Updates to docs and namespace, ready for R-3.1

Author: dbottomly

DESCRIPTION

@@ -1,12 +1,12 @@
 Package: pod
-Version: 0.99.02
+Version: 0.99.03
 Date: 2013-10-10
 Title: Tools for patient-specific expression outlier detection
 Author: Daniel Bottomly
 Maintainer: Daniel Bottomly <bottomly@ohsu.edu>
 Imports: Biobase
-Depends: R (>= 2.15.0)
-Suggests: oligo
+Depends: R (>= 3.1.0),methods
+Suggests: oligo,pd.huex.1.0.st.v2
 Description: An implementation of outlier detection methods discussed in the paper 'Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine'.  Additional related approaches may be added in the future. 
 License: GPL-3
 Collate:methods.R

NAMESPACE

@@ -1,3 +1,5 @@
+import(methods)
 importClassesFrom(Biobase, ExpressionSet)
+importFrom(Biobase, ExpressionSet, exprs)
 
 export(outlyingDegree,zScore)

man/methods.Rd

@@ -0,0 +1,40 @@
+\name{Methods for patient-specific outlier detection}
+\alias{outlyingDegree}
+\alias{outlyingDegree,matrix-method}
+\alias{outlyingDegree,ExpressionSet-method}
+\alias{zScore}
+\alias{zScore,matrix-method}
+\alias{zScore,ExpressionSet-method}
+\title{
+	Methods for patient-specific outlier detection
+}
+\description{
+	Implementations of the outlying degree and Zscore methodologies desribed in Bottomly et al 2013.
+}
+\usage{
+\S4method{outlyingDegree}{ExpressionSet}(obj, k, type=c("non.weight", "weight.before", "weight.after"), weight.func=default.weight.func)
+\S4method{outlyingDegree}{matrix}(obj, k, type=c("non.weight", "weight.before", "weight.after"), weight.func=default.weight.func) 
+\S4method{zScore}{ExpressionSet}(obj, robust=FALSE) 
+\S4method{zScore}{matrix}(obj, robust=FALSE)
+}
+\arguments{
+	\item{obj}{Either a \code{matrix} with rows corresponding to genes and columns corresponding to samples or an \code{ExpressionSet} object }
+	\item{k}{An integer value that can range from 1 to ncol(obj)-1}
+	\item{type}{One of either "non.weight", "weight.before" or "weight.after" which corresponds to the standard outlying degree method, whether to apply the weights specified in \code{weight.func} before computing the distance or after computing the distance respectively.}
+	\item{weight.func}{A function returning a square matrix containing the weights corresponding to the sample-sample dissimilarities.}
+	\item{robust}{Whether a robust Zscore should be computed e.g. (x-median(x))/mad(x)}
+	}
+	
+\author{
+Daniel Bottomly
+}
+
+\examples{
+set.seed(123)
+cur.mat <- matrix(rnorm(20000), ncol=20, nrow=1000, dimnames=list(paste0("gene", 1:1000), paste0("samp", 1:20)))
+cur.eset <- Biobase::ExpressionSet(assayData=cur.mat)
+stopifnot(all.equal(outlyingDegree(cur.mat, k=1), outlyingDegree(cur.eset, k=1)))
+stopifnot(all.equal(zScore(cur.mat), zScore(cur.eset)))
+}
+	
+\keyword{utilities}

inst/doc/pod.Rnw vignettes/pod.Rnwinst/doc/pod.Rnw renamed to vignettes/pod.Rnw

@@ -29,11 +29,11 @@ sampleNames <- c('09206', '09788', '09562', '09177', '09182', '09624', '08431',
     '08A_A11H1_09182_432BD.CEL', '012A_A11H1_09624_432BD.CEL', '011A_A11H1_08431_432BD.CEL', '010A_A11H1_T119_432BD.CEL',
     '014A_A11H1_09076_432BD.CEL', '06A_A11H1_08419_432BD.CEL', '07A_A11H1_08118_432BD.CEL', '015A_A11H1_09190_432BD.CEL')
     
-    cel.dir <- "/Users/bottomly/Desktop/github_projects/pod_vignette/432BD_CELS"
+    cel.dir <- "/Users/bottomly/Desktop/github/pod_vignette/432BD_CELS"
     
     affy.exprs <- read.celfiles(filenames=file.path(cel.dir, filenames), sampleNames=sampleNames, pkgname="pd.huex.1.0.st.v2")
     
-    norm.eset <- rma(object=affy.exprs, background=TRUE, normalize=TRUE, subset=NULL, target="probeset")
+    norm.eset <- rma(object=affy.exprs, background=TRUE, normalize=TRUE, subset=NULL, target="core")
 @
 
 Using our \texttt{norm.eset} object, we can now compute our outlier statistics, for instance the outlying degree, Zscore and Rscore as below.