Tom Sherman authored on 31/05/2018 21:34:33 | 1 | 1 |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/package.R |
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+\docType{package}
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+\name{CoGAPS-package}
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+\alias{CoGAPS-package}
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+\title{CoGAPS: Coordinated Gene Activity in Pattern Sets}
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+\description{
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+CoGAPS implements a Bayesian MCMC matrix factorization algorithm, |
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+GAPS, and links it to gene set statistic methods to infer |
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+biological process activity. It can be used to perform |
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+sparse matrix factorization on any data, and when this |
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+data represents biomolecules, to do gene set analysis. |
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+\tabular{ll}{
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+Package: \tab CoGAPS\cr |
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+Type: \tab Package\cr |
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+Version: \tab 2.99.0\cr |
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+Date: \tab 2018-01-24\cr |
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+License: \tab LGPL\cr |
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+} |
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+} |
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+\author{
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+Maintainer: Elana J. Fertig \email{ejfertig@jhmi.edu},
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+ Michael F. Ochs \email{ochsm@tcnj.edu}
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+} |
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+\references{
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+Fertig EJ, Ding J, Favorov AV, Parmigiani G, Ochs MF. |
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+CoGAPS: an R/C++ package to identify patterns and biological |
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+process activity in transcriptomic data. |
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+Bioinformatics. 2010 Nov 1;26(21):2792-3 |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/CoGAPS.R |
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+\name{CoGAPS}
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+\alias{CoGAPS}
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+\title{CoGAPS Matrix Factorization Algorithm}
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+\usage{
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+CoGAPS(D, S, nFactor = 7, nEquil = 1000, nSample = 1000, |
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+ nOutputs = 1000, nSnapshots = 0, alphaA = 0.01, alphaP = 0.01, |
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+ maxGibbmassA = 100, maxGibbmassP = 100, seed = -1, messages = TRUE, |
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+ singleCellRNASeq = FALSE, whichMatrixFixed = "N", |
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+ fixedPatterns = matrix(0), checkpointInterval = 0, |
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+ checkpointFile = "gaps_checkpoint.out", nCores = 1, ...) |
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+} |
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+\arguments{
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+\item{D}{data matrix}
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+ |
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+\item{S}{uncertainty matrix (std devs for chi-squared of Log Likelihood)}
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+ |
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+\item{nFactor}{number of patterns (basis vectors, metagenes), which must be
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+greater than or equal to the number of rows of FP} |
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+ |
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+\item{nEquil}{number of iterations for burn-in}
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+ |
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+\item{nSample}{number of iterations for sampling}
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+ |
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+\item{nOutputs}{how often to print status into R by iterations}
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+ |
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+\item{nSnapshots}{the number of individual samples to capture}
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+ |
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+\item{alphaA}{sparsity parameter for A domain}
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+ |
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+\item{alphaP}{sparsity parameter for P domain}
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+ |
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+\item{maxGibbmassA}{limit truncated normal to max size}
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+ |
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+\item{maxGibbmassP}{limit truncated normal to max size}
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+ |
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+\item{seed}{a positive seed is used as-is, while any negative seed tells
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+the algorithm to pick a seed based on the current time} |
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+ |
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+\item{messages}{display progress messages}
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+ |
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+\item{singleCellRNASeq}{indicates if the data is single cell RNA-seq data}
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+ |
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+\item{whichMatrixFixed}{character to indicate whether A or P matric contains
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+the fixed patterns} |
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+ |
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+\item{fixedPatterns}{matrix of fixed values in either A or P matrix}
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+ |
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+\item{checkpointInterval}{time (in seconds) between creating a checkpoint}
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+ |
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+\item{checkpointFile}{name of the checkpoint file}
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+ |
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+\item{...}{keeps backwards compatibility with arguments from older versions}
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+} |
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+\value{
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+list with A and P matrix estimates |
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+} |
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+\description{
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+CoGAPS Matrix Factorization Algorithm |
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+} |
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+\details{
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+calls the C++ MCMC code and performs Bayesian |
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+matrix factorization returning the two matrices that reconstruct |
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+the data matrix |
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+} |
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+\examples{
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+data(SimpSim) |
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+result <- CoGAPS(SimpSim.D, SimpSim.S, nFactor=3, nOutputs=250) |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/CoGAPS.R |
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+\name{CoGAPSFromFile}
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+\alias{CoGAPSFromFile}
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+\title{CoGAPS with file input for matrix}
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+\usage{
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+CoGAPSFromFile(D) |
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+} |
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+\description{
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+CoGAPS with file input for matrix |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/CoGAPS.R |
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+\name{CoGapsFromCheckpoint}
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+\alias{CoGapsFromCheckpoint}
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+\title{Restart CoGAPS from Checkpoint File}
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+\usage{
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+CoGapsFromCheckpoint(D, S, path, checkpointFile = NA) |
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+} |
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+\arguments{
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+\item{D}{data matrix}
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+ |
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+\item{S}{uncertainty matrix}
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+ |
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+\item{path}{path to checkpoint file}
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+ |
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+\item{checkpointFile}{name for future checkpooints made}
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+} |
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+\value{
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+list with A and P matrix estimates |
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+} |
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+\description{
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+Restart CoGAPS from Checkpoint File |
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+} |
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+\details{
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+loads the state of a previous CoGAPS run from a file and |
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+ continues the run from that point |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/GWCoGAPS.R |
