RLEPlot {RUVcorr} | R Documentation |
RLEPlot
generates three different types of
relative log expression plots for high-dimensional data.
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 )
X |
A matrix of gene expression values. |
Y |
A matrix of gene expression values. |
center |
A logical scalar; |
name |
A vector of characters describing the data contained in
|
title |
A character string describing the title of the plot. |
method |
The type of RLE plot to be displayed; possible inputs are
|
anno |
A dataframe or a matrix containing the annotation of
arrays in |
Factor |
A character string corresponding to a column name of
|
numeric |
A logical scalar indicating whether |
new.legend |
A vector describing the names used for labelling; if |
outlier |
A logical indicating whether outliers should be plotted; only
applicable when |
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.
RLEPlot
returns a plot.
Saskia Freytag, Terry Speed
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)