\name{edgeRnews}
\title{edgeR News}
\encoding{UTF-8}


\section{Version 2.6.12}{\itemize{
\item
There was a column name issue when the contrast argument to glmLRT() was a matrix with one column that caused topTags() to give an error.
The contrast argument is now dropped to a matrix to avoid this issue.
}}

\section{Version 2.6.11}{\itemize{
\item
exactTest() with dispersion=0 could potentially give an error from binomTest() because one of the pseudo counts from equalizeLibSizes() was negative.
Negative pseudo counts are now avoided.
}}

\section{Version 2.6.10}{\itemize{
\item
Removing link to shell.exec in edgeRUsersGuide.Rd, because it's Windows-specific.  Change is not user visible.
}}

\section{Version 2.6.9}{\itemize{
\item
Bug fix to glmLRT when there is only one gene but multiple coefs.
}}

\section{Version 2.6.8}{\itemize{
\item
Fix to glmFit, which was missing the ... argument in the generic.
}}

\section{Version 2.6.7}{\itemize{
\item
bug fix to calcNormFactors() when object was a matrix, method was "TMM" and refColumn was NULL.
}}

\section{Version 2.6.6}{\itemize{
\item
New edgeR.Rnw, pointing to edgeRUsersGuide().
}}

\section{Version 2.6.5}{\itemize{
\item
gof() now optionally produces a qq-plot of the genewise goodness of fit statistics.
}}

\section{Version 2.6.4}{\itemize{
\item
New function edgeRUsersGuide().
}}

\section{Version 2.6.3}{\itemize{
\item
Bug fix: glmFit generic was not passing on prior.count.total argument to default function.
}}

\section{Version 2.6.2}{\itemize{
\item
glmFit now shrinks the estimated fold-changes.
The default is the same as for exactTest().

\item
predFC output is now on the natural log scale instead of log2.

\item
New argument verbose for estimateCommonDisp() and estimateGLMCommonDisp().

\item
Many updates to the help pages and the User's Guide.
}}


\section{Version 2.6.0}{\itemize{

\item
edgeR now depends on limma.

\item
Considerable work on the User's Guide.
New case study added on Pathogen inoculated arabidopsis illustrating a two group comparison with batch effects.
All the other case studies have been updated and streamlined.
New section explaining why adjustments for GC content and mappability are not necessary in a differential expression context.

\item
New and more intuitive column headings for topTags() output.
'logFC' is now the first column.
Log-concentration is now replaced by log-counts-per-million ('logCPM').
'PValue'  replaces 'P.Value'.
These column headings are now inserted in the table of results by
exactTest() and glmLRT() instead of being modified by the show method for the TopTags object generated by topTags().
This means that the column names will be correct even when users access the fitted model objects
directly instead of using the show method.

\item
plotSmear() and plotMeanVar() now use logCPM instead of logConc.

\item
New function glmQLFTest() provides quasi-likelihood hypothesis testing using F-tests, as an alternative to likelihood ratio tests using the chisquare distribution.

\item
New functions normalizeChIPtoInput() and calcNormOffsetsforChIP()
for normalization of ChIP-Seq counts relative to input control.

\item
New capabilities for formal shrinkage of the logFC.
exactTest() now incorporates formal shrinkage of the logFC, controlled by argument 'prior.count.total'.
predFC() provides similar shrinkage capability for glms.

\item
estimateCommonDisp() and estimateGLMCommonDisp() now set the dispersion to NA when there is no replication,
instead of setting the dispersion to zero.
This means that users will need to set a dispersion value explicitly to use functions further down the analysis pipeline.

\item
New function estimateTrendedDisp() analogous to estimateGLMTrendedDisp() but for classic edgeR.

\item
The algorithms implemented in estimateTagwiseDisp() now uses fewer grid points but interpolates, similar to estimateGLMTagwiseDisp().

