deleted file mode 100644

@@ -1,34 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/calcCoGAPSStat.R

-\name{calcCoGAPSStat}

-\alias{calcCoGAPSStat}

-\title{Calculate Gene Set Statistics}

-\usage{

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

-}

-\arguments{

-\item{Amean}{A matrix mean values}

-

-\item{Asd}{A matrix standard deviations}

-

-\item{GStoGenes}{data.frame or list with gene sets}

-

-\item{numPerm}{number of permutations for null}

-}

-\value{

-gene set statistics for each column of A

-}

-\description{

-Calculate Gene Set Statistics

-}

-\details{

-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{

-data('SimpSim')

-calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd, GStoGenes=GSets,

-numPerm=500)

-}

-

new file mode 100644

@@ -0,0 +1,34 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/calcCoGAPSStat.R

+\name{calcCoGAPSStat}

+\alias{calcCoGAPSStat}

+\title{Calculate Gene Set Statistics}

+\usage{

+calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

+}

+\arguments{

+\item{Amean}{A matrix mean values}

+

+\item{Asd}{A matrix standard deviations}

+

+\item{GStoGenes}{data.frame or list with gene sets}

+

+\item{numPerm}{number of permutations for null}

+}

+\value{

+gene set statistics for each column of A

+}

+\description{

+Calculate Gene Set Statistics

+}

+\details{

+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{

+data('SimpSim')

+calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd, GStoGenes=GSets,

+numPerm=500)

+}

+

deleted file mode 100644

@@ -1,34 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/calcCoGAPSStat.R

-\name{calcCoGAPSStat}

-\alias{calcCoGAPSStat}

-\title{Calculate Gene Set Statistics}

-\usage{

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

-}

-\arguments{

-\item{Amean}{A matrix mean values}

-

-\item{Asd}{A matrix standard deviations}

-

-\item{GStoGenes}{data.frame or list with gene sets}

-

-\item{numPerm}{number of permutations for null}

-}

-\value{

-gene set statistics for each column of A

-}

-\description{

-Calculate Gene Set Statistics

-}

-\details{

-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{

-data('SimpSim')

-calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd, GStoGenes=GSets,

-numPerm=500)

-}

-

@@ -26,4 +26,9 @@ 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{

+data('SimpSim')

+calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd, GStoGenes=GSets,

+numPerm=500)

+}

@@ -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)

-}

@@ -15,20 +15,22 @@ calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

 \item{numPerm}{number of permutations for null}

 }

+\value{

+gene set statistics for each column of A

+}

 \description{

 Calculate Gene Set Statistics

 }

 \details{

 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

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

+the input gene set, and permutation tests

 }

 \examples{

-# Load the simulated data

-data('SimpSim')

-# Load the outputs from gapsRun

-data('results')

+# Load the sample data from CoGAPS

+data(SimpSim)

 # Run calcCoGAPSStat with the correct arguments from 'results'

-calcCoGAPSStat(results$Amean,results$Asd,GStoGenes=GSets,numPerm=500)

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

+GStoGenes=GSets, numPerm=500)

 }

@@ -2,9 +2,7 @@

 % Please edit documentation in R/calcCoGAPSStat.R

 \name{calcCoGAPSStat}

 \alias{calcCoGAPSStat}

-\title{\code{calcCoGAPSStat} 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}

+\title{Calculate Gene Set Statistics}

 \usage{

 calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

 }

@@ -18,8 +16,11 @@ calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

 \item{numPerm}{number of permutations for null}

 }

 \description{

-\code{calcCoGAPSStat} 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

+Calculate Gene Set Statistics

+}

+\details{

+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

 }

@@ -22,4 +22,11 @@ calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

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

 the input gene set, and permutation tests

 }

-

+\examples{

+# Load the simulated data

+data('SimpSim')

+# Load the outputs from gapsRun

+data('results')

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

+calcCoGAPSStat(results$Amean,results$Asd,GStoGenes=GSets,numPerm=500)

+}

@@ -22,3 +22,4 @@ calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

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

 the input gene set, and permutation tests

 }

+

@@ -22,4 +22,3 @@ calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

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

 the input gene set, and permutation tests

 }

-

@@ -1,36 +1,25 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/calcCoGAPSStat.R

