Changes in version 3.62.0 o New function fitFDistUnequalDF1() for improved empirical Bayes hyperparameter estimation. The new function is an alternative to the long-standing limma functions fitFDist() and fitFDistRobustly(). The new function gives special attention to contexts where the df1 argument is unequal between cases or can include small fractional values, such as those resulting from edgeR's voomLmFit() or from the edgeR 4.0 quasi-likelihood pipeline. The new function uses profile maximum likelihood instead of moment estimation to estimate df.prior, and so may be more accurate than fitFDist() or fitFDistRobustly() even when the df1 values are all equal. The new hyperpameter estimation will be used by default by eBayes(), treat() and squeezeVar() whenever the residual degrees of freedom are unequal, but users can request legacy behavior for those functions by setting `legacy=TRUE`. o New argument `adaptive.span` for voomWithQualityWeights() and normalizeCyclicLoess(), with the same purpose as the same argument for voom(). If `TRUE` then an optimal value for the span is chosen based on the number of genes in the dataset. voom() and voomWithQualityWeights() now return the chosen `span` as part of the output object if `adaptive.span=TRUE`. o contrasts.fit() now converts the coefficient name "(Intercept)" created automatically by model.matrix() to "Intercept", same as is done by makeContrasts(). This should avoid inconsistent coefficient names between the fitted model object and contrast matrices created by makeContrasts(). o New argument `keep.EList` for voomaLmFit(), similar to the same argument for edgeR::voomLmFit(). The observation weights generated by voomaLmFit() are now stored only if `keep.EList=TRUE`. o voomaByGroup() now uses the overall mean-variance trend for groups with only one sample, for which a separate trend is not estimable. o Add Ravindra et al (2023) as a reference and Mengbo Li as an author to the voomaByGroup() help page. Emphasize in the help details that only simple design matrices are supported by this function. o Revise the eBayes/treat help page to further clarify the `legacy` and `fc` arguments. o Bug fix to voomaByGroup() to save variance plot parameters correctly when `save.plot=TRUE`. o Remove argument `zero.weights` from the MArrayLM method for plotMD() and plotMA(). o Fix bug in the MArrayLM methods for plotMD() and plotMA(), which were attempting to access a `weights` component of the fitted model object, analogously to what plotMD() does with the EList or MAList objects, even though MArrayLM objects do not contain a `weights` component. o Use of the stats package digamma() function replaced with statmod's logmdigamma() in fitFDist(), fitFDistUnequalDF1() and intraspotCorrelation(). Slight improvements to speed, accuracy and code simplicity, but results should remain the same to at least 13 significant figures. Changes in version 3.60.0 o The default settings for the `small.n` and `min.span` arguments of chooseLowessSpan() have been increased, increasing the chosen span value. o New argument `adaptive.span` for voom(). If `TRUE`, then chooseLowessSpan() is used to select an optimal `span` value depending on the number of genes, same as is done for vooma(), voomaByGroup() and voomaLmFit(). o The plot information saved by voom() when `save.plot=TRUE` now includes `pch` and `cex` plotting character settings. o The default `span` set by vooma() is increased slightly to the value given by `chooseLowessSpan(ngenes, small.n=50, min.span=0.3, power=1/3)`. A new argument `legacy.span` had been added to optionally restore the old default settings for `span` for users who want backward compatibility. o vooma() argument `covariate` renamed to `predictor`. o vooma() has been revised in several other ways to match the behavior of voom() more closely. The order of arguments has been adjusted to match voom() and lmFit(). A new argument `save.plot` has been added similar to the same argument for voom(). Changes to have also been made to the title and xlab for the variance trend plot when `predictor` is non-NULL. o New function voomaLmFit() with the same functionality as vooma() but which automates the estimation of sample weights and intrablock correlation from vooma(). It produces an MArrayLM fit object instead of an EList object. It is the analogous to edgeR::voomLmFit() but for continuous microarray-like data. o Edits to the help pages for decideTests(), voom(), voomWithQualityWeights(), vooma() and "11RNAseq". Example added to the vooma() help page. o Add RNA-seq to package description. o Add Charity Law, Goknur Giner and Mengbo Li to author list in package description. Changes in version 3.58.0 o New argument `covariate` for vooma() that allows the variance trend to depend on an extra covariate in addition to average log-expression. This allows vooma() to take account of any predictor that is correlated with the precision of each expression value. o New argument 'group' to simplify the typical calling sequence for removeBatchEffect(). The `group` argument provides an alternative and possibly simpler way to specify the experimental conditions to be preserved. o treat() now works when there are NA coefficients. Previously it gave an error if any estimated coefficient was NA. o Fix behaviour of topTable() with `adjust.method=NULL` so that it gives "BH" adjustment as documented. Previously the argument was passed to p.adjust(), which gave "holm" adjustment. o Convert Introduction