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Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets.

Hormoz S, Bhanot G, Biehl M, Bilal E, Meyer P, Norel R, Rhrissorrakrai K, Dayarian A - Bioinformatics (2014)

Bottom Line: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development.However, in many cases, a naive 'extrapolation' between the two species has not succeeded.In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods.

View Article: PubMed Central - PubMed

Affiliation: Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA.

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PCA of the training data. (A) The gene set enrichment data (NES score) of the 246 rat genes (rows) as a function of the 52 stimuli (columns) used in the experiments. (B) The first eight principal components of the data in (A). The first principal component clearly shows the largest variation over the stimuli. The variation decreases for higher ranked components
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btu569-F3: PCA of the training data. (A) The gene set enrichment data (NES score) of the 246 rat genes (rows) as a function of the 52 stimuli (columns) used in the experiments. (B) The first eight principal components of the data in (A). The first principal component clearly shows the largest variation over the stimuli. The variation decreases for higher ranked components

Mentions: A probabilistic classification algorithm was used to predict the human gene enrichment scores. We used a naive Bayes classifier (Duda et al., 2000; Hastie et al., 2009) implemented using Matlab statistics toolbox (Methods). The training data for the classifier were a simplified representation of the combined rat A and B NES scores using a certain number of principal components (see below for the optimization procedure for the number of principal components). Figure 3 shows the first eight principal components (see Methods for implementation) of rat NES data under the 52 stimuli of sets A and B.


Inter-species inference of gene set enrichment in lung epithelial cells from proteomic and large transcriptomic datasets.

Hormoz S, Bhanot G, Biehl M, Bilal E, Meyer P, Norel R, Rhrissorrakrai K, Dayarian A - Bioinformatics (2014)

PCA of the training data. (A) The gene set enrichment data (NES score) of the 246 rat genes (rows) as a function of the 52 stimuli (columns) used in the experiments. (B) The first eight principal components of the data in (A). The first principal component clearly shows the largest variation over the stimuli. The variation decreases for higher ranked components
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325538&req=5

btu569-F3: PCA of the training data. (A) The gene set enrichment data (NES score) of the 246 rat genes (rows) as a function of the 52 stimuli (columns) used in the experiments. (B) The first eight principal components of the data in (A). The first principal component clearly shows the largest variation over the stimuli. The variation decreases for higher ranked components
Mentions: A probabilistic classification algorithm was used to predict the human gene enrichment scores. We used a naive Bayes classifier (Duda et al., 2000; Hastie et al., 2009) implemented using Matlab statistics toolbox (Methods). The training data for the classifier were a simplified representation of the combined rat A and B NES scores using a certain number of principal components (see below for the optimization procedure for the number of principal components). Figure 3 shows the first eight principal components (see Methods for implementation) of rat NES data under the 52 stimuli of sets A and B.

Bottom Line: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development.However, in many cases, a naive 'extrapolation' between the two species has not succeeded.In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods.

View Article: PubMed Central - PubMed

Affiliation: Kavli Institute for Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA 93106, USA, Department of Physics, Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, USA, Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands and IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10003, USA.

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Related in: MedlinePlus