MVR (version 1.20.0)

normalization.diagnostic: Function for Plotting Summary Normalization Diagnostic Plots

Description

Plot comparative Box-Whisker and Heatmap plots of variables across samples check the effectiveness of normalization before and after Mean-Variance Regularization.

Usage

normalization.diagnostic(obj, 
                             title = "",
                             pal,
                             device = NULL, 
                             file = "Normalization Diagnostic Plots")

Arguments

obj

Object of class "mvr" returned by mvr.

title

Title of the plot. Defaults to the empty string.

pal

Color palette.

device

Graphic display device in {NULL, "PS", "PDF"}. Defaults to NULL (screen). Currently implemented graphic display devices are "PS" (Postscript) or "PDF" (Portable Document Format).

file

File name for output graphic. Defaults to "Normalization Diagnostic Plots".

Value

• None. Displays the plots on the chosen device.

Details

Option file is used only if device is specified (i.e. non NULL). The argument pal can be any color palette, e.g. as provided by R package RColorBrewer.

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

justvsn (R package vsn) Variance stabilization and calibration for microarray data. loess (R package stats) Fit a polynomial surface determined by one or more numerical predictors, using local fitting.

Examples

#===================================================
# Loading the libraries
#===================================================
library("MVR")
library("RColorBrewer")

#===================================================
# Mean-Variance Regularization (Real dataset)
# Multi-Group Assumption
# Assuming unequal variance between groups
# Without cluster usage
#===================================================
nc.min <- 1
nc.max <- 30
probs <- seq(0, 1, 0.01)
n <- 6
GF <- factor(gl(n = 2, k = n/2, len = n), 
             ordered = FALSE, 
             labels = c("M", "S"))
mvr.obj <- mvr(data = Real, 
               block = GF, 
               log = FALSE, 
               nc.min = nc.min, 
               nc.max = nc.max, 
               probs = probs,
               B = 100, 
               parallel = FALSE, 
               conf = NULL,
               verbose = TRUE)

#===================================================
# Summary Normalization Diagnostic Plots (Real dataset)
# Multi-Group Assumption
# Assuming unequal variance between groups
#===================================================
normalization.diagnostic(obj = mvr.obj, 
                         title = "Normalization Diagnostic Plots 
                         (Real - Multi-Group Assumption)",
                         pal = brewer.pal(n=11, name="RdYlGn"),
                         device = "PS")

#===================================================
# Mean-Variance Regularization (Real dataset)
# Single-Group Assumption
# Assuming equal variance between groups
# Without cluster usage
#===================================================
nc.min <- 1
nc.max <- 30
probs <- seq(0, 1, 0.01)
n <- 6
mvr.obj <- mvr(data = Real, 
               block = rep(1,n), 
               log = FALSE, 
               nc.min = nc.min, 
               nc.max = nc.max, 
               probs = probs, 
               B = 100, 
               parallel = FALSE, 
               conf = NULL, 
               verbose = TRUE)

#===================================================
# Summary Normalization Stabilization Diagnostic Plots (Real dataset)
# Single-Group Assumption
# Assuming equal variance between groups
#===================================================
normalization.diagnostic(obj = mvr.obj, 
                         title = "Normalization Diagnostic Plots 
                         (Real - Single-Group Assumption)", 
                         pal = brewer.pal(n=11, name="RdYlGn"),
                         device = NULL)