 \name{calcCoGAPSStat}

 \alias{calcCoGAPSStat}

-\title{CoGAPS gene set statistic}

-

-\description{

-Computes the p-value for the association of underlying patterns from microarray data to activity in gene sets.}

-

+\title{\code{calcCoGAPSStat} 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}

 \usage{

-  calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm=500)}

-

-\arguments{

-\item{Amean}{Sampled mean value of the amplitude matrix \eqn{{{A}}}.  \code{row.names(Amean)} must correspond to the gene names contained in GStoGenes.}

-\item{Asd}{Sampled standard deviation of the amplitude matrix \eqn{{{A}}}.}

-\item{GStoGenes}{List or data frame containing the genes in each gene set. If a list, gene set names are the list names and corresponding elements are the names of genes contained in each set. If a data frame, gene set names are in the first column and corresponding gene names are listed in rows beneath each gene set name.}

-\item{numPerm}{Number of permuations used for the null distribution in the gene set statistic. (optional; default=500)}

-}

-

-\details{

-  This script links the patterns identified in the columns of \eqn{{P}}  to activity in each of the gene sets specified in GStoGenes using a novel z-score based statistic developed in Ochs et al. (2009).  Specifically, the z-score for pattern \eqn{p} and gene set \eqn{G_{i}} containing \eqn{G} total genes is given by \deqn{Z_{i,p} = \frac{1}{G} \sum_{g in G_{i}}A_{gp} / \sigma_{gp}}, where \eqn{g} indexes the genes in the set and \eqn{\sigma_{gp}} is the standard deviation of \eqn{{{A}}_{gp}} obtained from MCMC sampling.  CoGAPS then uses the specified \code{numPerm} random sample tests to compute a consistent p value estimate from that z score.

+calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500)

 }

+\arguments{

+\item{Amean}{A matrix mean values}

-\value{

-  A list containing:

-  \item{GSUpreg}{p-values for upregulation of each gene set in each pattern.}

-  \item{GSDownreg}{p-values for downregulation of each gene set in each pattern.}

-  \item{GSActEst}{p-values for activity of each gene set in each pattern.}

-}

+\item{Asd}{A matrix standard deviations}

-\author{Elana J. Fertig \email{ejfertig@jhmi.edu}}

+\item{GStoGenes}{data.frame or list with gene sets}

-\references{

-M.F. Ochs, L. Rink, C. Tarn, S. Mburu, T. Taguchi, B. Eisenberg, and A.K. Godwin.  (2009) Detection and treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data.  Cancer Research, 69:9125-9132.

+\item{numPerm}{number of permutations for null}

+}

+\description{

+\code{calcCoGAPSStat} 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

 }

-\seealso{\code{\link{CoGAPS}}}

-\keyword{misc}

@@ -32,5 +32,5 @@ Computes the p-value for the association of underlying patterns from microarray

 M.F. Ochs, L. Rink, C. Tarn, S. Mburu, T. Taguchi, B. Eisenberg, and A.K. Godwin.  (2009) Detection and treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data.  Cancer Research, 69:9125-9132.

 }

-\seealso{\code{\link{CoGAPS}}, \code{\link{GAPS}}}

+\seealso{\code{\link{CoGAPS}}}

 \keyword{misc}

@@ -9,14 +9,14 @@ Computes the p-value for the association of underlying patterns from microarray

   calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm=500)}

 \arguments{

-\item{Amean}{Sampled mean value of the amplitude matrix \eqn{{\bf{A}}}.  \code{row.names(Amean)} must correspond to the gene names contained in GStoGenes.}

-\item{Asd}{Sampled standard deviation of the amplitude matrix \eqn{{\bf{A}}}.}

+\item{Amean}{Sampled mean value of the amplitude matrix \eqn{{{A}}}.  \code{row.names(Amean)} must correspond to the gene names contained in GStoGenes.}

+\item{Asd}{Sampled standard deviation of the amplitude matrix \eqn{{{A}}}.}

 \item{GStoGenes}{List or data frame containing the genes in each gene set. If a list, gene set names are the list names and corresponding elements are the names of genes contained in each set. If a data frame, gene set names are in the first column and corresponding gene names are listed in rows beneath each gene set name.}

