@@ -4,28 +4,16 @@ export(CoGAPS)

 export(CoGapsFromCheckpoint)

 export(GWCoGAPS)

 export(binaryA)

-export(calcCoGAPSStat)

-export(calcGeneGSStat)

 export(calcZ)

-export(computeGeneGSProb)

 export(createGWCoGAPSSets)

 export(displayBuildReport)

 export(gapsMapRun)

 export(gapsRun)

-export(generateSeeds)

-export(patternMarkers)

-export(patternMatch4Parallel)

-export(patternMatcher)

 export(plotAtoms)

 export(plotDiag)

 export(plotGAPS)

 export(plotP)

-export(plotPatternMarkers)

-export(plotSmoothPatterns)

-export(postFixed4Parallel)

-export(reOrderBySet)

 export(reconstructGene)

-export(reorderByPatternMatch)

 export(residuals)

 import(doParallel)

 import(foreach)

@@ -118,6 +118,9 @@ displayBuildReport <- function()

 #' @return list with A and P matrix estimates

 #' @importFrom methods new

 #' @inheritParams CoGAPS

+#' @examples

+#' data(SimpSim)

+#' result <- gapsRun(SimpSim.D, SimpSim.S, nFactor=3)

 #' @export

 gapsRun <- function(D, S, ABins=data.frame(), PBins=data.frame(), nFactor=7,

 simulation_id="simulation", nEquil=1000, nSample=1000, nOutR=1000,

@@ -142,6 +145,11 @@ alphaP=0.01, nMaxP=100000, max_gibbmass_paraP=100.0, seed=-1, messages=TRUE)

 #' @return list with A and P matrix estimates

 #' @importFrom methods new

 #' @inheritParams gapsRun

+#' @examples

+#' data(SimpSim)

+#' nC <- ncol(SimpSim.D)

+#' patterns <- matrix(runif(nC, 0, 1), nrow=1, ncol=nC)

+#' result <- gapsMapRun(SimpSim.D, SimpSim.S, FP=patterns, nFactor=3)

 #' @export

 gapsMapRun <- function(D, S, FP, ABins=data.frame(), PBins=data.frame(),

 nFactor=5, simulation_id="simulation", nEquil=1000, nSample=1000, nOutR=1000,

@@ -163,6 +171,7 @@ v2CoGAPS <- function(result, ...)

 {

     if (!is.null(list(...)$GStoGenes))

     {

+        #warning('GStoGenes is deprecated with v3.0, see CoGAPS documentation')

         if (is.null(list(...)$plot) | list(...)$plot)

         {

             plotGAPS(result$Amean, result$Pmean)

@@ -8,13 +8,6 @@

 #' @param GStoGenes data.frame or list with gene sets

 #' @param numPerm number of permutations for null

 #' @return gene set statistics for each column of A

-#' @examples

-#' # Load the sample data from CoGAPS

-#' data(SimpSim)

-#' # Run calcCoGAPSStat with the correct arguments from 'results'

-#' calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd,

-#' GStoGenes=GSets, numPerm=500)

-#' @export

 calcCoGAPSStat <- function (Amean, Asd, GStoGenes, numPerm=500)

 {

     # test for std dev of zero, possible mostly in simple simulations

@@ -24,29 +17,30 @@ calcCoGAPSStat <- function (Amean, Asd, GStoGenes, numPerm=500)

     # calculate Z scores

     zMatrix <- calcZ(Amean,Asd)

+    # check input arguments

+    if (!is(GStoGenes, "data.frame") && !is(GStoGenes, "list") && !is(GStoGenes,"GSA.genesets"))

+    {

+        stop("GStoGenes must be a data.frame,GSA.genesets, or list with format specified in the users manual.")

