\name{Dimensionality Reduction} \alias{MDSplot} \alias{MDSplot,CuffData-method} \alias{MDSplot,CuffFeatureSet-method} \alias{PCAplot} \alias{PCAplot,CuffData-method} \alias{PCAplot,CuffFeatureSet-method} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Dimensionality reduction utilities } \description{ Dimensionality reduction plots for feature selection and extraction for cummeRbund } \usage{ \S4method{MDSplot}{CuffData}(object,replicates=FALSE,logMode=TRUE,pseudocount=1.0) \S4method{PCAplot}{CuffData}(object,x="PC1", y="PC2",replicates=FALSE,pseudocount=1.0,scale=TRUE,showPoints = TRUE,...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ The output of class CuffData from which to draw expression estimates. (e.g. genes(cuff)) } \item{x}{ For PCAplot, indicates which principal component is to be presented on the x-axis (e.g. "PC1","PC2","PC3", etc) } \item{y}{ See x. } \item{pseudocount}{ Value added to FPKM to avoid log transformation issues. } \item{logMode}{ Logical value whether or not to use log-transformed expression estimates (default: TRUE) } \item{replicates}{ A logical value to indicate whether or not individual replicate expression estimates will be used. } \item{scale}{ For PCAplot, a logical value passed directly to prcomp. } \item{showPoints}{ For PCAplot, a logical value whether or not to display individual gene values on final PCA plot. } \item{\dots}{ Additional passthrough arguments (may not be fully implemented yet). } } \details{ These methods attempt to project a matrix of expression estimates across conditions and/or replicates onto a smaller number of dimesions for feature selection, feature extraction, and can also be useful for outlier detection. } \value{ A ggplot2 object. } \references{ None. } \author{ Loyal A. Goff } \note{ None. } \examples{ cuff<-readCufflinks(system.file("extdata", package="cummeRbund")) #Create CuffSet object from sample data p<-PCAplot(genes(cuff),x="PC2",y="PC3",replicates=TRUE) m<-MDSplot(genes(cuff),replicates=TRUE) p #Render PCA plot m #Render MDS plot }