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+\name{GWCoGAPS}
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+\alias{GWCoGAPS}
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+\title{GWCoGAPS}
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+\usage{
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+GWCoGAPS(simulationName, nFactor, nCores = NA, cut = NA, minNS = NA, |
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+ manualMatch = FALSE, consensusPatterns = NULL, ...) |
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+} |
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+\arguments{
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+\item{simulationName}{name of this simulation}
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+ |
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+\item{nFactor}{number of patterns (basis vectors, metagenes), which must be
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+greater than or equal to the number of rows of FP} |
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+ |
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+\item{nCores}{number of cores for parallelization. If left to the default NA, nCores = nSets.}
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+ |
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+\item{cut}{number of branches at which to cut dendrogram used in patternMatch4Parallel}
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+ |
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+\item{minNS}{minimum of individual set contributions a cluster must contain}
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+ |
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+\item{manualMatch}{logical indicating whether or not to stop after initial phase for manual pattern matching}
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+ |
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+\item{consensusPatterns}{fixed pattern matrix to be used to ensure reciprocity of A weights accross sets}
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+ |
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+\item{...}{additional parameters to be fed into \code{gapsRun} and \code{gapsMapRun}}
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+} |
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+\value{
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+list of A and P estimates |
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+} |
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+\description{
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+GWCoGAPS |
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+} |
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+\details{
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+calls the C++ MCMC code and performs Bayesian |
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+matrix factorization returning the two matrices that reconstruct |
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+the data matrix for whole genome data; |
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+} |
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+\examples{
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+data(SimpSim) |
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+sim_name <- "example" |
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+createGWCoGAPSSets(SimpSim.D, SimpSim.S, nSets=2, sim_name) |
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+result <- GWCoGAPS(sim_name, nFactor=3, nEquil=200, nSample=200) |
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+} |
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+\seealso{
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+\code{\link{gapsRun}}, \code{\link{patternMatch4Parallel}}, and \code{\link{gapsMapRun}}
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/GWCoGAPS.R |
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+\name{GWCoGapsFromCheckpoint}
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+\alias{GWCoGapsFromCheckpoint}
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+\title{Restart a GWCoGaps Run from Checkpoint}
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+\usage{
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+GWCoGapsFromCheckpoint(simulationName, nCores, cut = NA, minNS = NA, ...) |
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+} |
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+\arguments{
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+\item{simulationName}{name of this simulation}
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+ |
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+\item{nCores}{number of cores for parallelization. If left to the default NA, nCores = nSets.}
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+ |
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+\item{cut}{number of branches at which to cut dendrogram used in patternMatch4Parallel}
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+ |
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+\item{minNS}{minimum of individual set contributions a cluster must contain}
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+ |
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+\item{...}{additional parameters to be fed into \code{gapsRun} and \code{gapsMapRun}}
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+} |
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+\value{
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+list of A and P estimates |
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+} |
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+\description{
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+Restart a GWCoGaps Run from Checkpoint |
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+} |
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+\examples{
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+data(SimpSim) |
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+sim_name <- "example" |
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+createGWCoGAPSSets(SimpSim.D, SimpSim.S, nSets=2, sim_name) |
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+trash <- GWCoGAPS(sim_name, nFactor=3, nEquil=200, nSample=200) |
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+result <- GWCoGapsFromCheckpoint(sim_name, 2) |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/binaryA.R |
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+\name{binaryA}
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+\alias{binaryA}
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+\title{Binary Heatmap for Standardized A Matrix}
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+\usage{
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+binaryA(Amean, Asd, threshold = 3) |
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+} |
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+\arguments{
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+\item{Amean}{the mean estimate for the A matrix}
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+ |
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+\item{Asd}{the standard deviations on Amean}
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+ |
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+\item{threshold}{the number of standard deviations above zero
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+that an element of Amean must be to get a value of 1} |
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+} |
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+\value{
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+plots a heatmap of the A Matrix |
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+} |
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+\description{
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+Binary Heatmap for Standardized A Matrix |
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+} |
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+\details{
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+creates a binarized heatmap of the A matrix |
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+in which the value is 1 if the value in Amean is greater than |
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+threshold * Asd and 0 otherwise |
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+} |
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+\examples{
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+data(SimpSim) |
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+binaryA(SimpSim.result$Amean, SimpSim.result$Asd, threshold=3) |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/CoGAPS.R |
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+\name{buildReport}
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+\alias{buildReport}
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+\title{Display Information About Package Compilation}
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+\usage{
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+buildReport() |
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+} |
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+\value{
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+display builds information |
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+} |
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+\description{
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+Display Information About Package Compilation |
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+} |
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+\details{
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+displays information about how the package was compiled, i.e. which |
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+ compiler/version was used, which compile time options were enabled, etc... |
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+} |
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+\examples{
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+ CoGAPS::buildReport() |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/calcCoGAPSStat.R |
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+\name{calcCoGAPSStat}
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+\alias{calcCoGAPSStat}
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+\title{Calculate Gene Set Statistics}
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+\usage{
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+calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm = 500) |
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+} |
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+\arguments{
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+\item{Amean}{A matrix mean values}
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+ |
