MVR (version 1.20.0)

MVR-package: Mean-Variance Regularization Package

Description

MVR is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data.

It is suited for handling difficult problems posed by high-dimensional multivariate datasets ($p \gg n$ paradigm), such as in omics-type data, among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:

1. Normalization and/or variance stabilization of the data

Computation of mean-variance-regularized t-statistics (F-statistics to come) Generation of diverse diagnostic plots Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a fast and easy experience in the R environment

Details

The following describes all the end-user functions, and internal R subroutines needed for running a complete MVR procedure. Other internal subroutines are not to be called by the end-user at any time. For computational efficiency, end-user regularization functions offer the option to configure a cluster. This is indicated by an asterisk (* = optionally involving cluster usage). The R functions are categorized as follows:

1. NEWS

MVR.news Function to display the NEWS file of the MVR package.

END-USER REGULARIZATION & VARIANCE STABILIZATION FUNCTION

mvr (*) Function for Mean-Variance Regularization and Variance Stabilization. End-user function for Mean-Variance Regularization (MVR) and Variance Stabilization by similarity statistic under sample group homoscedasticity or heteroscedasticity assumption. The function takes advantage of the R package parallel, which allows users to create a cluster of workstations on a local and/or remote machine(s), enabling parallel execution of this function and scaling up with the number of CPU cores available. END-USER REGULARIZED TESTS-STATISTICS FUNCTIONS

mvrt.test (*) Function for Computing Mean-Variance Regularized T-test Statistic and Its Significance. End-user function for computing MVR t-test statistic and its significance (p-value) under sample group homoscedasticity or heteroscedasticity assumption. The function takes advantage of the R package parallel, which allows users to create a cluster of workstations on a local and/or remote machine(s), enabling parallel execution of this function and scaling up with the number of CPU cores available.

END-USER DIAGNOSTIC PLOTS FOR QUALITY CONTROL

cluster.diagnostic Function for Plotting Summary Cluster Diagnostic Plots. Plot similarity statistic profiles and the optimal joint clustering configuration for the means and the variances by group. Plot quantile profiles of means and standard deviations by group and for each clustering configuration, to check that the distributions of first and second moments of the MVR-transformed data approch their respective null distributions under the optimal configuration found, assuming independence and normality of all the variables.

target.diagnostic Function for Plotting Summary Target Moments Diagnostic Plots. Plot comparative distribution densities of means and standard deviations of the data before and after Mean-Variance Regularization to check for location shifts between observed first and second moments and their expected target values under a target centered homoscedastic model. Plot comparative QQ scatterplots to look at departures between observed distributions of first and second moments of the MVR-transformed data and their theoretical distributions assuming independence and normality of all the variables.

stabilization.diagnostic Function for Plotting Summary Variance Stabilization Diagnostic Plots. Plot comparative variance-mean plots to check the variance stabilization across variables before and after Mean-Variance Regularization.

normalization.diagnostic Function for Plotting Summary Normalization Diagnostic Plots. Plot comparative Box-Whisker and Heatmap plots of variables across samples check the effectiveness of normalization before and after Mean-Variance Regularization.

END-USER DATASETS

A Real dataset coming from a quantitative proteomics experiment, consisting of $n=6$ samples split into a control ("M") and a treated group ("S") with $p=9052$ unique peptides or predictor variables. This is a balanced design with two sample groups ($G=2$), under unequal sample group variance.

A Synthetic dataset with $n=10$ observations (samples) and $p=200$ variables, where $nvar=40$ of them are significantly different between the two sample groups. This is a balanced design with two sample groups ($G=2$), under unequal sample group variance.

References

• Dazard J-E., Hua Xu and J. S. Rao (2011). "R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization." In JSM Proceedings, Section for Statistical Programmers and Analysts. Miami Beach, FL, USA: American Statistical Association IMS - JSM, 3849-3863.
• Dazard J-E. and J. S. Rao (2012). "Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data." Comput. Statist. Data Anal. 56(7):2317-2333.

See Also

• makeCluster(R packageparallel)
• justvsn(R packagevsn) Variance stabilization and calibration for microarray dataHuber, 2002
• eBayes(R packagelimma) Bayesian Regularized t-test statisticSmyth, 2004
• samr(R packagesamr) SAM Regularized t-test statisticTusher et al., 2001, Storey, 2003
• matest(R packagemaanova) James-Stein shrinkage estimator-based Regularized t-test statisticCui et al., 2005
• ebam(R packagesiggenes) Empirical Bayes Regularized z-test statisticEfron, 2001
• bayesTHierarchical Bayesian Regularized t-test statisticBaldi et al., 2001