 \item{numPerm}{Number of permuations used for the null distribution in the gene set statistic. (optional; default=500)}

 }

 \details{

-  This script links the patterns identified in the columns of \eqn{\bf{P}}  to activity in each of the gene sets specified in GStoGenes using a novel z-score based statistic developed in Ochs et al. (2009).  Specifically, the z-score for pattern \eqn{p} and gene set \eqn{G_{i}} containing $G$ total genes is given by \deqn{Z_{i,p} = \frac{1}{G} \sum_{g in \mathcal{G_{i}}} {\frac{{\bf{A}_{gp}}}{\sigma_{gp}}},} where \eqn{g} indexes the genes in the set and \eqn{\sigma_{gp}} is the standard deviation of \eqn{{\bf{A}}_{gp}} obtained from MCMC sampling.  CoGAPS then uses the specified \code{numPerm} random sample tests to compute a consistent p value estimate from that z score.

+  This script links the patterns identified in the columns of \eqn{{P}}  to activity in each of the gene sets specified in GStoGenes using a novel z-score based statistic developed in Ochs et al. (2009).  Specifically, the z-score for pattern \eqn{p} and gene set \eqn{G_{i}} containing \eqn{G} total genes is given by \deqn{Z_{i,p} = \frac{1}{G} \sum_{g in G_{i}}A_{gp} / \sigma_{gp}}, where \eqn{g} indexes the genes in the set and \eqn{\sigma_{gp}} is the standard deviation of \eqn{{{A}}_{gp}} obtained from MCMC sampling.  CoGAPS then uses the specified \code{numPerm} random sample tests to compute a consistent p value estimate from that z score.

 }

 \value{

new file mode 100644

@@ -0,0 +1,36 @@

+\name{calcCoGAPSStat}

+\alias{calcCoGAPSStat}

+\title{CoGAPS gene set statistic}

+

+\description{

+Computes the p-value for the association of underlying patterns from microarray data to activity in gene sets.}

+

+\usage{

+  calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm=500)}

+

+\arguments{

+\item{Amean}{Sampled mean value of the amplitude matrix \eqn{{\bf{A}}}.  \code{row.names(Amean)} must correspond to the gene names contained in GStoGenes.}

+\item{Asd}{Sampled standard deviation of the amplitude matrix \eqn{{\bf{A}}}.}

+\item{GStoGenes}{List or data frame containing the genes in each gene set. If a list, gene set names are the list names and corresponding elements are the names of genes contained in each set. If a data frame, gene set names are in the first column and corresponding gene names are listed in rows beneath each gene set name.}

+\item{numPerm}{Number of permuations used for the null distribution in the gene set statistic. (optional; default=500)}

+}

+

+\details{

+  This script links the patterns identified in the columns of \eqn{\bf{P}}  to activity in each of the gene sets specified in GStoGenes using a novel z-score based statistic developed in Ochs et al. (2009).  Specifically, the z-score for pattern \eqn{p} and gene set \eqn{G_{i}} containing $G$ total genes is given by \deqn{Z_{i,p} = \frac{1}{G} \sum_{g in \mathcal{G_{i}}} {\frac{{\bf{A}_{gp}}}{\sigma_{gp}}},} where \eqn{g} indexes the genes in the set and \eqn{\sigma_{gp}} is the standard deviation of \eqn{{\bf{A}}_{gp}} obtained from MCMC sampling.  CoGAPS then uses the specified \code{numPerm} random sample tests to compute a consistent p value estimate from that z score.

+}

+

+\value{

+  A list containing:

+  \item{GSUpreg}{p-values for upregulation of each gene set in each pattern.}

+  \item{GSDownreg}{p-values for downregulation of each gene set in each pattern.}

+  \item{GSActEst}{p-values for activity of each gene set in each pattern.}

+}

+

+\author{Elana J. Fertig \email{ejfertig@jhmi.edu}}

+

+\references{

+M.F. Ochs, L. Rink, C. Tarn, S. Mburu, T. Taguchi, B. Eisenberg, and A.K. Godwin.  (2009) Detection and treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data.  Cancer Research, 69:9125-9132.

+}

+

+\seealso{\code{\link{CoGAPS}}, \code{\link{GAPS}}}

+\keyword{misc}