RLEPlot: Plots different versions of relative log expression plots

In RUVcorr: Removal of unwanted variation for gene-gene correlations and related analysis

Description

RLEPlot generates three different types of relative log expression plots for high-dimensional data.

Usage

RLEPlot(
  X,
  Y,
  center = TRUE,
  name,
  title,
  method = c("IQR.points", "IQR.boxplots", "minmax"),
  anno = NULL,
  Factor = NULL,
  numeric = FALSE,
  new.legend = NULL,
  outlier = FALSE
)

Arguments

X	A matrix of gene expression values.
Y	A matrix of gene expression values.
center	A logical scalar; TRUE if centering should be applied.
name	A vector of characters describing the data contained in X and Y.
title	A character string describing the title of the plot.
method	The type of RLE plot to be displayed; possible inputs are "IQR.points", "IQR.boxplots" and "minmax" (for information see details).
anno	A dataframe or a matrix containing the annotation of arrays in X and Y (only applicable for method="IQR.points"); if anno=NULL data points are not colored.
Factor	A character string corresponding to a column name of anno to be used for coloring.
numeric	A logical scalar indicating whether Factor is numerical.
new.legend	A vector describing the names used for labelling; if NULL labels in Factor are used.
outlier	A logical indicating whether outliers should be plotted; only applicable when method="minmax".

Details

There are three different RLE plots that can be generated using RLEPlot:

"IQR.points"

Median expression vs. inter-quantile range of every array.

"IQR.boxplots"

Boxplots of the 25% and 75% quantile of all arrays.

"Minmax"

Ordinary RLE plots for the 5 arrays with the smallest and largest inter-quantile ranges.

Note that normal RLE plots are not supplied as they are not very suitable for high-dimensional data.

Value

RLEPlot returns a plot.

Author(s)

Saskia Freytag, Terry Speed

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 
250, 100, intercept=FALSE, check.input=FALSE)
Y.hat<-RUVNaiveRidge(Y, center=TRUE, nc_index=251:500, 0, 10, check.input=TRUE)
try(dev.off(), silent=TRUE)
par(mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
RLEPlot(Y$Y, Y.hat, name=c("Raw", "RUV"), title="", method="IQR.points")
try(dev.off(), silent=TRUE)
par(mfrow=c(1, 1))
RLEPlot(Y$Y, Y.hat, name=c("Raw", "RUV"), title="", method="IQR.boxplots")
try(dev.off(), silent=TRUE)
RLEPlot(Y$Y, Y.hat, name=c("Raw", "RUV"), title="", method="minmax")

#Create a random annotation file
anno<-as.matrix(sample(1:4, dim(Y.hat)[1], replace=TRUE))
colnames(anno)<-"Factor"
try(dev.off(), silent=TRUE)
RLEPlot(Y$Y, Y.hat, name=c("Raw", "RUV"), title="", method="IQR.points",
anno=anno, Factor="Factor", numeric=TRUE)

Example output

null device 
          1 
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RUVcorr documentation built on Nov. 8, 2020, 5:10 p.m.