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

Visual representation of the pathways for each MINE method.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110234-g002: Visual representation of the pathways for each MINE method.

Mentions: The program MINE to simulate the above procedures is written in Java under version 1.6 and utilizes the version 5 of the Jama library [25]. Details of the input and output of this software are already reported [26]. The program is available in sourceforge.net under the name linearminesimulations. There are four variants on the MINE method described below in this section and summarized in Figure 2.


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

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

Visual representation of the pathways for each MINE method.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110234-g002: Visual representation of the pathways for each MINE method.
Mentions: The program MINE to simulate the above procedures is written in Java under version 1.6 and utilizes the version 5 of the Jama library [25]. Details of the input and output of this software are already reported [26]. The program is available in sourceforge.net under the name linearminesimulations. There are four variants on the MINE method described below in this section and summarized in Figure 2.

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