+    }

+

     if (is(GStoGenes, "GSA.genesets"))

     {

         names(GStoGenes$genesets) <- GStoGenes$geneset.names

         GStoGenes <- GStoGenes$genesets

     }

-    else if (is(GStoGenes, "list"))

+

+    if (is(GStoGenes, "list"))

     {

         GStoGenesList <- GStoGenes

     }

-    else if (is(GStoGenes, "data.frame"))

+    else

     {

         GStoGenesList <- list()

         for (i in 1:dim(GStoGenes)[2])

         {

-            GStoGenesList[[as.character(colnames(GStoGenes)[i])]] <- 

-                as.character(unique(GStoGenes[,i]))

+            GStoGenesList[[as.character(colnames(GStoGenes)[i])]] <- as.character(unique(GStoGenes[,i]))

         }

     }

-    else

-    {

-        stop(paste("GStoGenes must be a data.frame, GSA.genesets, or list with",

-            "format specified in the users manual."))

-    }

     # get dimensions

     numGS   <- length(names(GStoGenesList))

@@ -10,13 +10,6 @@

 #' @param Pw weight on genes

 #' @param nullGenes logical indicating gene adjustment

 #' @return gene similiarity statistic

-#' @examples

-#' # Load the sample data from CoGAPS

-#' data('SimpSim')

-#' # Run calcGeneGSStat

-#' calcGeneGSStat(SimpSim.result$Amean, SimpSim.result$Asd, 

-#' GSGenes=GSets[[1]], numPerm=500, nullGenes=TRUE)

-#' @export

 calcGeneGSStat  <- function(Amean, Asd, GSGenes, numPerm, Pw=rep(1,ncol(Amean)),

 nullGenes=FALSE)

 {

@@ -46,10 +39,10 @@ nullGenes=FALSE)

     outStats <- outStats / apply(ZD,1,sum)

     outStats[which(apply(ZD,1,sum) < 1e-6)] <- 0

-    #if (sum(gsStat) < 1e-6)

-    #{

-    #    return(0)

-    #}

+    if (sum(gsStat) < 1e-6)

+    {

+        return(0)

+    }

     return(outStats)

 }

@@ -69,12 +62,6 @@ nullGenes=FALSE)

 #' @return A vector of length GSGenes containing the p-values of set membership

 #' for each gene containined in the set specified in GSGenes.

 #' @examples

-#' # Load the sample data from CoGAPS

-#' data('SimpSim')

-#' # Run calcGeneGSStat with the correct arguments from 'results'

-#' calcGeneGSStat(SimpSim.result$Amean, SimpSim.result$Asd, 

-#' GSGenes=GSets[[1]], numPerm=500)

-#' @export

 computeGeneGSProb <- function(Amean, Asd, GSGenes, Pw=rep(1,ncol(Amean)),

 numPerm=500, PwNull=FALSE)

 {

@@ -4,9 +4,6 @@

 #' @param seed positive values are kept, negative values will be overwritten

 #' by a seed generated from the current time

 #' @return vector of randomly generated seeds

-#' @examples

-#' seeds <- generateSeeds(chains=2, seed=-1)

-#' @export

 generateSeeds <- function(chains=2, seed=-1)

 {

     if (chains < 2 || (as.integer(chains) != chains))

@@ -8,13 +8,6 @@

 #' @param full logical indicating whether to return the ranks of each gene for each pattern

 #' @return By default a non-overlapping list of genes associated with each \code{lp}. If \code{full=TRUE} a data.frame of

 #' genes rankings with a column for each \code{lp} will also be returned.

-#' @examples

-#' # Load the sample data from CoGAPS

-#' data(SimpSim)

-#' # Run patternMarkers with the correct arguments from 'results'

-#' patternMarkers(Amatrix=results$Amean,scaledPmatrix=FALSE,

-#' Pmatrix=results$Pmean,threshold="all",full=TRUE)

-#' @export

 patternMarkers <- function(Amatrix=NA, scaledPmatrix=FALSE, Pmatrix=NA,

 threshold="all", lp=NA, full=FALSE)

 {

@@ -10,7 +10,6 @@

 #' @param ... additional parameters for \code{agnes}

 #' @return a matrix of concensus patterns by samples. If \code{bySet=TRUE} then a list of the set contributions to each

 #' concensus pattern is also returned.

