[med-svn] [r-bioc-deseq2] 01/05: New upstream version 1.12.4
Andreas Tille
tille at debian.org
Sat Sep 10 20:01:57 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-bioc-deseq2.
commit 3e47e029223e7b878cb6bda33e40f298c5c43800
Author: Andreas Tille <tille at debian.org>
Date: Sat Sep 10 21:52:32 2016 +0200
New upstream version 1.12.4
---
DESCRIPTION | 4 +-
R/core.R | 30 +-
R/expanded.R | 2 +-
build/vignette.rds | Bin 229 -> 231 bytes
inst/doc/DESeq2.pdf | 2052 +++++++++++++++++++++++++--------------------------
5 files changed, 1037 insertions(+), 1051 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 7d7c879..d462dcf 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: DESeq2
Type: Package
Title: Differential gene expression analysis based on the negative
binomial distribution
-Version: 1.12.3
+Version: 1.12.4
Author: Michael Love (HSPH Boston), Simon Anders, Wolfgang Huber (EMBL
Heidelberg)
Maintainer: Michael Love <michaelisaiahlove at gmail.com>
@@ -24,4 +24,4 @@ biocViews: Sequencing, ChIPSeq, RNASeq, SAGE, DifferentialExpression,
GeneExpression, Transcription
RoxygenNote: 5.0.1
NeedsCompilation: yes
-Packaged: 2016-05-26 03:48:34 UTC; biocbuild
+Packaged: 2016-08-08 03:45:27 UTC; biocbuild
diff --git a/R/core.R b/R/core.R
index 831d658..3e112e4 100644
--- a/R/core.R
+++ b/R/core.R
@@ -570,7 +570,7 @@ estimateDispersionsGeneEst <- function(object, minDisp=1e-8, kappa_0=1,
object <- sanitizeRowRanges(object)
if (is.null(modelMatrix)) {
- modelMatrix <- model.matrix(design(object), data=colData(object))
+ modelMatrix <- getModelMatrix(object)
checkFullRank(modelMatrix)
if (nrow(modelMatrix) == ncol(modelMatrix)) {
stop("the number of samples and the number of model coefficients are equal,
@@ -754,7 +754,7 @@ estimateDispersionsMAP <- function(object, outlierSD=2, dispPriorVar,
}
if (is.null(modelMatrix)) {
- modelMatrix <- model.matrix(design(object), data=colData(object))
+ modelMatrix <- getModelMatrix(object)
} else {
message("using supplied model matrix")
}
@@ -872,7 +872,7 @@ estimateDispersionsPriorVar <- function(object, minDisp=1e-8, modelMatrix=NULL)
objectNZ <- object[!mcols(object)$allZero,,drop=FALSE]
aboveMinDisp <- mcols(objectNZ)$dispGeneEst >= minDisp*100
if (is.null(modelMatrix)) {
- modelMatrix <- model.matrix(design(object), data=colData(object))
+ modelMatrix <- getModelMatrix(object)
}
# estimate the variance of the distribution of the
# log dispersion estimates around the fitted value
@@ -1149,7 +1149,7 @@ nbinomWaldTest <- function(object, betaPrior, betaPriorVar,
# calculate Cook's distance
dispModelMatrix <- if (modelAsFormula) {
- model.matrix(design(object), data=colData(object))
+ getModelMatrix(object)
} else {
modelMatrix
}
@@ -1272,8 +1272,7 @@ estimateBetaPriorVar <- function(object,
# renaming in reverse:
# make these standard colnames as from model.matrix()
- convertNames <- renameModelMatrixColumns(colData(object),
- design(object))
+ convertNames <- renameModelMatrixColumns(colData(object),design(object))
colnamesBM <- sapply(colnamesBM, function(x) {
if (x %in% convertNames$to) {
convertNames$from[convertNames$to == x]
@@ -1284,7 +1283,7 @@ estimateBetaPriorVar <- function(object,
colnames(betaMatrix) <- colnamesBM
# this is the model matrix from an MLE run
- modelMatrix <- model.matrix(design(objectNZ), colData(object))
+ modelMatrix <- getModelMatrix(object)
modelMatrixType <- attr(object, "modelMatrixType")
@@ -1485,10 +1484,8 @@ nbinomLRT <- function(object, full=design(object), reduced,
# try to form model matrices, test for difference
# in residual degrees of freedom
- fullModelMatrix <- model.matrix(full,
