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Microarray enriched gene rank.

Demidenko E - BioData Min (2015)

Bottom Line: We have shown by examples that findings based on GR confirm biological expectations.GR may be used for hypothesis generation on gene pathways.It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Data Science, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, 03755 NH USA.

ABSTRACT

Background: We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R.

Results: Computation of GR is illustrated with several microarray data sets. In particular, we apply GR (1) to answer whether bad genes are more connected than good genes in relation with cancer patient survival, (2) to associate gene connectivity with cluster/subtypes in ovarian cancer tumors, and to determine whether gene connectivity changes (3) from organ to organ within the same organism and (4) between organisms.

Conclusions: We have shown by examples that findings based on GR confirm biological expectations. GR may be used for hypothesis generation on gene pathways. It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting.

No MeSH data available.


Related in: MedlinePlus

Comparison of gene connectivity across three organisms using cdf. The higher the cdf, the more connected are the genes.
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Fig8: Comparison of gene connectivity across three organisms using cdf. The higher the cdf, the more connected are the genes.

Mentions: The gene network connectivity/complexity should increase from simple to complex organisms. While everybody agrees with this statement, there is no rigorous empirical verification of this conjecture. We tackle this question using microarray data from the three experiments, rice, Drosophila and Homo sapiens, analyzed previously, by plotting the empirical GR cdfs on the same graph; see Figure 8. Following definition of stochastic inequality, rice has the smallest gene connectivity and Homo sapiens has the largest connectivity (the median GR is where the cdf = 0.5). Remarkably, there exists a strong separation between the three organisms despite the fact that gene expressions represent different organs at different stages of development.Figure 8


Microarray enriched gene rank.

Demidenko E - BioData Min (2015)

Comparison of gene connectivity across three organisms using cdf. The higher the cdf, the more connected are the genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4305247&req=5

Fig8: Comparison of gene connectivity across three organisms using cdf. The higher the cdf, the more connected are the genes.
Mentions: The gene network connectivity/complexity should increase from simple to complex organisms. While everybody agrees with this statement, there is no rigorous empirical verification of this conjecture. We tackle this question using microarray data from the three experiments, rice, Drosophila and Homo sapiens, analyzed previously, by plotting the empirical GR cdfs on the same graph; see Figure 8. Following definition of stochastic inequality, rice has the smallest gene connectivity and Homo sapiens has the largest connectivity (the median GR is where the cdf = 0.5). Remarkably, there exists a strong separation between the three organisms despite the fact that gene expressions represent different organs at different stages of development.Figure 8

Bottom Line: We have shown by examples that findings based on GR confirm biological expectations.GR may be used for hypothesis generation on gene pathways.It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Data Science, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, 03755 NH USA.

ABSTRACT

Background: We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R.

Results: Computation of GR is illustrated with several microarray data sets. In particular, we apply GR (1) to answer whether bad genes are more connected than good genes in relation with cancer patient survival, (2) to associate gene connectivity with cluster/subtypes in ovarian cancer tumors, and to determine whether gene connectivity changes (3) from organ to organ within the same organism and (4) between organisms.

Conclusions: We have shown by examples that findings based on GR confirm biological expectations. GR may be used for hypothesis generation on gene pathways. It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting.

No MeSH data available.


Related in: MedlinePlus