Limits...
A MINE alternative to D-optimal designs for the linear model.

Bouffier AM, Arnold J, Schüttler HB - PLoS ONE (2014)

Bottom Line: Doing large-scale genomics experiments can be expensive, and so experimenters want to get the most information out of each experiment.Here we explore this idea in a simplified context, the linear model.We also establish in simulations with n<100, p=1000, σ=0.01 and 1000 replicates that these two variations of MINE also display a lower false positive rate than the MINE-like method and additionally, for a majority of the experiments, for the MINE method.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America.

ABSTRACT
Doing large-scale genomics experiments can be expensive, and so experimenters want to get the most information out of each experiment. To this end the Maximally Informative Next Experiment (MINE) criterion for experimental design was developed. Here we explore this idea in a simplified context, the linear model. Four variations of the MINE method for the linear model were created: MINE-like, MINE, MINE with random orthonormal basis, and MINE with random rotation. Each method varies in how it maximizes the MINE criterion. Theorem 1 establishes sufficient conditions for the maximization of the MINE criterion under the linear model. Theorem 2 establishes when the MINE criterion is equivalent to the classic design criterion of D-optimality. By simulation under the linear model, we establish that the MINE with random orthonormal basis and MINE with random rotation are faster to discover the true linear relation with p regression coefficients and n observations when p>n. We also establish in simulations with n<100, p=1000, σ=0.01 and 1000 replicates that these two variations of MINE also display a lower false positive rate than the MINE-like method and additionally, for a majority of the experiments, for the MINE method.

Show MeSH

Related in: MedlinePlus

The number of false positives as a function of the number of experiments.These numbers are averaged over all 1000 simulations for each method. Blue corresponds to MINE-like, red to MINE, green to MINE with random orthonormal basis, and purple to MINE with random rotation. The final two overlap almost exactly which is why the green line is not visible.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4214713&req=5

pone-0110234-g005: The number of false positives as a function of the number of experiments.These numbers are averaged over all 1000 simulations for each method. Blue corresponds to MINE-like, red to MINE, green to MINE with random orthonormal basis, and purple to MINE with random rotation. The final two overlap almost exactly which is why the green line is not visible.

Mentions: The next criterion involves comparing the false positives of each method (Figure 5). A false positive happens if any of the that are actually zero are considered significant. For comparison, the average number of incorrectly identified values, averaged over all simulations for each method, is shown.


A MINE alternative to D-optimal designs for the linear model.

Bouffier AM, Arnold J, Schüttler HB - PLoS ONE (2014)

The number of false positives as a function of the number of experiments.These numbers are averaged over all 1000 simulations for each method. Blue corresponds to MINE-like, red to MINE, green to MINE with random orthonormal basis, and purple to MINE with random rotation. The final two overlap almost exactly which is why the green line is not visible.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214713&req=5

pone-0110234-g005: The number of false positives as a function of the number of experiments.These numbers are averaged over all 1000 simulations for each method. Blue corresponds to MINE-like, red to MINE, green to MINE with random orthonormal basis, and purple to MINE with random rotation. The final two overlap almost exactly which is why the green line is not visible.
Mentions: The next criterion involves comparing the false positives of each method (Figure 5). A false positive happens if any of the that are actually zero are considered significant. For comparison, the average number of incorrectly identified values, averaged over all simulations for each method, is shown.

Bottom Line: Doing large-scale genomics experiments can be expensive, and so experimenters want to get the most information out of each experiment.Here we explore this idea in a simplified context, the linear model.We also establish in simulations with n<100, p=1000, σ=0.01 and 1000 replicates that these two variations of MINE also display a lower false positive rate than the MINE-like method and additionally, for a majority of the experiments, for the MINE method.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America.

ABSTRACT
Doing large-scale genomics experiments can be expensive, and so experimenters want to get the most information out of each experiment. To this end the Maximally Informative Next Experiment (MINE) criterion for experimental design was developed. Here we explore this idea in a simplified context, the linear model. Four variations of the MINE method for the linear model were created: MINE-like, MINE, MINE with random orthonormal basis, and MINE with random rotation. Each method varies in how it maximizes the MINE criterion. Theorem 1 establishes sufficient conditions for the maximization of the MINE criterion under the linear model. Theorem 2 establishes when the MINE criterion is equivalent to the classic design criterion of D-optimality. By simulation under the linear model, we establish that the MINE with random orthonormal basis and MINE with random rotation are faster to discover the true linear relation with p regression coefficients and n observations when p>n. We also establish in simulations with n<100, p=1000, σ=0.01 and 1000 replicates that these two variations of MINE also display a lower false positive rate than the MINE-like method and additionally, for a majority of the experiments, for the MINE method.

Show MeSH
Related in: MedlinePlus