- data=colData(object))
- reducedModelMatrix <- model.matrix(reduced,
- data=colData(object))
+ fullModelMatrix <- model.matrix(full,data=as.data.frame(colData(object)))
+ reducedModelMatrix <- model.matrix(reduced,data=as.data.frame(colData(object)))
df <- ncol(fullModelMatrix) - ncol(reducedModelMatrix)
} else {
if (betaPrior) {
@@ -1884,7 +1881,7 @@ fitNbinomGLMs <- function(object, modelMatrix=NULL, modelFormula, alpha_hat, lam
}
if (is.null(modelMatrix)) {
modelAsFormula <- TRUE
- modelMatrix <- model.matrix(modelFormula, data=colData(object))
+ modelMatrix <- model.matrix(modelFormula, data=as.data.frame(colData(object)))
} else {
modelAsFormula <- FALSE
}
@@ -1941,7 +1938,7 @@ fitNbinomGLMs <- function(object, modelMatrix=NULL, modelFormula, alpha_hat, lam
mu <- normalizationFactors * as.numeric(2^betaMatrix)
logLike <- rowSums(dnbinom(counts(object), mu=mu, size=1/alpha, log=TRUE))
deviance <- -2 * logLike
- modelMatrix <- model.matrix(~ 1, colData(object))
+ modelMatrix <- model.matrix(~ 1, as.data.frame(colData(object)))
colnames(modelMatrix) <- modelMatrixNames <- "Intercept"
w <- (mu^-1 + alpha)^-1
xtwx <- rowSums(w)
@@ -2279,7 +2276,7 @@ covarianceMatrix <- function(object, rowNumber) {
# convert coefficients to log scale
coefColumns <- names(mcols(object))[grep("log2 fold change",mcols(mcols(object))$description)]
beta <- log(2) * as.numeric(as.data.frame(mcols(object)[rowNumber,coefColumns,drop=FALSE]))
- x <- model.matrix(design(object), colData(object))
+ x <- getModelMatrix(object)
y <- counts(object)[rowNumber,]
sf <- sizeFactors(object)
alpha <- dispersions(object)[rowNumber]
@@ -2357,7 +2354,7 @@ fitGLMsWithPrior <- function(object, maxit, useOptim, useQR, betaPriorVar) {
} else {
# we can skip the first MLE fit because the
# beta prior variance and hat matrix diagonals were provided
- modelMatrix <- model.matrix(design(objectNZ), colData(object))
+ modelMatrix <- getModelMatrix(object)
H <- assays(objectNZ)[["H"]]
mleBetaMatrix <- as.matrix(mcols(objectNZ)[,grep("MLE_",names(mcols(objectNZ))),drop=FALSE])
}
@@ -2786,3 +2783,6 @@ and then provide your custom matrix to 'full' argument of DESeq().
}
}
+getModelMatrix <- function(object) {
+ model.matrix(design(object), data=as.data.frame(colData(object)))
+}
diff --git a/R/expanded.R b/R/expanded.R
index d5248d0..d3fdb6f 100644
--- a/R/expanded.R
+++ b/R/expanded.R
@@ -7,7 +7,7 @@ makeExpandedModelMatrix <- function(object) {
coldata[[f]] <- relevel(coldata[[f]],"_null_level_")
coldata[[f]][nrow(coldata)] <- "_null_level_"
}
- mm0 <- model.matrix(design(object), data=coldata)
+ mm0 <- model.matrix(design(object), data=as.data.frame(coldata))
# these can appear when interactions are present without main effect variables
nullLvls <- grepl("_null_level_",colnames(mm0))
mm <- mm0[-nrow(mm0),!nullLvls,drop=FALSE]
diff --git a/build/vignette.rds b/build/vignette.rds
index 05d2369..a30845c 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/inst/doc/DESeq2.pdf b/inst/doc/DESeq2.pdf
index 3f0bb3c..5a7c96f 100644
--- a/inst/doc/DESeq2.pdf
+++ b/inst/doc/DESeq2.pdf
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trailer
<< /Size 1057
/Root 1055 0 R
/Info 1056 0 R
-/ID [<7DF726F6845E896E34F88A3C87A17B07> <7DF726F6845E896E34F88A3C87A17B07>] >>
+/ID [<DD23239E18C18C7CD09EF0323A8F4305> <DD23239E18C18C7CD09EF0323A8F4305>] >>
startxref
-679659
+679614
%%EOF
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-med/r-bioc-deseq2.git
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