qFarms: qFarms expression measure

In farms: FARMS - Factor Analysis for Robust Microarray Summarization

Description

This function converts an instance of AffyBatch into an instance of exprSet-class using a factor analysis model for which a Bayesian Maximum a Posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. This function is a wrapper for expresso and uses the function normalize.quantiles for array normalization.

Usage

          qFarms(object, weight, mu,  weighted.mean, laplacian, robust, correction, centering, spuriousCorrelation, ...)
          

Arguments

object	An instance of AffyBatch.
weight	Hyperparameter value in the range of [0,1] which determines the influence of the prior. The default value is 0.5
mu	Hyperparameter value which allows to quantify different aspects of potential prior knowledge. Values near zero assumes that most genes do not contain a signal, and introduces a bias for loading matrix elements near zero. Default value is 0
weighted.mean	Boolean flag, that indicates whether a weighted mean or a least square fit is used to summarize the loading matrix. The default value is set to FALSE.
laplacian	Boolean flag, indicates whether a Laplacian prior for the factor is employed or not. Default value is FALSE.
robust	Boolean flag, that ensures non-constant results. Default value is TRUE.
correction	Value that indicates whether the covariance matrix should be corrected for negative eigenvalues which might emerge from the non-negative correlation constraints or not. Default = O (means that no correction is done), 1 (minimal noise (0.0001) is added to the diagonal elements of the covariance matrix to force positive definiteness), 2 (Maximum Likelihood solution to compute the nearest positive definite matrix under the given non-negative correlation constraints of the covariance matrix)
centering	Indicates whether the data is "median" or "mean" centered. Default value is "median".
spuriousCorrelation	Numeric value in the range of [0,1] that quantifies the suppression of spurious correlation when using the Laplacian prior. Default value is 0 (no suppression). Note, that this parameter is only active when the laplacian parameter is set to TRUE.
...	other arguments to be passed to expresso.

Details

This function is a wrapper for expresso.

Value

exprSet-class

See Also

expresso, expFarms, lFarms, normalize.quantiles

Examples

data(testAffyBatch)
eset <- qFarms(testAffyBatch)

farms documentation built on Nov. 8, 2020, 6:08 p.m.