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+\item{Asd}{A matrix standard deviations}
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+ |
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+\item{GStoGenes}{data.frame or list with gene sets}
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+ |
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+\item{numPerm}{number of permutations for null}
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+} |
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+\value{
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+gene set statistics for each column of A |
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+} |
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+\description{
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+Calculate Gene Set Statistics |
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+} |
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+\details{
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+calculates the gene set statistics for each |
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+column of A using a Z-score from the elements of the A matrix, |
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+the input gene set, and permutation tests |
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+} |
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+\examples{
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+data('SimpSim')
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+calcCoGAPSStat(SimpSim.result$Amean, SimpSim.result$Asd, GStoGenes=GSets, |
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+numPerm=500) |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/calcGeneGSStat.R |
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+\name{calcGeneGSStat}
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+\alias{calcGeneGSStat}
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+\title{Probability Gene Belongs in Gene Set}
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+\usage{
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+calcGeneGSStat(Amean, Asd, GSGenes, numPerm, Pw = rep(1, ncol(Amean)), |
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+ nullGenes = FALSE) |
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+} |
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+\arguments{
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+\item{Amean}{A matrix mean values}
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+ |
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+\item{Asd}{A matrix standard deviations}
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+ |
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+\item{GSGenes}{data.frame or list with gene sets}
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+ |
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+\item{numPerm}{number of permutations for null}
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+ |
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+\item{Pw}{weight on genes}
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+ |
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+\item{nullGenes}{logical indicating gene adjustment}
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+} |
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+\value{
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+gene similiarity statistic |
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+} |
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+\description{
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+Probability Gene Belongs in Gene Set |
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+} |
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+\details{
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+calculates the probability that a gene |
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+listed in a gene set behaves like other genes in the set within |
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+the given data set |
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+} |
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+\examples{
|
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+data("SimpSim")
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+calcGeneGSStat(SimpSim.result$Amean, SimpSim.result$Asd, GSGenes=GSets[[1]], |
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+numPerm=500) |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/calcZ.R |
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+\name{calcZ}
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+\alias{calcZ}
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+\title{Compute Z-Score Matrix}
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+\usage{
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+calcZ(meanMat, sdMat) |
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+} |
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+\arguments{
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+\item{meanMat}{matrix of mean values}
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+ |
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+\item{sdMat}{matrix of standard deviation values}
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+} |
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+\value{
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+matrix of z-scores |
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+} |
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+\description{
|
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+Compute Z-Score Matrix |
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+} |
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+\details{
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+calculates the Z-score for each element based on input mean |
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+and standard deviation matrices |
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+} |
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+\examples{
|
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+data(SimpSim) |
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+calcZ(SimpSim.result$Amean, SimpSim.result$Asd) |
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+} |
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+ |
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+% Generated by roxygen2: do not edit by hand |
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+% Please edit documentation in R/cellMatchR.R |
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+\name{cellMatchR}
|
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+\alias{cellMatchR}
|
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+\title{cellMatchR}
|
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+\usage{
|
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+cellMatchR(Atot, nSets, cnt, minNS = NA, maxNS = NA, ignore.NA = FALSE, |
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+ bySet = FALSE, plotDen = FALSE, ...) |
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+} |
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| 10 |
+\arguments{
|
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| 11 |
+\item{Atot}{a matrix containing the total by set estimates of Pmean output from \code{reOrderBySet}}
|
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+ |
|
| 13 |
+\item{nSets}{number of parallel sets used to generate \code{Atot}}
|
|
| 14 |
+ |
|
| 15 |
+\item{cnt}{number of branches at which to cut dendrogram}
|
|
| 16 |
+ |
|
| 17 |
+\item{minNS}{minimum of individual set contributions a cluster must contain}
|
|
| 18 |
+ |
|
| 19 |
+\item{maxNS}{maximum of individual set contributions a cluster must contain}
|
|
| 20 |
+ |
|
| 21 |
+\item{ignore.NA}{logical indicating whether or not to ignore NAs from potential over dimensionalization. Default is FALSE.}
|
|
| 22 |
+ |
|
| 23 |
+\item{bySet}{logical indicating whether to return list of matched set solutions from \code{Atot}}
|
|
| 24 |
+ |
|
| 25 |
+\item{plotDen}{plot}
|
|
| 26 |
+ |
|
| 27 |
+\item{...}{additional parameters for \code{agnes}}
|
|
| 28 |
+} |
|
| 29 |
+\value{
|
|
| 30 |
+a matrix of concensus patterns by samples. If \code{bySet=TRUE} then a list of the set contributions to each
|
|
| 31 |
+concensus pattern is also returned. |
|
| 32 |
+} |
|
| 33 |
+\description{
|
|
| 34 |
+cellMatchR |
|
| 35 |
+} |
|
| 36 |
+ |
| 0 | 37 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,42 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/calcGeneGSStat.R |
|
| 3 |
+\name{computeGeneGSProb}
|
|
| 4 |
+\alias{computeGeneGSProb}
|
|
| 5 |
+\title{Compute Gene Probability}
|
|
| 6 |
+\usage{
|
|
| 7 |
+computeGeneGSProb(Amean, Asd, GSGenes, Pw = rep(1, ncol(Amean)), |
|
| 8 |
+ numPerm = 500, PwNull = FALSE) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{Amean}{A matrix mean values}
|
|
| 12 |
+ |
|
| 13 |
+\item{Asd}{A matrix standard deviations}
|
|
| 14 |
+ |
|
| 15 |
+\item{GSGenes}{data.frame or list with gene sets}
|
|
| 16 |
+ |
|
| 17 |
+\item{Pw}{weight on genes}
|
|
| 18 |
+ |
|
| 19 |
+\item{numPerm}{number of permutations for null}
|
|
| 20 |
+ |
|
| 21 |
+\item{PwNull}{- logical indicating gene adjustment}
|
|
| 22 |
+} |
|
| 23 |
+\value{
|
|
| 24 |
+A vector of length GSGenes containing the p-values of set membership |
|
| 25 |
+for each gene containined in the set specified in GSGenes. |
|
| 26 |
+} |
|
| 27 |
+\description{
|
|
| 28 |
+Compute Gene Probability |
|
| 29 |
+} |
|
| 30 |
+\details{
|
|
| 31 |
+Computes the p-value for gene set membership using the CoGAPS-based |
|
| 32 |
+statistics developed in Fertig et al. (2012). This statistic refines set |
|
| 33 |
+membership for each candidate gene in a set specified in \code{GSGenes} by
|
|
| 34 |
+comparing the inferred activity of that gene to the average activity of the |
|
| 35 |
+set. |
|
| 36 |
+} |
|
| 37 |
+\examples{
|
|
| 38 |
+data("SimpSim")
|
|
| 39 |
+computeGeneGSProb(SimpSim.result$Amean, SimpSim.result$Asd, GSGenes=GSets[[1]], |
|
| 40 |
+numPerm=500) |
|
| 41 |
+} |
|
| 42 |
+ |
| 0 | 43 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,31 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/createGWCoGAPSSets.R |
|
| 3 |
+\name{createGWCoGAPSSets}
|
|
| 4 |
+\alias{createGWCoGAPSSets}
|
|
| 5 |
+\title{Create Gene Sets for GWCoGAPS}
|
|
| 6 |
+\usage{
|
|
| 7 |
+createGWCoGAPSSets(D, S, nSets, simulationName) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{D}{data matrix}
|
|
| 11 |
+ |
|
| 12 |
+\item{S}{uncertainty matrix}
|
|
| 13 |
+ |
|
| 14 |
+\item{nSets}{number of sets to partition the data into}
|
|
| 15 |
+ |
|
| 16 |
+\item{simulationName}{name used to identify files created by this simulation}
|
|
| 17 |
+} |
|
| 18 |
+\value{
|
|
| 19 |
