Changes in version 2.6.12: o 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. Changes in version 2.6.11: o 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. Changes in version 2.6.10: o Removing link to shell.exec in edgeRUsersGuide.Rd, because it's Windows-specific. Change is not user visible. Changes in version 2.6.9: o Bug fix to glmLRT when there is only one gene but multiple coefs. Changes in version 2.6.8: o Fix to glmFit, which was missing the ... argument in the generic. Changes in version 2.6.7: o bug fix to calcNormFactors() when object was a matrix, method was "TMM" and refColumn was NULL. Changes in version 2.6.6: o New edgeR.Rnw, pointing to edgeRUsersGuide(). Changes in version 2.6.5: o gof() now optionally produces a qq-plot of the genewise goodness of fit statistics. Changes in version 2.6.4: o New function edgeRUsersGuide(). Changes in version 2.6.3: o Bug fix: glmFit generic was not passing on prior.count.total argument to default function. Changes in version 2.6.2: o glmFit now shrinks the estimated fold-changes. The default is the same as for exactTest(). o predFC output is now on the natural log scale instead of log2. o New argument verbose for estimateCommonDisp() and estimateGLMCommonDisp(). o Many updates to the help pages and the User's Guide. Changes in version 2.6.0: o edgeR now depends on limma. o 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. o 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. o plotSmear() and plotMeanVar() now use logCPM instead of logConc. o New function glmQLFTest() provides quasi-likelihood hypothesis testing using F-tests, as an alternative to likelihood ratio tests using the chisquare distribution. o New functions normalizeChIPtoInput() and calcNormOffsetsforChIP() for normalization of ChIP-Seq counts relative to input control. o 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. o 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. o New function estimateTrendedDisp() analogous to estimateGLMTrendedDisp() but for classic edgeR. o The algorithms implemented in estimateTagwiseDisp() now uses fewer grid points but interpolates, similar to estimateGLMTagwiseDisp(). o 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. o New user-friendly function plotBCV() displays estimated dispersions. o New argument target.size for thinCounts(). o New utility functions getDispersion() and zscoreNBinom(). o dimnames() methods for DGEExact, DGELRT and TopTags classes. o Function pooledVar() removed as no longer necessary. o Minor fixes to various functions to ensure correct results in special cases. Changes in version 2.4.0: o New function spliceVariants() for detecting alternative exon usage from exon-level count data. o 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. o estimateTagwiseDisp() now includes an abundance trend on the dispersions by default. o exactTest() now uses tagwise.dispersion by default if found in the object. o estimateCRDisp() is removed. It is now replaced by estimateGLMCommonDisp(), estimateGLMTrendedDisp() and estimateGLMTagwiseDisp(). o Changes to glmFit() so that it automatically detects dispersion estimates if in data object. It uses tagwise if available, then trended, then common. o 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(). o New function cutWithMinN() used in binning methods. o glmFit() now S3 generic function, and glmFit() has new method argument specifying fitting algorithm. o DGEGLM objects now subsettable. o 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. o Add as.data.frame method for TopTags objects. o New function cpm() to calculate counts per million conveniently. o Adding args to dispCoxReidInterpolateTagwise() to give more access to tuning parameters. o estimateGLMTagwiseDisp() now uses trended.dispersion by default if trended.dispersion is found. o Change to glmLRT() to ensure character coefficient argument will work. o Change to maPlot() so that any really extreme logFCs are brought back to a more reasonable scale. o estimateGLMCommonDisp() now returns NA when there are no residual df rather than returning dispersion of zero. o The trend computation of the local common likelihood in dispCoxReidInterpolateTagwise() is now based on moving averages rather than lowess. o Changes to binGLMDispersion() to allow trended dispersion for data sets with small numbers of genes, but with extra warnings. o dispDeviance() and dispPearson() now give graceful estimates and messages when the dispersion is outside the specified interval. o Bug fix to mglmOneWay(), which was confusing parametrizations when the design matrix included negative values. o mglmOneWay() (and hence glmFit) no longer produces NA coefficients when some of the fitted values were exactly zero. o 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. o Bug fix to mglmSimple() with matrix offset. o Bug fix to adjustedProfLik() when there are fitted values exactly at zero for one or more groups.