@@ -2,19 +2,20 @@ Package: edge

 Type: Package

 Title: Extraction of Differential Gene Expression

 Date: 2015-04-15

-Version: 2.2.0

+Version: 2.2.1

 Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass

-Maintainer: John D. Storey <jstorey@princeton.edu>, Andrew J. Bass <ajbass@princeton.edu>

+Maintainer: John D. Storey <jstorey@princeton.edu>, Andrew J. Bass

+    <ajbass@princeton.edu>

 biocViews: MultipleComparison, DifferentialExpression, TimeCourse,

     Regression, GeneExpression, DataImport

 Description: The edge package implements methods for carrying out differential

-    expression analyses of genome-wide gene expression studies. Significance

-    testing using the optimal discovery procedure and generalized likelihood

-    ratio tests (equivalent to F-tests and t-tests) are implemented for general study

-    designs. Special functions are available to facilitate the analysis of

-    common study designs, including time course experiments. Other packages

-    such as snm, sva, and qvalue are integrated in edge to provide a wide range

-    of tools for gene expression analysis.

+    expression analyses of genome-wide gene expression studies. Significance testing

+    using the optimal discovery procedure and generalized likelihood ratio tests

+    (equivalent to F-tests and t-tests) are implemented for general study designs.

+    Special functions are available to facilitate the analysis of common study

+    designs, including time course experiments. Other packages such as snm, sva,

+    and qvalue are integrated in edge to provide a wide range of tools for gene

+    expression analysis.

 VignetteBuilder: knitr

 Imports:

     methods,

@@ -37,3 +38,4 @@ BugReports: https://github.com/jdstorey/edge/issues

 LazyData: true

 License: MIT + file LICENSE

 NeedsCompilation: yes

+RoxygenNote: 5.0.1

@@ -1,4 +1,4 @@

-# Generated by roxygen2 (4.1.1): do not edit by hand

+# Generated by roxygen2: do not edit by hand

 export("fullMatrix<-")

 export("fullModel<-")

@@ -35,11 +35,11 @@ exportMethods(nullModel)

 exportMethods(sType)

 import(Biobase)

 import(MASS)

+import(jackstraw)

 import(methods)

 import(qvalue)

 import(snm)

 import(splines)

 import(sva)

-import(jackstraw)

 useDynLib(edge,kldistance)

 useDynLib(edge,odpScoreCluster)

@@ -526,7 +526,7 @@ setGeneric("apply_snm", function(object, int.var=NULL, ...)

 #' their principal components (PCs).

 #'

 #' @param object \code{S4 object}: \code{\linkS4class{deSet}}

-#' @param PC a numeric vector of principal components of interest. Choose a subset of r significant PCs to be used.

+#' @param r1 a numeric vector of principal components of interest. Choose a subset of r significant PCs to be used.

 #' @param r a number (a positive integer) of significant principal components.

 #' @param s a number (a positive integer) of synthetic null variables. Out of m variables, s variables are independently permuted.

 #' @param B a number (a positive integer) of resampling iterations. There will be a total of s*B null statistics.

@@ -546,11 +546,11 @@ setGeneric("apply_snm", function(object, int.var=NULL, ...)

 #' varibles and their r PCs.

 #'

 #' You could specify a subset of significant PCs

-#' that you are interested in (PC). If PC is given, then this function computes

+#' that you are interested in r1. If PC is given, then this function computes

 #' statistical significance of association between m variables and PC, while

 #' adjusting for other PCs (i.e., significant PCs that are not your interest).

 #' For example, if you want to identify variables associated with 1st and 2nd

-#' PCs, when your data contains three significant PCs, set r=3 and PC=c(1,2). 

+#' PCs, when your data contains three significant PCs, set r=3 and r1=c(1,2). 

 #' 

 #' Please take a careful look at your data and use appropriate graphical and

 #' statistical criteria to determine a number of significant PCs, r. The number

@@ -596,17 +596,17 @@ setGeneric("apply_snm", function(object, int.var=NULL, ...)

