Merge branch 'Usability-updates'

Author: dbottomly

DESCRIPTION

@@ -1,10 +1,9 @@
 Package: pod
-Version: 0.99.04
+Version: 0.99.05
 Date: 2013-10-10
 Title: Tools for patient-specific expression outlier detection
 Authors@R: c(person("Daniel", "Bottomly", role=c("aut", "cre"), email="bottomly@ohsu.edu"),person("Heather", "Turner", role="ctb"), person("Peter", "Ryabinin", role="ctb"), person("Beth", "Wilmot", role="aut"), person("Shannon", "McWeeney", role="aut"))
-Imports: Biobase
-Depends: R (>= 3.1.0),methods
+Depends: R (>= 3.1.0),methods, Biobase
 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

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

README.md

@@ -3,6 +3,28 @@ The `pod` package
 
 The `pod` package contains implementations of the methods compared in 'Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine'.  Currently, this is a bare-bones package however, I will be updating it periodically with documentation, new methods etc. 
 
+How to install
+--------
+
+Using a recent version of R (version >= 3.1) use the following commands:
+
+source("http://bioconductor.org/biocLite.R")
+
+biocLite(c("Biobase", "devtools"))
+
+library(devtools)
+
+install_github(rep="dbottomly/pod", ref="master") 
+
+
+Basic usage
+--------
+
+See the vignette for typical usage:
+
+library(pod)
+vignette("pod")
+
 How to contribute
 ---------
 

data/simEset.R

@@ -0,0 +1,4 @@
+set.seed(123)
+require(Biobase)
+
+simEset <- ExpressionSet(assayData=matrix(rnorm(10000, mean=6, sd=2), nrow=1000, ncol=10, dimnames=list(paste("gene", 1:1000,sep="_"),paste("sample", 1:10 ,sep="_"))))

data/simExprs.R

@@ -31,12 +31,12 @@ Daniel Bottomly
 
 \examples{
 data(simEset)
-out.deg.eset <- outlyingDegree(cur.eset, k=1)
+out.deg.eset <- outlyingDegree(simEset, k=1)
 cur.mat <- exprs(simEset)
 out.deg.mat <- outlyingDegree(cur.mat, k=1)
 all.equal(out.deg.eset, out.deg.mat)
 
-zscore.eset <- zScore(cur.eset)
+zscore.eset <- zScore(simEset)
 zscore.mat <- zScore(cur.mat)
 
 all.equal(zscore.eset, zscore.mat)

man/.Rapp.history

@@ -31,15 +31,15 @@ Using our \texttt{simExprst} ExpressionSet object, we can now compute our outlie
 
 <<>>=
 
-norm.out.deg <- outlyingDegree(simEset), k=5)
+norm.out.deg <- outlyingDegree(simEset, k=5)
 
 norm.out.deg[1:5,1:5]
 
-norm.zscore <- zScore(simEset), robust=FALSE)
+norm.zscore <- zScore(simEset, robust=FALSE)
 
 norm.zscore[1:5,1:5]
 
-norm.rscore <- zScore(simEset), robust=TRUE)
+norm.rscore <- zScore(simEset, robust=TRUE)
 
 norm.rscore[1:5,1:5]
 
@@ -49,7 +49,7 @@ Similarly, we can compute these values using a plain matrix.
 
 <<>>=
 
-norm.exprs <- exprs(simEset))
+norm.exprs <- exprs(simEset)
 
 norm.out.deg.mat <- outlyingDegree(norm.exprs, k=5)