+simulationName used to identify saved files |
|
| 20 |
+} |
|
| 21 |
+\description{
|
|
| 22 |
+Create Gene Sets for GWCoGAPS |
|
| 23 |
+} |
|
| 24 |
+\details{
|
|
| 25 |
+factors whole genome data into randomly generated sets for indexing |
|
| 26 |
+} |
|
| 27 |
+\examples{
|
|
| 28 |
+data(SimpSim) |
|
| 29 |
+createGWCoGAPSSets(SimpSim.D, SimpSim.S, nSets=2, "example") |
|
| 30 |
+} |
|
| 31 |
+ |
| 0 | 32 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,33 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/createscCoGAPSSets.R |
|
| 3 |
+\name{createscCoGAPSSets}
|
|
| 4 |
+\alias{createscCoGAPSSets}
|
|
| 5 |
+\title{Create Gene Sets for scCoGAPS}
|
|
| 6 |
+\usage{
|
|
| 7 |
+createscCoGAPSSets(D, nSets, simulationName, samplingRatio = NULL, |
|
| 8 |
+ path = "", anotionObj = NULL) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{D}{data matrix}
|
|
| 12 |
+ |
|
| 13 |
+\item{nSets}{number of sets to partition the data into}
|
|
| 14 |
+ |
|
| 15 |
+\item{simulationName}{name used to identify files created by this simulation}
|
|
| 16 |
+ |
|
| 17 |
+\item{samplingRatio}{vector of relative quantities to use for sampling celltypes}
|
|
| 18 |
+ |
|
| 19 |
+\item{path}{character string indicating were to save resulting data objects. default is current working dir}
|
|
| 20 |
+ |
|
| 21 |
+\item{anotionObj}{vector of same length as number of columns of D}
|
|
| 22 |
+} |
|
| 23 |
+\value{
|
|
| 24 |
+simulationName used to identify saved files |
|
| 25 |
+} |
|
| 26 |
+\description{
|
|
| 27 |
+factors whole genome data into randomly generated sets for indexing |
|
| 28 |
+} |
|
| 29 |
+\examples{
|
|
| 30 |
+data(SimpSim) |
|
| 31 |
+createscCoGAPSSets(SimpSim.D, nSets=2, simulationName="example") |
|
| 32 |
+} |
|
| 33 |
+ |
| 0 | 34 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,80 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/CoGAPS.R |
|
| 3 |
+\name{gapsMapRun}
|
|
| 4 |
+\alias{gapsMapRun}
|
|
| 5 |
+\title{Backwards Compatibility with v2}
|
|
| 6 |
+\usage{
|
|
| 7 |
+gapsMapRun(D, S, FP, ABins = data.frame(), PBins = data.frame(), |
|
| 8 |
+ nFactor = 5, simulation_id = "simulation", nEquil = 1000, |
|
| 9 |
+ nSample = 1000, nOutR = 1000, output_atomic = FALSE, |
|
| 10 |
+ fixedMatrix = "P", fixedBinProbs = FALSE, fixedDomain = "N", |
|
| 11 |
+ sampleSnapshots = TRUE, numSnapshots = 100, alphaA = 0.01, |
|
| 12 |
+ nMaxA = 1e+05, max_gibbmass_paraA = 100, alphaP = 0.01, nMaxP = 1e+05, |
|
| 13 |
+ max_gibbmass_paraP = 100, seed = -1, messages = TRUE) |
|
| 14 |
+} |
|
| 15 |
+\arguments{
|
|
| 16 |
+\item{D}{data matrix}
|
|
| 17 |
+ |
|
| 18 |
+\item{S}{uncertainty matrix}
|
|
| 19 |
+ |
|
| 20 |
+\item{FP}{data.frame with rows giving fixed patterns for P}
|
|
| 21 |
+ |
|
| 22 |
+\item{ABins}{unused}
|
|
| 23 |
+ |
|
| 24 |
+\item{PBins}{unused}
|
|
| 25 |
+ |
|
| 26 |
+\item{nFactor}{number of patterns (basis vectors, metagenes), which must be
|
|
| 27 |
+greater than or equal to the number of rows of FP} |
|
| 28 |
+ |
|
| 29 |
+\item{simulation_id}{unused}
|
|
| 30 |
+ |
|
| 31 |
+\item{nEquil}{number of iterations for burn-in}
|
|
| 32 |
+ |
|
| 33 |
+\item{nSample}{number of iterations for sampling}
|
|
| 34 |
+ |
|
| 35 |
+\item{nOutR}{number of output messages}
|
|
| 36 |
+ |
|
| 37 |
+\item{output_atomic}{unused}
|
|
| 38 |
+ |
|
| 39 |
+\item{fixedMatrix}{unused}
|
|
| 40 |
+ |
|
| 41 |
+\item{fixedBinProbs}{unused}
|
|
| 42 |
+ |
|
| 43 |
+\item{fixedDomain}{unused}
|
|
| 44 |
+ |
|
| 45 |
+\item{sampleSnapshots}{indicates if snapshots should be made}
|
|
| 46 |
+ |
|
| 47 |
+\item{numSnapshots}{how many snapshots to take}
|
|
| 48 |
+ |
|
| 49 |
+\item{alphaA}{sparsity parameter for A domain}
|
|
| 50 |
+ |
|
| 51 |
+\item{nMaxA}{unused}
|
|
| 52 |
+ |
|
| 53 |
+\item{max_gibbmass_paraA}{limit truncated normal to max size}
|
|
| 54 |
+ |
|
| 55 |
+\item{alphaP}{sparsity parameter for P domain}
|
|
| 56 |
+ |
|
| 57 |
+\item{nMaxP}{unused}
|
|
| 58 |
+ |
|
| 59 |
+\item{max_gibbmass_paraP}{limit truncated normal to max size}
|
|
| 60 |
+ |
|
| 61 |
+\item{seed}{a positive seed is used as-is, while any negative seed tells
|
|
| 62 |
+the algorithm to pick a seed based on the current time} |
|
| 63 |
+ |
|
| 64 |
+\item{messages}{display progress messages}
|
|
| 65 |
+ |
|
| 66 |
+\item{...}{v2 style parameters}
|
|
| 67 |
+} |
|
| 68 |
+\value{
|
|
| 69 |
+list with A and P matrix estimates |
|
| 70 |
+} |
|
| 71 |
+\description{
|
|
| 72 |
+Backwards Compatibility with v2 |
|
| 73 |
+} |
|
| 74 |
+\examples{
|
|
| 75 |
+data(SimpSim) |
|
| 76 |
+nC <- ncol(SimpSim.D) |
|
| 77 |
+patterns <- matrix(1:nC/nC, nrow=1, ncol=nC) |
|
| 78 |
+result <- gapsMapRun(SimpSim.D, SimpSim.S, FP=patterns, nFactor=3) |
|
| 79 |
+} |
|
| 80 |
+ |
| 0 | 81 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,71 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/CoGAPS.R |
|
| 3 |
+\name{gapsRun}
|
|
| 4 |
+\alias{gapsRun}
|
|
| 5 |
+\title{Backwards Compatibility with v2}
|
|
| 6 |
+\usage{
|
|
| 7 |
+gapsRun(D, S, ABins = data.frame(), PBins = data.frame(), nFactor = 7, |
|
| 8 |
+ simulation_id = "simulation", nEquil = 1000, nSample = 1000, |
|
| 9 |
+ nOutR = 1000, output_atomic = FALSE, fixedBinProbs = FALSE, |
|
| 10 |
+ fixedDomain = "N", sampleSnapshots = TRUE, numSnapshots = 100, |
|
| 11 |
+ alphaA = 0.01, nMaxA = 1e+05, max_gibbmass_paraA = 100, alphaP = 0.01, |
|
| 12 |
+ nMaxP = 1e+05, max_gibbmass_paraP = 100, seed = -1, messages = TRUE) |
|
| 13 |
+} |
|
| 14 |
+\arguments{
|
|
| 15 |
+\item{D}{data matrix}
|
|
| 16 |
+ |
|
| 17 |
+\item{S}{uncertainty matrix}
|
|
| 18 |
+ |
|
| 19 |
+\item{ABins}{unused}
|
|
| 20 |
+ |
|
| 21 |
+\item{PBins}{unused}
|
|
| 22 |
+ |
|
| 23 |
+\item{nFactor}{number of patterns (basis vectors, metagenes), which must be
|
|
| 24 |
+greater than or equal to the number of rows of FP} |
|
| 25 |
+ |
|
| 26 |
+\item{simulation_id}{unused}
|
|
| 27 |
+ |
|
| 28 |
+\item{nEquil}{number of iterations for burn-in}
|
|
| 29 |
+ |
|
| 30 |
+\item{nSample}{number of iterations for sampling}
|
|
| 31 |
+ |
|
| 32 |
+\item{nOutR}{number of output messages}
|
|
| 33 |
+ |
|
| 34 |
+\item{output_atomic}{unused}
|
|
| 35 |
+ |
|
| 36 |
+\item{fixedBinProbs}{unused}
|
|
| 37 |
+ |
|
| 38 |
+\item{fixedDomain}{unused}
|
|
| 39 |
+ |
|
| 40 |
+\item{sampleSnapshots}{indicates if snapshots should be made}
|
|
| 41 |
+ |
|
| 42 |
+\item{numSnapshots}{how many snapshots to take}
|
|
| 43 |
+ |
|
| 44 |
+\item{alphaA}{sparsity parameter for A domain}
|
|
| 45 |
+ |
|
| 46 |
+\item{nMaxA}{unused}
|
|
| 47 |
+ |
|
| 48 |
+\item{max_gibbmass_paraA}{limit truncated normal to max size}
|
|
| 49 |
+ |
|
| 50 |
+\item{alphaP}{sparsity parameter for P domain}
|
|
| 51 |
+ |
|
| 52 |
+\item{nMaxP}{unused}
|
|
| 53 |
+ |
|
| 54 |
+\item{max_gibbmass_paraP}{limit truncated normal to max size}
|
|
| 55 |
+ |
|
| 56 |
+\item{seed}{a positive seed is used as-is, while any negative seed tells
|
|
| 57 |
+the algorithm to pick a seed based on the current time} |
|
| 58 |
+ |
|
| 59 |
+\item{messages}{display progress messages}
|
|
| 60 |
+} |
|
| 61 |
+\value{
|
|
| 62 |
+list with A and P matrix estimates |
|
| 63 |
+} |
|
| 64 |
+\description{
|
|
| 65 |
+Backwards Compatibility with v2 |
|
| 66 |
+} |
|
| 67 |
+\examples{
|
|
| 68 |
+data(SimpSim) |
|
| 69 |
+result <- gapsRun(SimpSim.D, SimpSim.S, nFactor=3) |
|
| 70 |
+} |
|
| 71 |
+ |
| 0 | 72 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,21 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/generateSeeds.R |
|
| 3 |
+\name{generateSeeds}
|
|
| 4 |
+\alias{generateSeeds}
|
|
| 5 |
+\title{Generate Seeds for Multiple Concurrent Runs}
|
|
| 6 |
+\usage{
|
|
| 7 |
+generateSeeds(chains = 2, seed = -1) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{chains}{number of seeds to generate (number of chains to run)}
|
|
| 11 |
+ |
|
| 12 |
+\item{seed}{positive values are kept, negative values will be overwritten
|
|
| 13 |
+by a seed generated from the current time} |
|
| 14 |
+} |
|
| 15 |
+\value{
|
|
| 16 |
+vector of randomly generated seeds |
|
| 17 |
+} |
|
| 18 |
+\description{
|
|
| 19 |
+Generate Seeds for Multiple Concurrent Runs |
|
| 20 |
+} |
|
| 21 |
+ |
| 0 | 22 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,30 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/patternMarkers.R |
|
| 3 |
+\name{patternMarkers}
|
|
| 4 |
+\alias{patternMarkers}
|
|
| 5 |
+\title{patternMarkers}
|
|
| 6 |
+\usage{
|
|
| 7 |
+patternMarkers(Amatrix = NA, scaledPmatrix = FALSE, Pmatrix = NA, |
|
| 8 |
+ threshold = "all", lp = NA, full = FALSE) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{Amatrix}{A matrix of genes by weights resulting from CoGAPS or other NMF decomposition}
|
|
| 12 |
+ |
|
| 13 |
+\item{scaledPmatrix}{logical indicating whether the corresponding pattern matrix was fixed to have max 1 during decomposition}
|
|
| 14 |
+ |
|
| 15 |
+\item{Pmatrix}{the corresponding Pmatrix (patterns X samples) for the provided Amatrix (genes x patterns). This must be supplied if scaledPmatrix is FALSE.}
|
|
| 16 |
+ |
|
| 17 |
+\item{threshold}{# the type of threshold to be used. The default "all" will distribute genes into pattern with the lowest ranking. The "cut" thresholding by the first gene to have a lower ranking, i.e. better fit to, a pattern.}
|
|
| 18 |
+ |
|
| 19 |
+\item{lp}{a vector of weights for each pattern to be used for finding markers. If NA markers for each pattern of the A matrix will be used.}
|
|
| 20 |
+ |
|
| 21 |
+\item{full}{logical indicating whether to return the ranks of each gene for each pattern}
|
|
| 22 |
+} |
|
| 23 |
+\value{
|
|
| 24 |
+By default a non-overlapping list of genes associated with each \code{lp}. If \code{full=TRUE} a data.frame of
|
|
| 25 |
+genes rankings with a column for each \code{lp} will also be returned.
|
|
| 26 |
+} |
|
| 27 |
+\description{
|
|
| 28 |
+patternMarkers |
|
| 29 |
+} |
|
| 30 |
+ |
| 0 | 31 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,44 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/patternMatch4Parallel.R |
|
| 3 |
+\name{patternMatch4Parallel}
|
|
| 4 |
+\alias{patternMatch4Parallel}
|
|
| 5 |
+\title{patternMatch4Parallel}
|
|
| 6 |
+\usage{
|
|
| 7 |
+patternMatch4Parallel(Ptot, nSets, cnt, minNS = NA, maxNS = NULL, |
|
| 8 |
+ cluster.method = "complete", ignore.NA = FALSE, bySet = FALSE, ...) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{Ptot}{a matrix containing the total by set estimates of Pmean output
|
|
| 12 |
+from \code{reOrderBySet}}
|
|
| 13 |
+ |
|
| 14 |
+\item{nSets}{number of parallel sets used to generate \code{Ptot}}
|
|
| 15 |
+ |
|
| 16 |
+\item{cnt}{number of branches at which to cut dendrogram}
|
|
| 17 |
+ |
|
| 18 |
+\item{minNS}{minimum of individual set contributions a cluster must contain}
|
|
| 19 |
+ |
|
| 20 |
+\item{maxNS}{max of individual set contributions a cluster must contain.