 #' de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model,

 #'                       full.model = full_model)

 #' ## apply the jackstraw

-#' out = apply_jackstraw(de_obj, PC=1, r=1)

+#' out = apply_jackstraw(de_obj, r1=1, r=1)

 #' ## Use optional arguments

 #' ## For example, set s and B for a balance between speed of the algorithm and accuracy of p-values

-#' ## out = apply_jackstraw(dat, PC=1, r=1, s=10, B=1000, seed=5678)

+#' ## out = apply_jackstraw(dat, r1=1, r=1, s=10, B=1000, seed=5678)

 #'

 #' @seealso \code{\link{permutationPA}}

 #'

 #' @author Neo Christopher Chung \email{nc@@princeton.edu}

 #' @import jackstraw

 #' @export

-setGeneric("apply_jackstraw", function(object, PC = NULL, r = NULL, s = NULL, B = NULL,

+setGeneric("apply_jackstraw", function(object, r1 = NULL, r = NULL, s = NULL, B = NULL,

                                        covariate = NULL, verbose = TRUE, seed = NULL)

   standardGeneric("apply_jackstraw"))

@@ -348,10 +348,10 @@ setMethod("apply_snm",

 #' @rdname apply_jackstraw

 setMethod("apply_jackstraw",

           signature = signature(object="deSet"),

-          function(object, PC = NULL, r = NULL, s = NULL, B = NULL,

+          function(object, r1 = NULL, r = NULL, s = NULL, B = NULL,

                    covariate = NULL, verbose = TRUE, seed = NULL) {

             dat <- exprs(object)

-            js <- jackstraw::jackstraw(dat, PC = PC, r = r, s = s, B = B,

+            js <- jackstraw::jackstraw.PCA(dat, r1 = r1, r = r, s = s, B = B,

                       covariate = covariate, verbose = verbose, seed = seed)

             return(js)

           })

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{apply_jackstraw}

@@ -6,16 +6,16 @@

 \alias{apply_jackstraw,deSet-method}

 \title{Non-Parametric Jackstraw for Principal Component Analysis (PCA)}

 \usage{

-apply_jackstraw(object, PC = NULL, r = NULL, s = NULL, B = NULL,

+apply_jackstraw(object, r1 = NULL, r = NULL, s = NULL, B = NULL,

   covariate = NULL, verbose = TRUE, seed = NULL)

-\S4method{apply_jackstraw}{deSet}(object, PC = NULL, r = NULL, s = NULL,

+\S4method{apply_jackstraw}{deSet}(object, r1 = NULL, r = NULL, s = NULL,

   B = NULL, covariate = NULL, verbose = TRUE, seed = NULL)

 }

 \arguments{

 \item{object}{\code{S4 object}: \code{\linkS4class{deSet}}}

-\item{PC}{a numeric vector of principal components of interest. Choose a subset of r significant PCs to be used.}

+\item{r1}{a numeric vector of principal components of interest. Choose a subset of r significant PCs to be used.}

 \item{r}{a number (a positive integer) of significant principal components.}

@@ -55,11 +55,11 @@ r significant PCs, this function tests for linear association between m

 varibles and their r PCs.

 You could specify a subset of significant PCs

-that you are interested in (PC). If PC is given, then this function computes

+that you are interested in r1. If PC is given, then this function computes

 statistical significance of association between m variables and PC, while

 adjusting for other PCs (i.e., significant PCs that are not your interest).

 For example, if you want to identify variables associated with 1st and 2nd

-PCs, when your data contains three significant PCs, set r=3 and PC=c(1,2).

+PCs, when your data contains three significant PCs, set r=3 and r1=c(1,2). 

 Please take a careful look at your data and use appropriate graphical and

 statistical criteria to determine a number of significant PCs, r. The number

@@ -84,10 +84,11 @@ full_model <- ~sex + ns(age, df = 4)

 de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model,

                       full.model = full_model)

 ## apply the jackstraw

-out = apply_jackstraw(de_obj, PC=1, r=1)

+out = apply_jackstraw(de_obj, r1=1, r=1)

 ## Use optional arguments

 ## For example, set s and B for a balance between speed of the algorithm and accuracy of p-values

-## out = apply_jackstraw(dat, PC=1, r=1, s=10, B=1000, seed=5678)

+## out = apply_jackstraw(dat, r1=1, r=1, s=10, B=1000, seed=5678)

+

 }

 \author{

 Neo Christopher Chung \email{nc@princeton.edu}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{apply_qvalue}

@@ -44,6 +44,7 @@ de_lrt <- lrt(de_obj)

 de_lrt <- apply_qvalue(de_lrt, fdr.level = 0.05,

 pi0.method = "bootstrap", adj=1.2)

 summary(de_lrt)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{apply_snm}

@@ -13,15 +13,15 @@ apply_snm(object, int.var = NULL, ...)