-#' @export

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

 patternMatch4Parallel <- function(Ptot, nSets, cnt, minNS, 

 cluster.method="complete", ignore.NA=FALSE, bySet=FALSE, ...)

@@ -5,7 +5,6 @@

 #' @param order optional vector indicating order of samples for plotting. Default is NULL.

 #' @param sample.color optional vector of colors of same length as colnames. Default is NULL.

 #' @return either an index of selected sets' contributions or the editted \code{PBySet} object

-#' @export

 patternMatcher<-function(PBySet=NULL,out=NULL,order=NULL, sample.color=NULL)

 {

     runApp(list(

@@ -11,8 +11,8 @@

 #' # Load the sample data from CoGAPS

 #' data(SimpSim)

 #' # Run plotAtoms

-#' plotAtoms(results,type="sampA")

-#'@export

+#' plotAtoms(SimpSim.result, type="sampA")

+#' @export

 plotAtoms<-function(gapsRes, type='sampA')

 {

     if (type == 'sampA')       atoms <- gapsRes$atomsASamp

@@ -12,15 +12,6 @@

 #' @param ... additional graphical parameters to be passed to \code{heatmap.2}

 #' @return heatmap of the \code{data} values for the \code{patternMarkers}

 #' @seealso  \code{\link{heatmap.2}}

-#' @examples

-#' # Load the sample data from CoGAPS

-#' data(SimpSim)

-#' # Run patternMarkers and save the outputs

-#' PM <- patternMarkers(Amatrix=results$Amean,scaledPmatrix=FALSE,

-#' Pmatrix=results$Pmean,threshold="all",full=TRUE)

-#' # Run plotPatternMarkers with the correct argument from 'PM'

-#' plotPatternMarkers(data=SimpSim.D,patternMarkers=PM$PatternMarkers)

-#' @export

 plotPatternMarkers <- function(data=NA, patternMarkers=NA, patternPalette=NA,

 sampleNames=NA, samplePalette=NULL, colDenogram=TRUE, heatmapCol="bluered",

 scale='row', ...)

@@ -17,7 +17,6 @@

 #' `main') and graphical parameters (see `par') which are passed to

 #' `plot.window()', `title()' and `axis'.

 #' @return plot

-#' @export

 plotSmoothPatterns <- function(P, x=NULL, breaks=NULL, breakStyle=TRUE,

 orderP=!all(is.null(x)), plotPTS=FALSE, pointCol='black', lineCol='grey',

 add=FALSE, ...)

@@ -5,7 +5,6 @@

 #' for gapsMapRun

 #' @return list of two data.frames containing the A matrix estimates or their

 #' corresponding standard deviations from output of parallel CoGAPS

-#' @export

 postFixed4Parallel <- function(AP.fixed=NA, setPs=NA)

 {

     ASummary <- do.call(rbind,lapply(AP.fixed, function(x) x$Amean))

@@ -7,7 +7,6 @@

 #' @param nSets number of sets

 #' @return a list containing the \code{nSets} sets solution for Amean under "A",

 #' Pmean under "P", and Asd under "Asd"

-#' @export

 reOrderBySet<-function(AP, nFactor, nSets)

 {

     P<-do.call(rbind,lapply(AP, function(x) x$Pmean))

@@ -3,7 +3,6 @@

 #' @param P matrix to be matched

 #' @param matchTo matrix to match P to

 #' @return matched patterns

-#' @export

 reorderByPatternMatch <- function(P, matchTo)

 {

     # check that P and the matchTo matrix have the same dimensions

@@ -26,11 +26,4 @@ calculates the gene set statistics for each

 column of A using a Z-score from the elements of the A matrix,

 the input gene set, and permutation tests

 }

-\examples{

-# Load the sample data from CoGAPS

-data(SimpSim)

