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eliminated some roxygen-related warnings

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paul-shannon committed Apr 27, 2019
1 parent 974afc0 commit bb237499e6f62c8ecb48bb01a87e454ca7199af2
Showing with 51 additions and 24 deletions.
  1. +3 −2 DESCRIPTION
  2. +1 −0 NAMESPACE
  3. +8 −7 R/EnsembleSolver.R
  4. +0 −2 R/MotifMatcher.R
  5. +1 −1 R/PCAMax.R
  6. +3 −1 R/Trena.R
  7. +3 −2 inst/unitTests/test_Trena.R
  8. +3 −1 man/Trena-class.Rd
  9. +1 −1 man/normalizeModel.Rd
  10. +27 −6 man/solve.Ensemble.Rd
  11. +1 −1 vignettes/TReNA_Vignette.Rmd
@@ -1,8 +1,8 @@
Package: trena
Type: Package
Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning
Version: 1.5.14
Date: 2019-04-15
Version: 1.5.15
Date: 2019-04-26
Author: Seth Ament <seth.ament@systemsbiology.org>, Paul Shannon <pshannon@systemsbioloyg.org>, Matthew Richards <mrichard@systemsbiology.org>
Maintainer: Paul Shannon <paul.thurmond.shannon@gmail.com>
Imports:
@@ -67,4 +67,5 @@ Collate:
'help.R'
'sharedFunctions.R'
'utils.R'
Encoding: UTF-8
RoxygenNote: 6.1.1
@@ -54,6 +54,7 @@ exportMethods(getSolverNames)
exportMethods(getTarget)
exportMethods(normalizeModel)
exportMethods(rescalePredictorWeights)
exportMethods(run)
exportMethods(show)
import(BSgenome)
import(BiocParallel)
@@ -185,18 +185,17 @@ setMethod("getSolverNames", "EnsembleSolver",
#'
#' @return A data frame containing the scores for all solvers and two composite scores
#' relating the target gene to each transcription factor. The two new scores are:
#' @details
#' \itemize{
#' \item{"concordance": a composite score created similarly to "extreme_score", but with each solver's
#' score scaled using *atan(x)*. This score scales from 0-1}
#' \item{"pcaMax": a composite score created using the root mean square of the principal
#' components of the individual solver scores}
#' \item{concordance}{a composite score}
#' \item{pcaMax}{a composite of the principal components of the individual solver scores}
#' }
#'
#' @seealso \code{\link{EnsembleSolver}}
#'
#' @family solver methods
#'
#' @examples
#' @examples
#' \dontrun{
#' # Load included Alzheimer's data, create an Ensemble object with default solvers, and solve
#' load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
@@ -215,6 +214,8 @@ setMethod("getSolverNames", "EnsembleSolver",
#' solverNames = c("lasso", "pearson", "ridge"))
#' tbl <- run(ensemble.solver)
#' }
#'
#' @export

setMethod("run", "EnsembleSolver",

@@ -475,8 +476,8 @@ setMethod("run", "EnsembleSolver",
# if(class(tbl.augmented) == "try-error"){
# #browser()
# warning("The signal strength of ensemble of solvers is too weak to support
#composite scores ('pcaMax' and 'concordance' in the model output table. This is a classic
#'large n, small m' problem that could be rectified by providing more samples")
# composite scores ('pcaMax' and 'concordance' in the model output table. This is a classic
# "large n, small m" problem that could be rectified by providing more samples")
# tbl.all$pcaMax <- NA
# tbl.all$concordance <- NA
# } else {
@@ -70,12 +70,10 @@ MotifMatcher <- function(genomeName,
stopifnot(is.list(pfms))

if(genomeName == "hg38"){
# library(BSgenome.Hsapiens.UCSC.hg38) ## Remove the library reference
reference.genome <- BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38
}

else if(genomeName == "hg19"){
# library(BSgenome.Hsapiens.UCSC.hg19) ## Remove the library reference
reference.genome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19
}

@@ -54,7 +54,7 @@ PCAMax <- function(tbl, tfIdentifierColumnName="tf.hgnc")
#' @aliases normalizeModel
#'
#' @param obj An object of the class PCAMax
#' @param max numeric, a maximum value for the normalized distrubtions
#' @param normalizing.max numeric, a maximum value for the normalized distrubtions
#'
#' @return a normalized matrix, each column treated separately
#'
@@ -63,7 +63,9 @@ genome.db.uri <- "postgres://bddsrds.globusgenomics.org/hg38" # has gtf and mo
#' trena <- Trena("hg38")
#'
#' @seealso \code{\link{getRegulatoryChromosomalRegions}}, \code{\link{getRegulatoryTableColumnNames}},
#' \code{\link{getGeneModelTableColumnNames}}, \code{\link{createGeneModel}}
#' \code{\link{getGeneModelTableColumnNames}},
#' \code{\link{createGeneModelFromRegulatoryRegions}},
#' \code{\link{createGeneModelFromTfList}}

Trena = function(genomeName, quiet=TRUE)
{
@@ -200,8 +200,9 @@ test_createGeneModelFromRegulatoryRegions <- function()
expected.colnames <- c("gene", "betaLasso", "lassoPValue", "pearsonCoeff", "rfScore", "betaRidge",
"spearmanCoeff", "bindingSites")
checkTrue(all(expected.colnames %in% colnames(tbl.geneModel)))
checkTrue(nrow(tbl.geneModel) > 100)
checkTrue(all(c("HLF", "STAT4", "SATB2") %in% tbl.geneModel$gene[1:10]))
browser()
checkTrue(nrow(tbl.geneModel) > 40)
checkTrue("HLF" %in% tbl.geneModel$gene[1:10])

checkEquals(openPostgresConnections(), 0)

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@@ -3,7 +3,7 @@ title: "A Brief Introduction to TReNA"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{A Brief Introduction to TReNA}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEngine{knitr::knitr}}
%\VignetteEncoding{UTF-8}
---

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