 \arguments{

 \item{object}{\code{S4 object}: \code{\linkS4class{deSet}}}

-\item{int.var}{\code{data frame}: intensity-dependent effects (see

+\item{int.var}{\code{data frame}: intensity-dependent effects (see 

 \code{\link{snm}} for details)}

 \item{...}{Additional arguments for \code{\link{snm}}}

 }

 \value{

-\code{apply_snm} returns a \code{\linkS4class{deSet}} object where

+\code{apply_snm} returns a \code{\linkS4class{deSet}} object where 

 assayData (the expression data) that has been passed to apply_snm is replaced

-with the normalized data that \code{\link{snm}} returns.  Specifically,

+with the normalized data that \code{\link{snm}} returns.  Specifically, 

 \code{exprs(object)} is replaced by \code{$norm.dat} from \code{\link{snm}},

 where \code{object} is the \code{\link{deSet}} object.

 }

@@ -48,6 +48,7 @@ null.model = null_model)

 # run snm using intensity-dependent adjustment variable

 de_snm <- apply_snm(de_obj, int.var = singleChannel$int.var,

 verbose = FALSE, num.iter = 1)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{apply_sva}

@@ -16,7 +16,7 @@ apply_sva(object, ...)

 \item{...}{Additional arguments for \code{\link{sva}}}

 }

 \value{

-\code{\linkS4class{deSet}} object where the surrogate variables

+\code{\linkS4class{deSet}} object where the surrogate variables 

 estimated by \code{\link{sva}} are added to the full model and null model

 matrices.

 }

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R

 \docType{methods}

 \name{betaCoef}

@@ -43,6 +43,7 @@ de_fit <- fit_models(de_obj)

 # extract beta coefficients

 beta <- betaCoef(de_fit)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/form_models.R

 \name{build_models}

 \alias{build_models}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/form_models.R

 \name{build_study}

 \alias{build_study}

@@ -12,7 +12,7 @@ build_study(data, grp = NULL, adj.var = NULL, bio.var = NULL,

 \item{data}{\code{matrix}: gene expression data (rows are genes, columns are

 samples).}

-\item{grp}{\code{vector}: group assignement in the study (for K-class

+\item{grp}{\code{vector}: group assignement in the study (for K-class 

 studies). Optional.}

 \item{adj.var}{\code{matrix}: adjustment variables. Optional.}

@@ -24,10 +24,10 @@ studies). Optional.}

 \item{ind}{\code{factor}: individual factor for repeated observations of the

 same individuals. Optional.}

-\item{sampling}{\code{string}: type of study. Either "static" or

+\item{sampling}{\code{string}: type of study. Either "static" or 

 "timecourse". Default is "static".}

-\item{basis.df}{\code{numeric}: degrees of freedom of the basis for time

+\item{basis.df}{\code{numeric}: degrees of freedom of the basis for time 

 course study. Default is 2.}

 \item{basis.type}{\code{string}: either "ncs" (natural cubic spline) or "ps"

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllClasses.R

 \docType{class}

 \name{deFit-class}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllClasses.R

 \docType{class}

 \name{deSet-class}

@@ -8,7 +8,7 @@

 The \code{deSet} class extends the \code{\link{ExpressionSet}} class.

 While the \code{ExpressionSet} class contains information about the

 experiment, the \code{deSet} class contains both experimental information and

-additional information relevant for differential expression analysis,

+additional information relevant for differential expression analysis, 

 explained below in Slots.

 }

 \section{Slots}{

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/ExpressionSet-methods.R

 \docType{methods}

 \name{deSet}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/edge.R

 \docType{package}

 \name{edge}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/edge.R

 \docType{data}

 \name{endotoxin}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R

 \docType{methods}

 \name{fitFull}

@@ -42,6 +42,7 @@ de_fit <- fit_models(de_obj)

 # extract fitted values for full model

 fitted_full <- fitFull(de_fit)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R

 \docType{methods}

 \name{fitNull}

@@ -42,6 +42,7 @@ de_fit <- fit_models(de_obj)

 # extract fitted values from null model

 fitted_null <- fitNull(de_fit)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{fit_models}

@@ -28,7 +28,7 @@ squares method. Model fits can be either statistic type "odp" (optimal

 discovery procedure) or "lrt" (likelihood ratio test).

 }

 \details{

-If "odp" method is implemented then the null model is removed from the full

+If "odp" method is implemented then the null model is removed from the full 

 model (see Storey 2007).  Otherwise, the statistic type has no affect on the

 model fit.