vignette from Sweave/pdf from to Rmarkdown/html. The Introduction vignette is also somewhat expanded. o cameraPR() has been addeed to the `10GeneSetTests.Rd` help topic. o The pdf limma User's Guide is now formally listed as a vignette. o Non-user-visible changes: limma now imports statmod rather than suggesting it. BiocStyle has been added to the Suggests field of DESCRIPTION. Changes in version 3.56.0 o Rename readSampleInfoFromGEO() to sampleInfoFromGEO(). o Add new argument `p.value` for topGO() and topKEGG(). o Fix a problem with inconsistent pathway names from KEGG. Due to a change on the KEGG website, pathway names from getGeneKEGGLinks() have "path:" prefixes but pathway names from getKEGGPathwayNames() do not. To make the pathway names consistent, getGeneKEGGLinks() now removes the "path:" prefix. o eBayes() now checks whether `fit` is a list object before undertaking other tests. o Revise voom.Rd to specify that y-values in voom plot are sqrt residual standard deviations. o Update out-of-date URLs in the help pages and the User's Guide. Convert reference URLs to DOIs. o Convert CITATION to use the newer bibentry() instead of citEntry(). Changes in version 3.54.0 o New function goanaTrend() to estimate a covariate-dependent trend in the probability of differential expression for use in goana() and kegga() with `trend=TRUE`. The trend estimated by goanaTrend() is squeezed slightly towards constancy to provide stability when the number of DE genes is small. The amount of squeezing decreases with the number of DE genes. goana() and kegga() now call goanaTrend() when the `trend` argument is used. When `plot=TRUE` the plot is now created by goanaTrend() instead of by barcodeplot(). o Rename argument `prior.prob` to `null.prob` in goana() and kegga(). o Update goana() and kegga() code to catch NA gene IDs or covariate values. o goana() and kegga() no longer give an error when a trend is estimated but there are no DE genes. o kegga() now uses https instead of http links when reading from rest.kegg.jp. o Add new argument `fc` to topTable() and topTableF(). Change default for `lfc` to NULL instead of 0. o lmFit() now checks explicitly for NAs in the design matrix. o Update references listed on help pages to use DOIs instead of URLs. Changes in version 3.52.0 o New function readSampleInfoFromGEO(). o Allow the `trend` argument of eBayes() to specify a general variance covariate. o More detailed checking and error messages when the `object` input to lmFit() is a data.frame. o Improve checking for incorrectly specified `contrasts` argument in contrasts.fit(). Changes in version 3.50.0 o Improve help pages for fry() and barcodeplot(). o Revise Section 18.1.10 of User's Guide so that coloring of X and Y genes is consistent between plots. o Fix bug in the MDS class method of plotMDS() (introduced in the previous release) when new `dim.plot` values are set. o Fix bug in read.idat() when `annotation` contains NA values. Changes in version 3.48.0 New functionality o Explicitly setting `weights=NULL` in a call to lmFit() no longer over-rides the `weights` value found in `object`. Default settings in lmFit() changed from `ndups=` and `spacing=1` to `ndups=NULL` and `spacing=NULL`, although this doesn't change function behavior from a user point of view. o A number of improvements to duplicateCorrelation() to make the results more robust and to make the interface consistent with lmFit(). duplicateCorrelation() sets `weights` same as lmFit(). Setting `weights=NULL` in the function call no longer overwrites weights found in `object`. duplicateCorrelation() now checks whether the block factor is spanned by the design matrix. If so, it returns intrablock correlations of zero with a warning. Previously this usage error was not specifically trapped and could lead to correlations that were or NA or close to 1 depending on floating point errors. duplicateCorrelation() now issues a simplified message when design is not of full rank and uses message() to do so instead of cat(). In terms of output, duplicateCorrelation() now bounds the genewise correlations away from the upper and lower bounds by 0.01 so that the correlation matrix will always be positive-definite. There is also a fix to the value returned by duplicateCorrelation() when no blocks or duplicates are present. o New argument `fc` for treat() so that the fold-change threshold can optionally be specified on the fold-change scale rather than as a log2-fold-change. o plotMDS() no longer calls cmdscale() but instead performs the necessary eigenvector computations directly. Proportion of variance explained by each dimension is now computed and is optionally added to the dimension labels. The `ndim` argument is now removed. All eigenvectors are now stored so that plotMDS.MDS does not need to recompute them when different dimensions are plotted. o New arguments `path` and `bgxpath` for read.idat(). read.idat() now checks for gzipped IDAT files and, if detected, gives an informative error message. read.idat() now checks for existence of input files before calling illuminaio read functions. Other code improvements o The average log-expression column written by write.fit() now has column heading "AveExpr" to match the output from topTable(). The column was previously called "A". Documentation o Additional documentation for the `design` argument of `lmFit` using the term "samples" instead of "arrays" and mentioning that the design matrix defaults to `object$design` when that