|
|
| 21 |
+default is nSets+minNS} |
|
| 22 |
+ |
|
| 23 |
+\item{cluster.method}{the agglomeration method to be used for clustering}
|
|
| 24 |
+ |
|
| 25 |
+\item{ignore.NA}{logical indicating whether or not to ignore NAs from
|
|
| 26 |
+potential over dimensionalization. Default is FALSE.} |
|
| 27 |
+ |
|
| 28 |
+\item{bySet}{logical indicating whether to return list of matched set
|
|
| 29 |
+solutions from \code{Ptot}}
|
|
| 30 |
+ |
|
| 31 |
+\item{...}{additional parameters for \code{agnes}}
|
|
| 32 |
+} |
|
| 33 |
+\value{
|
|
| 34 |
+a matrix of concensus patterns by samples. If \code{bySet=TRUE} then
|
|
| 35 |
+a list of the set contributions to each |
|
| 36 |
+concensus pattern is also returned. |
|
| 37 |
+} |
|
| 38 |
+\description{
|
|
| 39 |
+patternMatch4Parallel |
|
| 40 |
+} |
|
| 41 |
+\seealso{
|
|
| 42 |
+\code{\link{agnes}}
|
|
| 43 |
+} |
|
| 44 |
+ |
| 0 | 45 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,25 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/patternMatcher.R |
|
| 3 |
+\name{patternMatcher}
|
|
| 4 |
+\alias{patternMatcher}
|
|
| 5 |
+\title{PatternMatcher Shiny Ap}
|
|
| 6 |
+\usage{
|
|
| 7 |
+patternMatcher(PBySet = NULL, out = NULL, order = NULL, |
|
| 8 |
+ sample.color = NULL) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{PBySet}{list of matched set solutions for the Pmatrix from an NMF algorithm}
|
|
| 12 |
+ |
|
| 13 |
+\item{out}{optional name for saving output}
|
|
| 14 |
+ |
|
| 15 |
+\item{order}{optional vector indicating order of samples for plotting. Default is NULL.}
|
|
| 16 |
+ |
|
| 17 |
+\item{sample.color}{optional vector of colors of same length as colnames. Default is NULL.}
|
|
| 18 |
+} |
|
| 19 |
+\value{
|
|
| 20 |
+either an index of selected sets' contributions or the editted \code{PBySet} object
|
|
| 21 |
+} |
|
| 22 |
+\description{
|
|
| 23 |
+PatternMatcher Shiny Ap |
|
| 24 |
+} |
|
| 25 |
+ |
| 0 | 26 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,30 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/plotAtoms.R |
|
| 3 |
+\name{plotAtoms}
|
|
| 4 |
+\alias{plotAtoms}
|
|
| 5 |
+\title{Plot Number of Atoms}
|
|
| 6 |
+\usage{
|
|
| 7 |
+plotAtoms(gapsRes, type = "sampA") |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{gapsRes}{the list resulting from applying GAPS}
|
|
| 11 |
+ |
|
| 12 |
+\item{type}{the atoms to plot, values are "sampA", "sampP" ,
|
|
| 13 |
+"equilA", or "equilP" to plot sampling or equilibration teop |
|
| 14 |
+atom numbers} |
|
| 15 |
+} |
|
| 16 |
+\value{
|
|
| 17 |
+plot |
|
| 18 |
+} |
|
| 19 |
+\description{
|
|
| 20 |
+Plot Number of Atoms |
|
| 21 |
+} |
|
| 22 |
+\details{
|
|
| 23 |
+a simple plot of the number of atoms |
|
| 24 |
+from one of the vectors returned with atom numbers |
|
| 25 |
+} |
|
| 26 |
+\examples{
|
|
| 27 |
+data(SimpSim) |
|
| 28 |
+plotAtoms(SimpSim.result, type="sampA") |
|
| 29 |
+} |
|
| 30 |
+ |
| 0 | 31 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,25 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/plotDiag.R |
|
| 3 |
+\name{plotDiag}
|
|
| 4 |
+\alias{plotDiag}
|
|
| 5 |
+\title{Diagnostic Plots}
|
|
| 6 |
+\usage{
|
|
| 7 |
+plotDiag(gapsRes) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{gapsRes}{list returned by CoGAPS}
|
|
| 11 |
+} |
|
| 12 |
+\value{
|
|
| 13 |
+plot |
|
| 14 |
+} |
|
| 15 |
+\description{
|
|
| 16 |
+Diagnostic Plots |
|
| 17 |
+} |
|
| 18 |
+\details{
|
|
| 19 |
+plots a series of diagnostic plots |
|
| 20 |
+} |
|
| 21 |
+\examples{
|
|
| 22 |
+data(SimpSim) |
|
| 23 |
+plotDiag(SimpSim.result) |
|
| 24 |
+} |
|
| 25 |
+ |
| 0 | 26 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,31 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/plotGAPS.R |
|
| 3 |
+\name{plotGAPS}
|
|
| 4 |
+\alias{plotGAPS}
|
|
| 5 |
+\title{Plot Decomposed A and P Matrices}
|
|
| 6 |
+\usage{
|
|
| 7 |
+plotGAPS(A, P) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{A}{the mean A matrix}
|
|
| 11 |
+ |
|
| 12 |
+\item{P}{the mean P matrix}
|
|
| 13 |
+ |
|
| 14 |
+\item{outputPDF}{optional root name for PDF output, if
|
|
| 15 |
+not specified, output goes to screen} |
|
| 16 |
+} |
|
| 17 |
+\value{
|
|
| 18 |
+plot |
|
| 19 |
+} |
|
| 20 |
+\description{
|
|
| 21 |
+Plot Decomposed A and P Matrices |
|
| 22 |
+} |
|
| 23 |
+\details{
|
|
| 24 |
+plots the output A and P matrices as a |
|
| 25 |
+heatmap and line plot respectively |
|
| 26 |
+} |
|
| 27 |
+\examples{
|
|
| 28 |
+data(SimpSim) |
|
| 29 |
+plotGAPS(SimpSim.result$Amean, SimpSim.result$Pmean) |
|
| 30 |
+} |
|
| 31 |
+ |
| 0 | 32 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,27 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/plotP.R |
|
| 3 |
+\name{plotP}
|
|
| 4 |
+\alias{plotP}
|
|
| 5 |
+\title{Plot the P Matrix}
|
|
| 6 |
+\usage{
|
|
| 7 |
+plotP(Pmean, Psd = NULL) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{Pmean}{matrix of mean values of P}
|
|
| 11 |
+ |
|
| 12 |
+\item{Psd}{matrix of standard deviation values of P}
|
|
| 13 |
+} |
|
| 14 |
+\value{
|
|
| 15 |
+plot |
|
| 16 |
+} |
|
| 17 |
+\description{
|
|
| 18 |
+Plot the P Matrix |
|
| 19 |
+} |
|
| 20 |
+\details{
|
|
| 21 |
+plots the P matrix in a line plot with error bars |
|
| 22 |
+} |
|
| 23 |
+\examples{
|
|
| 24 |
+data(SimpSim) |
|
| 25 |
+plotP(SimpSim.result$Pmean, SimpSim.result$Psd) |
|
| 26 |
+} |
|
| 27 |
+ |
| 0 | 28 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,40 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/plotPatternMarkers.R |
|
| 3 |
+\name{plotPatternMarkers}
|
|
| 4 |
+\alias{plotPatternMarkers}
|
|
| 5 |
+\title{plotPatternMarkers}
|
|
| 6 |
+\usage{
|
|
| 7 |
+plotPatternMarkers(data = NA, patternMarkers = NA, patternPalette = NA, |
|
| 8 |
+ sampleNames = NA, samplePalette = NULL, colDenogram = TRUE, heatmapCol, |
|
| 9 |
+ scale = "row", ...) |
|
| 10 |
+} |
|
| 11 |
+\arguments{
|
|
| 12 |
+\item{data}{the dataset from which the patterns where generated}
|
|
| 13 |
+ |
|
| 14 |
+\item{patternMarkers}{the list of genes generated from the patternMarkers function}
|
|
| 15 |
+ |
|
| 16 |
+\item{patternPalette}{a vector indicating what color should be used for each pattern}
|
|
| 17 |
+ |
|
| 18 |
+\item{sampleNames}{names of the samples to use for labeling}
|
|
| 19 |
+ |
|
| 20 |
+\item{samplePalette}{a vector indicating what color should be used for each sample}
|
|
| 21 |
+ |
|
| 22 |
+\item{colDenogram}{logical indicating whether to display sample denogram}
|
|
| 23 |
+ |
|
| 24 |
+\item{heatmapCol}{pallelet giving color scheme for heatmap}
|
|
| 25 |
+ |
|
| 26 |
+\item{scale}{character indicating if the values should be centered and scaled in either
|
|
| 27 |
+the row direction or the column direction, or none. The default is "row".} |
|
| 28 |
+ |