\item
The power trend fitted by dispCoxReidPowerTrend() now includes a positive asymptote.
This greatly improves the fit on real data sets.
This now becomes the default method for estimateGLMTrendedDisp() when the number of genes is less than 200.

\item
New user-friendly function plotBCV() displays estimated dispersions.  

\item
New argument target.size for thinCounts().

\item
New utility functions getDispersion() and zscoreNBinom().

\item
dimnames() methods for DGEExact, DGELRT and TopTags classes.

\item
Function pooledVar() removed as no longer necessary.

\item
Minor fixes to various functions to ensure correct results in special cases.

}}


\section{Version 2.4.0}{\itemize{

\item New function spliceVariants() for detecting alternative exon usage
      from exon-level count data.

\item A choice of rejection regions is now implemented for exactTest(),
      and the default is changed from one based on small probabilities
      to one based on doubling the smaller of the tail probabilities.
      This gives better results than the original conditional test when
      the dispersion is large (especially > 1).  A Beta distribution
      approximation to the tail probability is also implemented when
      the counts are large, making exactTest() much faster and
      less memory hungry.

\item estimateTagwiseDisp() now includes an abundance trend on the
      dispersions by default.
      
\item exactTest() now uses tagwise.dispersion by default if found in the
      object.
    
\item estimateCRDisp() is removed. It is now replaced by
      estimateGLMCommonDisp(), estimateGLMTrendedDisp() and
      estimateGLMTagwiseDisp().
    
\item Changes to glmFit() so that it automatically detects dispersion
      estimates if in data object. It uses tagwise if available, then
      trended, then common.

\item Add getPriorN() to calculate the weight given to the common
      parameter likelihood in order to smooth (or stabilize) the
      dispersion estimates. Used as default for estimateTagwiseDisp and
      estimateGLMTagwiseDisp().
    
\item New function cutWithMinN() used in binning methods.
    
\item glmFit() now S3 generic function, and glmFit() has new method
      argument specifying fitting algorithm.
    
\item DGEGLM objects now subsettable.
    
\item plotMDS.dge() is retired, instead a DGEList method is now defined for 
      plotMDS() in the limma package.  One advantage is that the plot can
      be repeated with different graphical parameters without recomputing
      the distances.  The MDS method is also now much faster.
    
\item Add as.data.frame method for TopTags objects.
    
\item New function cpm() to calculate counts per million conveniently. 
    
\item Adding args to dispCoxReidInterpolateTagwise() to give more access to 
      tuning parameters.
    
\item estimateGLMTagwiseDisp() now uses trended.dispersion by default if 
      trended.dispersion is found.
      
\item Change to glmLRT() to ensure character coefficient argument will work.
    
\item Change to maPlot() so that any really extreme logFCs are brought back 
      to a more reasonable scale.
      
\item estimateGLMCommonDisp() now returns NA when there are no residual
      df rather than returning dispersion of zero.

\item The trend computation of the local common likelihood in
      dispCoxReidInterpolateTagwise() is now based on moving averages
      rather than lowess.

\item Changes to binGLMDispersion() to allow trended dispersion for data
      sets with small numbers of genes, but with extra warnings.

\item dispDeviance() and dispPearson() now give graceful estimates and
      messages when the dispersion is outside the specified interval.

\item Bug fix to mglmOneWay(), which was confusing parametrizations when
      the design matrix included negative values.

\item mglmOneWay() (and hence glmFit) no longer produces NA coefficients
      when some of the fitted values were exactly zero.

\item Changes to offset behaviour in estimateGLMCommonDisp(),
      estimateGLMTrendedDisp() and estimateGLMTagwiseDisp() to fix bug.
      Changes to several other functions on the way to fixing bugs
      when computing dispersions in data sets with genes that have all
      zero counts.

\item Bug fix to mglmSimple() with matrix offset.
    
\item Bug fix to adjustedProfLik() when there are fitted values exactly
      at zero for one or more groups.
}}

