Changes in version 3.0.8: o Bug fix to adjustedProfileLik when design matrix has just one column. Changes in version 3.0.7: o Fix typo in Section 4.3.6 of User's Guide. o Bug fix to glmLRT when contrast was a matrix with one column. Changes in version 3.0.6: o glmLRT now permits contrast to be a matrix with multiple columns, making the behavior analogous to the coef argument. Changes in version 3.0.5: o Bug fix to subsetting for DGEGLM objects. Changes in version 3.0.4: o Adding prior.count.total=0 to the calls to glmFit() in several functions, to avoid unnecessary calculations of predicted fold-changes. (The change saves nearly half of computation time for glm dispersion estimation.) o In glmFit(), the maximum number of iterations in the call to mglmLevenberg() is reduced to 250. Previously the number of iterations was so large that the Levenberg damping parameter could become exactly zero due to floating point underflow, making an infinite loop possible in rare pathological cases. Changes in version 3.0.3: o Bug fix to plotMDS.DGEList to make consistent use of norm.factors. Changes in version 3.0.2: o Include a code example in Section 2.9 (item 2) of the User's Guide about how to pre-specify the dispersion value. Changes in version 3.0.1: o New section on between and with subject designs in User's Guide. Correct a number of typos in Section 3.4 of the User's Guide. o Bug fix to plotBCV() when logCPM is not found in data object. Changes in version 3.0.0: o New chapter in the User's Guide covering a number of common types of experimental designs, including multiple groups, multiple factors and additive models. Many other updates to the User's Guide and to the help pages. o New function edgeRUsersGuide() to open the User's Guide in a pdf viewer. o Many functions have made faster by rewriting the core computations in C++. This includes adjustedProfileLik(), mglmLevenberg(), maximizeInterpolant() and goodTuring(). o New argument verbose for estimateCommonDisp() and estimateGLMCommonDisp(). o The trended dispersion methods based on binning and interpolation have been rewritten to give more stable results when the number of genes is not large. o The amount by which the tagwise dispersion estimates are squeezed towards the global value is now specified in estimateTagwiseDisp(), estimateGLMTagwiseDisp() and dispCoxReidInterpolateTagwise() by specifying the prior degrees of freedom prior.df instead of the prior number of samples prior.n. o The weighted likelihood empirical Bayes code has been simplified or developed in a number of ways. The old functions weightedComLik() and weightedComLikMA() are now removed as no longer required. o The functions estimateSmoothing() and approx.expected.info() have been removed as no longer recommended. o The span used by estimateGLMTagwiseDisp() is now chosen by default as a decreasing function of the number of tags in the dataset. o New method "loess" for the trend argument of estimateTagwiseDisp, with "tricube" now treated as a synonym. o New functions loessByCol() and locfitByCol() for smoothing columns of matrix by non-robust loess curves. These functions are used in the weighted likelihood empirical Bayes procedures to compute local common likelihood. o glmFit now shrinks the estimated fold-changes towards zero. The default shrinkage is as for exactTest(). o predFC output is now on the natural log scale instead of log2. o mglmLevenberg() is now the default glm fitting algorithm, avoiding the occasional errors that occurred previously with mglmLS(). o The arguments of glmLRT() and glmQLFTest() have been simplified so that the argument y, previously the first argument of glmLRT, is no longer required. o glmQLFTest() now ensures that no p-value is smaller than what would be obtained by treating the likelihood ratio test statistic as chisquare. o glmQLFTest() now treats tags with all zero counts in replicate arrays as having zero residual df. o gof() now optionally produces a qq-plot of the genewise goodness of fit statistics. o Argument null.hypothesis removed from equalizeLibSizes(). o DGEList now longer outputs a component called all.zeros. o goodTuring() now longer produces a plot. Instead there is a new function goodTuringPlot() for plotting log-probability versus log-frequency. goodTuring() has a new argument 'conf' giving the confidence factor for the linear regression approximation. o Added plot.it argument to maPlot(). 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.