|
| 29 |
+\item{...}{additional graphical parameters to be passed to \code{heatmap.2}}
|
|
| 30 |
+} |
|
| 31 |
+\value{
|
|
| 32 |
+heatmap of the \code{data} values for the \code{patternMarkers}
|
|
| 33 |
+} |
|
| 34 |
+\description{
|
|
| 35 |
+plotPatternMarkers |
|
| 36 |
+} |
|
| 37 |
+\seealso{
|
|
| 38 |
+\code{\link{heatmap.2}}
|
|
| 39 |
+} |
|
| 40 |
+ |
| 0 | 41 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,46 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/plotSmoothPatterns.R |
|
| 3 |
+\name{plotSmoothPatterns}
|
|
| 4 |
+\alias{plotSmoothPatterns}
|
|
| 5 |
+\title{Plot Smooth Patterns}
|
|
| 6 |
+\usage{
|
|
| 7 |
+plotSmoothPatterns(P, x = NULL, breaks = NULL, breakStyle = TRUE, |
|
| 8 |
+ orderP = !all(is.null(x)), plotPTS = FALSE, pointCol = "black", |
|
| 9 |
+ lineCol = "grey", add = FALSE, ...) |
|
| 10 |
+} |
|
| 11 |
+\arguments{
|
|
| 12 |
+\item{P}{the mean A matrix}
|
|
| 13 |
+ |
|
| 14 |
+\item{x}{optional variables}
|
|
| 15 |
+ |
|
| 16 |
+\item{breaks}{breaks in plots}
|
|
| 17 |
+ |
|
| 18 |
+\item{breakStyle}{style of breaks}
|
|
| 19 |
+ |
|
| 20 |
+\item{orderP}{whether to order patterns}
|
|
| 21 |
+ |
|
| 22 |
+\item{plotPTS}{whether to plot points on lines}
|
|
| 23 |
+ |
|
| 24 |
+\item{pointCol}{color of points}
|
|
| 25 |
+ |
|
| 26 |
+\item{lineCol}{color of line}
|
|
| 27 |
+ |
|
| 28 |
+\item{add}{logical specifying if bars should be added to an already existing
|
|
| 29 |
+plot; defaults to `FALSE'.} |
|
| 30 |
+ |
|
| 31 |
+\item{...}{arguments to be passed to/from other methods. For the default
|
|
| 32 |
+method these can include further arguments (such as `axes', `asp' and |
|
| 33 |
+`main') and graphical parameters (see `par') which are passed to |
|
| 34 |
+`plot.window()', `title()' and `axis'.} |
|
| 35 |
+} |
|
| 36 |
+\value{
|
|
| 37 |
+plot |
|
| 38 |
+} |
|
| 39 |
+\description{
|
|
| 40 |
+Plot Smooth Patterns |
|
| 41 |
+} |
|
| 42 |
+\details{
|
|
| 43 |
+plots the output A and P matrices as a heatmap and a |
|
| 44 |
+line plot respectively |
|
| 45 |
+} |
|
| 46 |
+ |
| 0 | 47 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,24 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/postFixed4Parallel.R |
|
| 3 |
+\name{postFixed4Parallel}
|
|
| 4 |
+\alias{postFixed4Parallel}
|
|
| 5 |
+\title{Post Processing of Parallel Output}
|
|
| 6 |
+\usage{
|
|
| 7 |
+postFixed4Parallel(AP.fixed, setValues, setMatrix = "P") |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{AP.fixed}{output of parallel gapsMapRun calls with same FP}
|
|
| 11 |
+ |
|
| 12 |
+\item{setValues}{data.frame with rows giving fixed patterns for P used as input
|
|
| 13 |
+for gapsMapRun} |
|
| 14 |
+ |
|
| 15 |
+\item{setMatrix}{which matrix, A or P}
|
|
| 16 |
+} |
|
| 17 |
+\value{
|
|
| 18 |
+list of two data.frames containing the A matrix estimates or their |
|
| 19 |
+corresponding standard deviations from output of parallel CoGAPS |
|
| 20 |
+} |
|
| 21 |
+\description{
|
|
| 22 |
+Post Processing of Parallel Output |
|
| 23 |
+} |
|
| 24 |
+ |
| 0 | 25 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,22 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/postFixed4SC.R |
|
| 3 |
+\name{postFixed4SC}
|
|
| 4 |
+\alias{postFixed4SC}
|
|
| 5 |
+\title{Post Processing of Parallel Output}
|
|
| 6 |
+\usage{
|
|
| 7 |
+postFixed4SC(AP.fixed, setAs) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{AP.fixed}{output of parallel gapsMapRun calls with same FP}
|
|
| 11 |
+ |
|
| 12 |
+\item{setAs}{data.frame with rows giving fixed patterns for A used as input
|
|
| 13 |
+for gapsMapRun} |
|
| 14 |
+} |
|
| 15 |
+\value{
|
|
| 16 |
+list of two data.frames containing the A matrix estimates or their |
|
| 17 |
+corresponding standard deviations from output of parallel CoGAPS |
|
| 18 |
+} |
|
| 19 |
+\description{
|
|
| 20 |
+Post Processing of Parallel Output |
|
| 21 |
+} |
|
| 22 |
+ |
| 0 | 23 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,24 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/reOrderBySet.R |
|
| 3 |
+\name{reOrderBySet}
|
|
| 4 |
+\alias{reOrderBySet}
|
|
| 5 |
+\title{reOrderBySet}
|
|
| 6 |
+\usage{
|
|
| 7 |
+reOrderBySet(AP, nFactor, nSets, match = "P") |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{AP}{output of gapsRun in parallel}
|
|
| 11 |
+ |
|
| 12 |
+\item{nFactor}{number of patterns}
|
|
| 13 |
+ |
|
| 14 |
+\item{nSets}{number of sets}
|
|
| 15 |
+ |
|
| 16 |
+\item{match}{which matrix to use for downstream matching. default is P}
|
|
| 17 |
+} |
|
| 18 |
+\value{
|
|
| 19 |
+a list containing the \code{nSets} sets solution for Amean under "A", Pmean under "P", and Asd under "Asd"
|
|
| 20 |
+} |
|
| 21 |
+\description{
|
|
| 22 |
+<restructures output of gapsRun into a list containing each sets solution for Amean, Pmean, and Asd> |
|
| 23 |
+} |
|
| 24 |
+ |
| 0 | 25 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,26 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/reconstructGene.R |
|
| 3 |
+\name{reconstructGene}
|
|
| 4 |
+\alias{reconstructGene}
|
|
| 5 |
+\title{Reconstruct Gene}
|
|
| 6 |
+\usage{
|
|
| 7 |
+reconstructGene(A, P, genes = NA) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{A}{A matrix estimates}
|
|
| 11 |
+ |
|
| 12 |
+\item{P}{corresponding P matrix estimates}
|
|
| 13 |
+ |
|
| 14 |
+\item{genes}{an index of the gene or genes of interest}
|
|
| 15 |
+} |
|
| 16 |
+\value{
|
|
| 17 |
+the D' estimate of a gene or set of genes |
|
| 18 |
+} |
|
| 19 |
+\description{
|
|
| 20 |
+Reconstruct Gene |
|
| 21 |
+} |
|
| 22 |
+\examples{
|
|
| 23 |
+data(SimpSim) |
|
| 24 |
+reconstructGene(SimpSim.result$Amean, SimpSim.result$Pmean) |
|
| 25 |
+} |
|
| 26 |
+ |
| 0 | 27 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,20 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/reorderByPatternMatch.R |
|
| 3 |
+\name{reorderByPatternMatch}
|
|
| 4 |
+\alias{reorderByPatternMatch}
|
|
| 5 |
+\title{Reorder By Pattern Match}
|
|
| 6 |
+\usage{
|
|
| 7 |
+reorderByPatternMatch(P, matchTo) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{P}{matrix to be matched}
|
|
| 11 |
+ |
|
| 12 |
+\item{matchTo}{matrix to match P to}
|
|
| 13 |
+} |
|
| 14 |
+\value{
|
|
| 15 |
+matched patterns |
|
| 16 |
+} |
|
| 17 |
+\description{
|
|
| 18 |
+Reorder By Pattern Match |
|
| 19 |
+} |
|
| 20 |
+ |
| 0 | 21 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,31 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/residuals.R |
|
| 3 |
+\name{residuals}
|
|
| 4 |
+\alias{residuals}
|
|
| 5 |
+\title{Plot of Residuals}
|
|
| 6 |
+\usage{
|
|
| 7 |
+residuals(AMean_Mat, PMean_Mat, D, S) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{AMean_Mat}{matrix of mean values for A from GAPS}
|
|
| 11 |
+ |
|
| 12 |