 }

@@ -62,6 +62,7 @@ fit_odp <- fit_models(de_obj, stat.type = "odp") # odp method

 # summarize object

 summary(fit_odp)

+

 }

 \author{

 John Storey

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R, R/setMethods.R

 \docType{methods}

 \name{fullMatrix}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R, R/setMethods.R

 \docType{methods}

 \name{fullModel}

@@ -50,6 +50,8 @@ mod_full <- fullModel(de_obj)

 # change the full model in the experiment

 fullModel(de_obj) <- ~sex + ns(age, df = 2)

+

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/edge.R

 \docType{data}

 \name{gibson}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R, R/setMethods.R

 \docType{methods}

 \name{individual}

@@ -20,8 +20,8 @@ individual(object) <- value

 \item{object}{\code{\linkS4class{deSet}}}

 \item{value}{\code{factor}: Identifies which samples correspond to which

-  individuals. Important if the same individuals are sampled multiple times

-  in a longitudinal fashion.}

+individuals. Important if the same individuals are sampled multiple times

+in a longitudinal fashion.}

 }

 \value{

 \code{individual} returns information regarding dinstinct individuals

@@ -57,6 +57,7 @@ individual = ind)

 # extract out the individuals factor

 ind_exp <- individual(de_obj)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/edge.R

 \docType{data}

 \name{kidney}

@@ -26,8 +26,8 @@ obtained per individual, and the age and sex of each individual were

 recorded.

 }

 \note{

-These data are a random subset of 500 probe-sets from the total number of

-probe-sets in the original data set. To download the full data set, go to

+These data are a random subset of 500 probe-sets from the total number of 

+probe-sets in the original data set. To download the full data set, go to 

 \url{http://genomine.org/edge/}. The \code{age} and \code{sex} are contained

 in \code{kidcov} data frame.

 }

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{kl_clust}

@@ -44,7 +44,7 @@ exact results but mODP has the advantage of being computationally

 faster.

 }

 \note{

-The results are generally insensitive to the number of modules after a

+The results are generally insensitive to the number of modules after a 

   certain threshold of about n.mods>=50 in our experience. It is recommended

   that users experiment with the number of modules. If the number of modules

   is equal to the number of genes then the original ODP is implemented.

@@ -75,6 +75,7 @@ de_clust <- kl_clust(de_obj, n.mods = 10)

 # input a deFit object

 de_fit <- fit_models(de_obj, stat.type = "odp")

 de_clust <- kl_clust(de_obj, de.fit = de_fit)

+

 }

 \author{

 John Storey, Jeffrey Leek

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{lrt}

@@ -90,6 +90,7 @@ de_lrt <- lrt(de_obj, de.fit = de_fit)

 # summarize object

 summary(de_lrt)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R, R/setMethods.R

 \docType{methods}

 \name{nullMatrix}

@@ -48,6 +48,7 @@ full.model = full_model)

 # extract the null model as a matrix

 mat_null <- nullMatrix(de_obj)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R, R/setMethods.R

 \docType{methods}

 \name{nullModel}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deSet-methods.R

 \docType{methods}

 \name{odp}

@@ -91,6 +91,7 @@ bs.its = 30)

 # summarize object

 summary(de_odp)

+

 }

 \author{

 John Storey, Jeffrey Leek, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R, R/setMethods.R

 \docType{methods}

 \name{qvalueObj}

@@ -56,6 +56,7 @@ qval_obj <- qvalueObj(de_odp)

 pvals <- qval_obj$pvalues

 qval_new <- qvalue(pvals, pfdr = TRUE, fdr.level = 0.1)

 qvalueObj(de_odp) <- qval_new

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R

 \docType{methods}

 \name{resFull}

@@ -42,6 +42,7 @@ de_fit <- fit_models(de_obj)

 # extract out the full residuals from the model fit

 res_full <- resFull(de_fit)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R

 \docType{methods}

 \name{resNull}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/getMethods.R

 \docType{methods}

 \name{sType}

@@ -43,6 +43,7 @@ de_fit <- fit_models(de_obj)

 # extract the statistic type of model fits

 stat_type <- sType(de_fit)

+

 }

 \author{

 John Storey, Andrew Bass

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deFit-methods.R, R/deSet-methods.R

 \docType{methods}

 \name{show}

@@ -1,4 +1,4 @@

-% Generated by roxygen2 (4.1.1): do not edit by hand

+% Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/AllGenerics.R, R/deFit-methods.R, R/deSet-methods.R

 \docType{methods}

 \name{summary}