component is not NULL. o duplicateCorrelation() help page revised including new code example. o Help page for voom() now explains that the design matrix will be set from the `group` factor of the DGEList object if available. o coolmap() help page now clarifies which heatmap.2() arguments are reserved and which can be included in the coolmap call. Bug fixes o Fix typo in voom() warning when negative counts are detected. Changes in version 3.46.0 New functionality o New function chooseLowessSpan(). o fitFDist() with a non-NULL `covariate` now fits a smoother trend (with fewer spline knots) than before unless there are at least 30 observations. This also affects squeezeVar() and eBayes() with `trend=TRUE`. o New argument `output.style` to weightedLowess(). o write.fit() now outputs a blank column heading for the row names so that the number of column names in the delimited output file will agree with the number of columns (similar to write.csv in the base package). Code improvements o Subsetting and contrasts.fit() now work on MArrayLM objects even if the cov.coefficients component is absent. o volcanoplot() now prompts user to run eBayes() if the object doesn't contain p-values. o Add checks and more informative error messages to getEAWP() and lmFit() when the data object is missing, NULL or has zero rows. o Subsetting for RGList, MAList, EListRaw, EList, MArrayLM or TestResults objects now accepts arguments other than `i` or `j` but ignores them without an error. This is relevant if a user adds a `drop` argument by analogy with matrix subsetting. Previously that produced an error. o Subsetting of TestResults now requires two arguments, same as all of the other data object classes listed above. Previously single index subsetting (for example object[i]) was allowed. o Improved treatment of zero weights by weighted.median(). o More consistent use of rep.int(), rep_len() and rep() throughout the package. o Replace NA with NA_real_ where appropriate. o Add a message to topTableF() to warn that the function is obsolete and will be removed in a future version of limma. Remove usages of topTableF() from the User's Guide. o Remove obsolete function toptable(), which has been officially deprecated in favor of topTable() since 1 Feb 2018. Documentation o Revise and expand the weightedLowess help page. o Add more explanation about "heirarchical" method in the details section of the decideTests() help page. o Add Note to voom help page to emphasise that voom is not an appropriate tool to analyse expresssion measures that have been normalized for library size. o Revise the help page for plotMDS() to clarify that the function can produce PCA plots as well as PCoA plots. o Add example to EList-class help page. o Edits to the eBayes help page. o Edit concept entries for help files (for reference manual index). Bug fixes o Revise write.fit() so that column names of the output file are correct when `digits=NULL` and the `fit` object contains only one coefficient column. Previously the same contrast name was repeated as the column name for the coefficients, t-statistics and p.values, making them hard to distinguish. o Bug fix to arrayWeights() when `y` contains NAs, the design matrix has several columns and some but not all genes have no residual df. Changes in version 3.44.0 New functionality o Add new argument `package` to changeLog(). changeLog() now works for any package rather than only for limma. Code improvements o Improved treatment of NA coefficients by contrasts.fit(). contrasts.fit() has always removed coefficients that were NA because of singularities in the design matrix, but any extra NA coefficients caused by NA expression values would cause all the stdev.unscaled values returned by contrasts.fit() to be NA for those genes. contrasts.fit() now returns non-NA coefficients and non-NA stdev.unscaled values if all the NA coefficients are multiplied by zero contrast multipliers. o Speed improvement for contrasts.fit() if some input coefficients are not involved (have zero multipliers) in any of the contrasts. o contrasts.fit() now preserves the `pivot` component of the fit object instead of removing it. o voom now checks explicitly for NA or negative counts and gives an informative error message. o fitFDist() now allows a covariate trend even with fewer than 4 useable observations. (Useable means positive residual df and finite variance and covariate values.) o Internal code cleaned up to avoid partial matching of function arguments, attributes or list component names. The automatic package tests are now run with the warnPartialMatchArgs, warnPartialMatchAttr and warnPartialMatchDollar options all set to TRUE. Documentation o More explanation of `covariates` argument to removeBatchEffects(). Bug fixes o Fix bug in contrasts.fit() when some coefficients are NA due to NA expression values and the corresponding contrast multipliers are zero. The new code fixes the improvement originally introduced in limma 3.35.9. The new code will return non-NA contrasts if the NA coefficients all get 0 weight in the contrast. Previous code was correct if all genes had the same NA coefficients but could give an avoidable NA result if some genes had NA coefficients but the first gene did not. o Improvements to subsetting MArrayLM objects by column. Previously the presence of non-estimable coefficients sometimes caused the cov.coefficient matrix to be subsetted incorrectly; that is now fixed. Subsetting zero columns is now allowed even