-# Run calcCoGAPSStat with the correct arguments from 'results'

-calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd,

-GStoGenes=GSets, numPerm=500)

-}

@@ -31,11 +31,4 @@ calculates the probability that a gene

 listed in a gene set behaves like other genes in the set within

 the given data set

 }

-\examples{

-# Load the sample data from CoGAPS

-data('SimpSim')

-# Run calcGeneGSStat

-calcGeneGSStat(SimpSim.result$Amean, SimpSim.result$Asd, 

-GSGenes=GSets[[1]], numPerm=500, nullGenes=TRUE)

-}

@@ -34,11 +34,4 @@ membership for each candidate gene in a set specified in \code{GSGenes} by

 comparing the inferred activity of that gene to the average activity of the

 set.

 }

-\examples{

-# Load the sample data from CoGAPS

-data('SimpSim')

-# Run calcGeneGSStat with the correct arguments from 'results'

-calcGeneGSStat(SimpSim.result$Amean, SimpSim.result$Asd, 

-GSGenes=GSets[[1]], numPerm=500)

-}

@@ -71,4 +71,10 @@ list with A and P matrix estimates

 \description{

 Backwards Compatibility with v2

 }

+\examples{

+data(SimpSim)

+nC <- ncol(SimpSim.D)

+patterns <- matrix(runif(nC, 0, 1), nrow=1, ncol=nC)

+result <- gapsMapRun(SimpSim.D, SimpSim.S, FP=patterns, nFactor=3)

+}

@@ -64,4 +64,8 @@ list with A and P matrix estimates

 \description{

 Backwards Compatibility with v2

 }

+\examples{

+data(SimpSim)

+result <- gapsRun(SimpSim.D, SimpSim.S, nFactor=3)

+}

@@ -18,7 +18,4 @@ vector of randomly generated seeds

 \description{

 Generate Seeds for Multiple Concurrent Runs

 }

-\examples{

-seeds <- generateSeeds(chains=2, seed=-1)

-}

@@ -27,11 +27,4 @@ genes rankings with a column for each \code{lp} will also be returned.

 \description{

 patternMarkers

 }

-\examples{

-# Load the sample data from CoGAPS

-data(SimpSim)

-# Run patternMarkers with the correct arguments from 'results'

-patternMarkers(Amatrix=results$Amean,scaledPmatrix=FALSE,

-Pmatrix=results$Pmean,threshold="all",full=TRUE)

-}

@@ -27,6 +27,6 @@ from one of the vectors returned with atom numbers

 # Load the sample data from CoGAPS

 data(SimpSim)

 # Run plotAtoms

-plotAtoms(results,type="sampA")

+plotAtoms(SimpSim.result, type="sampA")

 }

@@ -34,15 +34,6 @@ heatmap of the \code{data} values for the \code{patternMarkers}

 \description{

 plotPatternMarkers

 }

-\examples{

-# Load the sample data from CoGAPS

-data(SimpSim)

-# Run patternMarkers and save the outputs

-PM <- patternMarkers(Amatrix=results$Amean,scaledPmatrix=FALSE,

-Pmatrix=results$Pmean,threshold="all",full=TRUE)

-# Run plotPatternMarkers with the correct argument from 'PM'

-plotPatternMarkers(data=SimpSim.D,patternMarkers=PM$PatternMarkers)

-}

 \seealso{

 \code{\link{heatmap.2}}

 }

@@ -69,10 +69,7 @@ Rcpp::NumericMatrix GibbsSampler::getNormedMatrix(char mat)

     for (unsigned r = 0; r < mPMatrix.nRow(); ++r)

     {

         normVec[r] = gaps::algo::sum(mPMatrix.getRow(r));

-        if (normVec[r] == 0)

-        {

-            normVec[r] = 1.0;

-        }

+        normVec[r] = (normVec[r] == 0) ? 1.f : normVec[r];

     }

     if (mat == 'A')