarray and RNA-Sequencing data from the same biological samples (see Additional file 1 for specific parameters). Although these technologies both measure RNA abundance, they produce data with different numerical distributions. The GSOA results for these two platforms were highly correlated (Spearman correlation coefficient = 0.909 for AUC values, 0.728 for P values, see Fig. 3). This level of correlation exceeds what we observed at the individual gene level (average correlation per gene = 0.676). Importantly, the findings for these two platforms led to similar biological conclusions. As expected, among the top results for both platforms were multiple pathways related to HER2 (ERBB2) signaling (see Additional file 4). Other top pathways included those related to PI3K signaling - which has been associated with the HER2 positive subtype [43].Fig. 3


Inferring pathway dysregulation in cancers from multiple types of omic data.

MacNeil SM, Johnson WE, Li DY, Piccolo SR, Bild AH - Genome Med (2015)

Correlation between GSOA output for microarray and RNA-Sequencing data. We used GSOA to compared HER2+ breast cancer samples against HER2− samples from TCGA for either microarrays or RNA-Sequencing data. GSOA output values correlated strongly for a) AUC scores and b) P values. Spearman correlation coefficients were used to quantify similarity in ranks between the values
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4499940&req=5

Fig3: Correlation between GSOA output for microarray and RNA-Sequencing data. We used GSOA to compared HER2+ breast cancer samples against HER2− samples from TCGA for either microarrays or RNA-Sequencing data. GSOA output values correlated strongly for a) AUC scores and b) P values. Spearman correlation coefficients were used to quantify similarity in ranks between the values
Mentions: We sought to characterize pathway-level effects resulting from HER2 amplification in breast tumors from TCGA [42]. We used GSOA to compare HER2 positive samples against HER2 negative samples (including normal controls). Using 1,320 canonical pathways [29], we first tested the robustness of our method to inter-platform differences by applying GSOA to microarray and RNA-Sequencing data from the same biological samples (see Additional file 1 for specific parameters). Although these technologies both measure RNA abundance, they produce data with different numerical distributions. The GSOA results for these two platforms were highly correlated (Spearman correlation coefficient = 0.909 for AUC values, 0.728 for P values, see Fig. 3). This level of correlation exceeds what we observed at the individual gene level (average correlation per gene = 0.676). Importantly, the findings for these two platforms led to similar biological conclusions. As expected, among the top results for both platforms were multiple pathways related to HER2 (ERBB2) signaling (see Additional file 4). Other top pathways included those related to PI3K signaling - which has been associated with the HER2 positive subtype [43].Fig. 3

Bottom Line: Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common.Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level.GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncological Sciences, University of Utah, Salt Lake City, UT USA ; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT USA.

ABSTRACT
Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.

No MeSH data available.


Related in: MedlinePlus