+\item{PMean_Mat}{matrix of mean values for P from GAPS}
|
|
| 13 |
+ |
|
| 14 |
+\item{D}{original data matrix run through GAPS}
|
|
| 15 |
+ |
|
| 16 |
+\item{S}{original standard deviation matrix run through GAPS}
|
|
| 17 |
+} |
|
| 18 |
+\value{
|
|
| 19 |
+creates a residual plot |
|
| 20 |
+} |
|
| 21 |
+\description{
|
|
| 22 |
+Plot of Residuals |
|
| 23 |
+} |
|
| 24 |
+\details{
|
|
| 25 |
+calculate residuals and produce heatmap |
|
| 26 |
+} |
|
| 27 |
+\examples{
|
|
| 28 |
+data(SimpSim) |
|
| 29 |
+residuals(SimpSim.result$Amean, SimpSim.result$Pmean, SimpSim.D, SimpSim.S) |
|
| 30 |
+} |
|
| 31 |
+ |
| 0 | 32 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,39 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/scCoGAPS.R |
|
| 3 |
+\name{scCoGAPS}
|
|
| 4 |
+\alias{scCoGAPS}
|
|
| 5 |
+\title{scCoGAPS}
|
|
| 6 |
+\usage{
|
|
| 7 |
+scCoGAPS(simulationName, nFactor, nCores = NA, cut = NA, minNS = NA, |
|
| 8 |
+ manualMatch = FALSE, consensusAs = NULL, ...) |
|
| 9 |
+} |
|
| 10 |
+\arguments{
|
|
| 11 |
+\item{simulationName}{name of this simulation}
|
|
| 12 |
+ |
|
| 13 |
+\item{nFactor}{number of patterns (basis vectors, metagenes), which must be
|
|
| 14 |
+greater than or equal to the number of rows of FP} |
|
| 15 |
+ |
|
| 16 |
+\item{nCores}{number of cores for parallelization. If left to the default NA, nCores = nSets.}
|
|
| 17 |
+ |
|
| 18 |
+\item{cut}{number of branches at which to cut dendrogram used in patternMatch4singleCell}
|
|
| 19 |
+ |
|
| 20 |
+\item{minNS}{minimum of individual set contributions a cluster must contain}
|
|
| 21 |
+ |
|
| 22 |
+\item{manualMatch}{logical indicating whether or not to stop after initial phase for manual pattern matching}
|
|
| 23 |
+ |
|
| 24 |
+\item{consensusAs}{fixed pattern matrix to be used to ensure reciprocity of A weights accross sets}
|
|
| 25 |
+ |
|
| 26 |
+\item{...}{additional parameters to be fed into \code{gapsRun} and \code{gapsMapRun}}
|
|
| 27 |
+} |
|
| 28 |
+\value{
|
|
| 29 |
+list of A and P estimates |
|
| 30 |
+} |
|
| 31 |
+\description{
|
|
| 32 |
+scCoGAPS |
|
| 33 |
+} |
|
| 34 |
+\details{
|
|
| 35 |
+calls the C++ MCMC code and performs Bayesian |
|
| 36 |
+matrix factorization returning the two matrices that reconstruct |
|
| 37 |
+the data matrix for whole genome data; |
|
| 38 |
+} |
|
| 39 |
+ |
| 0 | 40 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,26 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/scCoGAPS.R |
|
| 3 |
+\name{scCoGapsFromCheckpoint}
|
|
| 4 |
+\alias{scCoGapsFromCheckpoint}
|
|
| 5 |
+\title{Restart a scCoGAPS run from a Checkpoint}
|
|
| 6 |
+\usage{
|
|
| 7 |
+scCoGapsFromCheckpoint(simulationName, nCores, cut = NA, minNS = NA, ...) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{simulationName}{name of this simulation}
|
|
| 11 |
+ |
|
| 12 |
+\item{nCores}{number of cores for parallelization. If left to the default NA, nCores = nSets.}
|
|
| 13 |
+ |
|
| 14 |
+\item{cut}{number of branches at which to cut dendrogram used in patternMatch4Parallel}
|
|
| 15 |
+ |
|
| 16 |
+\item{minNS}{minimum of individual set contributions a cluster must contain}
|
|
| 17 |
+ |
|
| 18 |
+\item{...}{additional parameters to be fed into \code{gapsRun} and \code{gapsMapRun}}
|
|
| 19 |
+} |
|
| 20 |
+\value{
|
|
| 21 |
+list of A and P estimates |
|
| 22 |
+} |
|
| 23 |
+\description{
|
|
| 24 |
+Restart a scCoGAPS run from a Checkpoint |
|
| 25 |
+} |
|
| 26 |
+ |
| ... | ... |
@@ -14,7 +14,7 @@ Atom AtomicDomain::front() const |
| 14 | 14 |
// O(1) |
| 15 | 15 |
Atom AtomicDomain::randomAtom() const |
| 16 | 16 |
{
|
| 17 |
- uint64_t ndx = gaps::random::Generator::uniform64(0, mAtoms.size() - 1); |
|
| 17 |
+ uint64_t ndx = gaps::random::uniform64(0, mAtoms.size() - 1); |
|
| 18 | 18 |
return mAtoms[ndx]; |
| 19 | 19 |
} |
| 20 | 20 |
|
| ... | ... |
@@ -24,7 +24,7 @@ uint64_t AtomicDomain::randomFreePosition() const |
| 24 | 24 |
uint64_t pos = 0; |
| 25 | 25 |
do |
| 26 | 26 |
{
|
| 27 |
- pos = gaps::random::Generator::uniform64(0, mDomainSize); |
|
| 27 |
+ pos = gaps::random::uniform64(0, mDomainSize); |
|
| 28 | 28 |
} while (mUsedPositions.count(pos) > 0); // hash map => count is O(l) |
| 29 | 29 |
return pos; |
| 30 | 30 |
} |
| ... | ... |
@@ -21,7 +21,7 @@ mNumCores(nCores) |
| 21 | 21 |
{
|
| 22 | 22 |
mASampler.sync(mPSampler); |
| 23 | 23 |
mPSampler.sync(mASampler); |
| 24 |
- gaps::random::Generator::setSeed(seed); |
|
| 24 |
+ gaps::random::setSeed(seed); |
|
| 25 | 25 |
} |
| 26 | 26 |
|
| 27 | 27 |
GapsRunner::GapsRunner(const Rcpp::NumericMatrix &D, const Rcpp::NumericMatrix &S, |
| ... | ... |
@@ -197,8 +197,8 @@ void GapsRunner::storeSamplerInfo(Vector &atomsA, Vector &atomsP, Vector &chi2) |
| 197 | 197 |
chi2[mCurrentIter] = mASampler.chi2(); |
| 198 | 198 |
atomsA[mCurrentIter] = mASampler.nAtoms(); |
| 199 | 199 |
atomsP[mCurrentIter] = mPSampler.nAtoms(); |
| 200 |
- mIterA = gaps::random::Generator::poisson(std::max(atomsA[mCurrentIter], 10.f)); |
|
| 201 |
- mIterP = gaps::random::Generator::poisson(std::max(atomsP[mCurrentIter], 10.f)); |
|
| 200 |
+ mIterA = gaps::random::poisson(std::max(atomsA[mCurrentIter], 10.f)); |
|
| 201 |
+ mIterP = gaps::random::poisson(std::max(atomsP[mCurrentIter], 10.f)); |
|
| 202 | 202 |
} |
| 203 | 203 |
|
| 204 | 204 |
static void printTime(const std::string &message, unsigned totalSeconds) |
| ... | ... |
@@ -243,7 +243,7 @@ void GibbsSampler<T, MatA, MatB>::birth(uint64_t pos, unsigned row, |
| 243 | 243 |
unsigned col) |
| 244 | 244 |
{
|
| 245 | 245 |
float mass = impl()->canUseGibbs(row, col) ? gibbsMass(row, col, 0.f) |
| 246 |
- : gaps::random::Generator::exponential(mLambda); |
|
| 246 |
+ : gaps::random::exponential(mLambda); |
|
| 247 | 247 |
if (mass >= gaps::algo::epsilon) |
| 248 | 248 |
{
|
| 249 | 249 |
mDomain.updateMass(pos, mass); |
| ... | ... |
@@ -273,7 +273,7 @@ unsigned col) |
| 273 | 273 |
float newMass = impl()->canUseGibbs(row, col) ? gibbsMass(row, col, -mass) : 0.f; |
| 274 | 274 |
mass = newMass < gaps::algo::epsilon ? mass : newMass; |
| 275 | 275 |
float deltaLL = impl()->computeDeltaLL(row, col, mass); |
| 276 |
- if (deltaLL * mAnnealingTemp >= std::log(gaps::random::Generator::uniform())) |
|
| 276 |
+ if (deltaLL * mAnnealingTemp >= std::log(gaps::random::uniform())) |
|
| 277 | 277 |
{
|
| 278 | 278 |
mDomain.updateMass(pos, mass); |
| 279 | 279 |
mMatrix(row, col) += mass; |
| ... | ... |
@@ -295,7 +295,7 @@ unsigned r1, unsigned c1, unsigned r2, unsigned c2) |
| 295 | 295 |
if (r1 != r2 || c1 != c2) // automatically reject if change in same bin |
| 296 | 296 |
{
|
| 297 | 297 |
float deltaLL = impl()->computeDeltaLL(r1, c1, -mass, r2, c2, mass); |
| 298 |
- if (deltaLL * mAnnealingTemp > std::log(gaps::random::Generator::uniform())) |
