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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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(A) Universal noise curve. Standard deviation of gene expression levels over the replicates versus their mean expression level. The mean expression level of replicates for all genes and stimuli was coarse grained into 14 bins, and the standard deviation was averaged for all set of replicates in a given bin. (B) Saturation curve of gene expression data, computed by integrating the noise curve. This curve for F was used to remove the saturation effect by applying the inverse of F to the gene expression signal
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btu569-F2: (A) Universal noise curve. Standard deviation of gene expression levels over the replicates versus their mean expression level. The mean expression level of replicates for all genes and stimuli was coarse grained into 14 bins, and the standard deviation was averaged for all set of replicates in a given bin. (B) Saturation curve of gene expression data, computed by integrating the noise curve. This curve for F was used to remove the saturation effect by applying the inverse of F to the gene expression signal

Mentions: Universal noise curve: The mean and standard deviation was calculated for a given stimulus and gene over the three/four available replicates. The mean expression levels were binned into 14 bins; the mean expression levels within a given bin were combined and their standard deviations averaged. This procedure produces a universal noise curve of the gene expression data, which is shown in Figure 2.


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)

(A) Universal noise curve. Standard deviation of gene expression levels over the replicates versus their mean expression level. The mean expression level of replicates for all genes and stimuli was coarse grained into 14 bins, and the standard deviation was averaged for all set of replicates in a given bin. (B) Saturation curve of gene expression data, computed by integrating the noise curve. This curve for F was used to remove the saturation effect by applying the inverse of F to the gene expression signal
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btu569-F2: (A) Universal noise curve. Standard deviation of gene expression levels over the replicates versus their mean expression level. The mean expression level of replicates for all genes and stimuli was coarse grained into 14 bins, and the standard deviation was averaged for all set of replicates in a given bin. (B) Saturation curve of gene expression data, computed by integrating the noise curve. This curve for F was used to remove the saturation effect by applying the inverse of F to the gene expression signal
Mentions: Universal noise curve: The mean and standard deviation was calculated for a given stimulus and gene over the three/four available replicates. The mean expression levels were binned into 14 bins; the mean expression levels within a given bin were combined and their standard deviations averaged. This procedure produces a universal noise curve of the gene expression data, which is shown in Figure 2.

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