|
| 298 |
+ if (deltaLL * mAnnealingTemp > std::log(gaps::random::uniform())) |
|
| 299 | 299 |
{
|
| 300 | 300 |
removeMass(src, mass, r1, c1); |
| 301 | 301 |
addMass(dest, mass, r2, c2); |
| ... | ... |
@@ -311,7 +311,7 @@ void GibbsSampler<T, MatA, MatB>::exchange(uint64_t p1, float m1, uint64_t p2, |
| 311 | 311 |
float m2, unsigned r1, unsigned c1, unsigned r2, unsigned c2) |
| 312 | 312 |
{
|
| 313 | 313 |
float pUpper = gaps::random::p_gamma(m1 + m2, 2.f, 1.f / mLambda); |
| 314 |
- float newMass = gaps::random::Generator::inverseGammaSample(0.f, pUpper, 2.f, 1.f / mLambda); |
|
| 314 |
+ float newMass = gaps::random::inverseGammaSample(0.f, pUpper, 2.f, 1.f / mLambda); |
|
| 315 | 315 |
if (r1 != r2 || c1 != c2) // automatically reject if change in same bin |
| 316 | 316 |
{
|
| 317 | 317 |
if ((m1 > m2 && newMass - m1 < 0) || (m1 < m2 && m2 - newMass < 0)) |
| ... | ... |
@@ -347,7 +347,7 @@ float m2, unsigned r1, unsigned c1, unsigned r2, unsigned c2) |
| 347 | 347 |
{
|
| 348 | 348 |
float deltaLL = impl()->computeDeltaLL(r1, c1, delta, r2, c2, -delta); |
| 349 | 349 |
float priorLL = (pOld == 0.f) ? 1.f : pOld / pNew; |
| 350 |
- float u = std::log(gaps::random::Generator::uniform() * priorLL); |
|
| 350 |
+ float u = std::log(gaps::random::uniform() * priorLL); |
|
| 351 | 351 |
if (u < deltaLL * mAnnealingTemp) |
| 352 | 352 |
{
|
| 353 | 353 |
acceptExchange(p1, m1, delta, p2, m2, -delta, r1, c1, r2, c2); |
| ... | ... |
@@ -418,7 +418,7 @@ float GibbsSampler<T, MatA, MatB>::gibbsMass(unsigned row, unsigned col, float m |
| 418 | 418 |
|
| 419 | 419 |
if (pLower < 1.f) |
| 420 | 420 |
{
|
| 421 |
- float m = gaps::random::Generator::inverseNormSample(pLower, 1.f, mean, sd); |
|
| 421 |
+ float m = gaps::random::inverseNormSample(pLower, 1.f, mean, sd); |
|
| 422 | 422 |
return std::max(std::min(m, mMaxGibbsMass), 0.f); |
| 423 | 423 |
} |
| 424 | 424 |
} |
| ... | ... |
@@ -442,7 +442,7 @@ unsigned r2, unsigned c2, float m2) |
| 442 | 442 |
|
| 443 | 443 |
if (!(pLower > 0.95f || pUpper < 0.05f)) |
| 444 | 444 |
{
|
| 445 |
- float delta = gaps::random::Generator::inverseNormSample(pLower, pUpper, mean, sd); |
|
| 445 |
+ float delta = gaps::random::inverseNormSample(pLower, pUpper, mean, sd); |
|
| 446 | 446 |
return std::min(std::max(-m1, delta), m2); // conserve mass |
| 447 | 447 |
} |
| 448 | 448 |
} |
| ... | ... |
@@ -103,8 +103,8 @@ bool ProposalQueue::makeProposal(AtomicDomain &domain) |
| 103 | 103 |
|
| 104 | 104 |
float bdProb = mMaxAtoms < 2 ? 0.6667f : 0.5f; |
| 105 | 105 |
|
| 106 |
- mU1 = mUseCachedRng ? mU1 : gaps::random::Generator::uniform(); |
|
| 107 |
- mU2 = mUseCachedRng ? mU2: gaps::random::Generator::uniform(); |
|
| 106 |
+ mU1 = mUseCachedRng ? mU1 : gaps::random::uniform(); |
|
| 107 |
+ mU2 = mUseCachedRng ? mU2: gaps::random::uniform(); |
|
| 108 | 108 |
mUseCachedRng = false; |
| 109 | 109 |
|
| 110 | 110 |
float lowerBound = deathProb(mMinAtoms); |
| ... | ... |
@@ -168,7 +168,7 @@ bool ProposalQueue::move(AtomicDomain &domain) |
| 168 | 168 |
return false; |
| 169 | 169 |
} |
| 170 | 170 |
|
| 171 |
- uint64_t newLocation = gaps::random::Generator::uniform64(lbound, rbound - 1); |
|
| 171 |
+ uint64_t newLocation = gaps::random::uniform64(lbound, rbound - 1); |
|
| 172 | 172 |
if (mUsedIndices.count(a.pos / mDimensionSize) || mUsedIndices.count(newLocation / mDimensionSize)) |
| 173 | 173 |
{
|
| 174 | 174 |
return false; // matrix conflict - can't compute deltaLL |
| ... | ... |
@@ -5,11 +5,11 @@ |
| 5 | 5 |
|
| 6 | 6 |
TEST_CASE("Test Random.h - Random Number Generation")
|
| 7 | 7 |
{
|
| 8 |
- gaps::random::Generator::setSeed(0); |
|
| 8 |
+ gaps::random::setSeed(0); |
|
| 9 | 9 |
|
| 10 | 10 |
SECTION("Make sure uniform01 is working")
|
| 11 | 11 |
{
|
| 12 |
- REQUIRE(gaps::random::Generator::uniform() != gaps::random::Generator::uniform()); |
|
| 12 |
+ REQUIRE(gaps::random::uniform() != gaps::random::uniform()); |
|
| 13 | 13 |
} |
| 14 | 14 |
|
| 15 | 15 |
SECTION("Test uniform distribution over unit interval")
|
| ... | ... |
@@ -19,9 +19,9 @@ TEST_CASE("Test Random.h - Random Number Generation")
|
| 19 | 19 |
unsigned N = 10000; |
| 20 | 20 |
for (unsigned i = 0; i < N; ++i) |
| 21 | 21 |
{
|
| 22 |
- min = std::min(gaps::random::Generator::uniform(), min); |
|
| 23 |
- max = std::max(gaps::random::Generator::uniform(), max); |
|
| 24 |
- sum += gaps::random::Generator::uniform(); |
|
| 22 |
+ min = std::min(gaps::random::uniform(), min); |
|
| 23 |
+ max = std::max(gaps::random::uniform(), max); |
|
| 24 |
+ sum += gaps::random::uniform(); |
|
| 25 | 25 |
} |
| 26 | 26 |
REQUIRE(sum / N == Approx(0.5f).epsilon(0.01f)); |
| 27 | 27 |
REQUIRE(min >= 0.f); |
| ... | ... |
@@ -33,14 +33,14 @@ TEST_CASE("Test Random.h - Random Number Generation")
|
| 33 | 33 |
SECTION("Test uniform distribution over general interval")
|
| 34 | 34 |
{
|
| 35 | 35 |
// bounds equal |
| 36 |
- REQUIRE(gaps::random::Generator::uniform(4.3f, 4.3f) == 4.3f); |
|
| 36 |
+ REQUIRE(gaps::random::uniform(4.3f, 4.3f) == 4.3f); |
|
| 37 | 37 |
|
| 38 | 38 |
// full range possible |
| 39 | 39 |
float min = 10., max = 0.; |
| 40 | 40 |
for (unsigned i = 0; i < 1000; ++i) |
| 41 | 41 |
{
|
| 42 |
- min = std::min(gaps::random::Generator::uniform(0.f,10.f), min); |
|
| 43 |
- max = std::max(gaps::random::Generator::uniform(0.f,10.f), max); |
|
| 42 |
+ min = std::min(gaps::random::uniform(0.f,10.f), min); |
|
| 43 |
+ max = std::max(gaps::random::uniform(0.f,10.f), max); |
|
| 44 | 44 |
} |
| 45 | 45 |
REQUIRE(min < 0.1f); |
| 46 | 46 |
REQUIRE(max > 9.9f); |
| ... | ... |
@@ -58,7 +58,7 @@ TEST_CASE("Test Random.h - Random Number Generation")
|
| 58 | 58 |
float norm[1024]; |
| 59 | 59 |
for (unsigned i = 0; i < 1024; ++i) |
| 60 | 60 |
{
|
| 61 |
- norm[i] = gaps::random::Generator::normal(0.f, 1.f); |
|
| 61 |
+ norm[i] = gaps::random::normal(0.f, 1.f); |
|
| 62 | 62 |
mean += norm[i]; |
| 63 | 63 |
} |
| 64 | 64 |
|
| ... | ... |
@@ -78,7 +78,7 @@ TEST_CASE("Test Random.h - Random Number Generation")
|
| 78 | 78 |
float total = 0.f; |
| 79 | 79 |
for (unsigned i = 0; i < 10000; ++i) |
| 80 | 80 |
{
|
| 81 |
- float num = gaps::random::Generator::poisson(4.f); |
|
| 81 |
+ float num = gaps::random::poisson(4.f); |
|
| 82 | 82 |
total += num; |
| 83 | 83 |
|
| 84 | 84 |
REQUIRE((int)num == num); // should be integer |
| ... | ... |
@@ -93,7 +93,7 @@ TEST_CASE("Test Random.h - Random Number Generation")
|
| 93 | 93 |
float total = 0.f; |
| 94 | 94 |
for (unsigned i = 0; i < 10000; ++i) |
| 95 | 95 |
{
|
| 96 |
- float num = gaps::random::Generator::exponential(1.f); |
|
| 96 |
+ float num = gaps::random::exponential(1.f); |
|
| 97 | 97 |
total += num; |
| 98 | 98 |
|
| 99 | 99 |
REQUIRE(num >= 0.f); // should be non-negative |