when F-statistics are present. o Fix to classifyTestsF() when one or more of the coefficients have zero variance. Previously the zero variance will introduce NAs into the correlation matrix and hence cause an error. This is now avoided. This also fixes eBayes() when one or more of the coefficients are identically zero (usually caused by an all-zero contrast). Changes in version 3.42.0 New functionality o New head() and tail() methods for all limma data classes. o New unique() and subsetting methods for TestResults objects. Previously a subsetted TestResults object became an ordinary numeric matrix, but now the TestResults class is preserved. Single index subsetting is also allowed but produces a numeric vector. o New argument `gene.weights` for fry() so as to match the arguments and behavior of mroast(). Previously gene weights could be input to fry() only through the `index` argument. fry's mixed p-values now take account of gene weights. Previously gene weights were not used in fry's mixed p-value calculation. o Add arguments `block`, `correlation` and `weights` to voom() and remove the `...` argument. o Update the arguments of voomWithQualityWeights() to match the revisions to arrayWeights() in limma 3.40.0. Add new argument `var.group`. o New arguments `xlab`, `ylab` and `main` for plotFB(). The default plot title now includes the column name from the data object. Default for `pch` increased to 0.3. o plotWithHighlights and plotMD.MArrayLM now gives special treatment to the case that `status` is a TestResults object. Code improvements o roast() and mroast() now use less memory. Memory usage now remains bounded regardless of the number of rotations used. Both functions are faster than before when approx.zscore=TRUE. A new argument `legacy` is added to allow users to turn these improvements off if they wish to reproduce numerical results from limma 3.40.0 exactly. The default number of rotations `nrot` is increased from 999 to 1999. A bug has been fixed for roast() and mroast() when `index` is a character vector of geneids or rownames. Previously this usage failed. o Complete rewrite of zscoreT(), which is now somewhat faster and offer two new options for the normalizing transformation. The new argument `method` indicates which transformation is used when `approx=TRUE`. The default approximation method is changed from "hill" to "bailey". zscoreT() now allows NA values when `approx=TRUE` and works correctly for all valid df values. Previously `approx=TRUE` returned NA results for `df` infinite or <= 0.05. A simple robust approximation from Wallace (1959) has been introduced to handle very large or very small `df` values. Previously `approx=FALSE` treated any `df` greater than 10000 as infinite; this threshold is now raised to 1e300. o tZScore() is slightly faster. o All uses of the approx() function now set `ties=list("ordered",mean)`, except for fitFDistRobustly() which uses `ties=mean`. The change was prompted by a change in R 3.6.0 whereby using the default for `ties` generates a warning message whenever ties are present in `x`. The new code gives the same results but is very slightly faster and avoids the warning message. limma now depends on R >= 3.6.0 because of this change. Documentation o Update Yoruba case study in User's Guide to call voom() and duplicateCorrelation() twice each instead of once. o Update Users Guide: update Rsubread reference, correct remarks about prior distribution for 1/sigma^2 in Section 13.2 and rerun the Yoruba and Pasilla case studies. o Use "RNA-seq" and "ChIP-seq" consistently in the documentation instead of "RNA-Seq" and "ChIP-Seq". Bug fixes o Fix bug in barcodeplot() when `index` or `index2` are character vectors. Changes in version 3.40.0 o Major rewrite of arrayWeights() to improve speed and stability. The arrayWeightsSimple() function has been removed and its functionality incorporated into arrayWeights(). A new 'var.group' argument has been added to simplify specification of the variance design matrix. The weights are now squeezed slightly towards unit and a new argument 'prior.n' has been added to control the prior weight with which the array weights are squeezed. arrayWeights() now chooses between the REML and gene-by-gene algorithms automatically by default. REML is chosen when there are no prior weights or missing values and gene-by-gene is used otherwise. arrayWeights() now checks for and skips over any genes with zero residual variances. o voom() now checks for experiments that have no replication. In this case it now returns weights that are all 1, with an informative warning but no error. Two references have been added to the voom help page. o classifyTests.Rd now focuses on nested F-tests. The function FStat() is now removed. It was a convenience wrapper for classifyTestsF() with fstat.only=TRUE but is not used often enough to justify itself. classifyTestsP() is no longer exported as it is not intended to be called by users. The function classifyTestsT() is now removed, having been superceded by more sophisticated multiple testing methods. o contrasts.fit() now removes any test statistics or p-values found in the fit object. This avoids any potential mis-match between coefficients and test statistics if eBayes() has been run on the object previously. More details have been added to the warning about approximations in the contrasts.fit() help page. o The old function ebayes(), which was deprecated a year ago in favor of eBayes(), is now removed. o The fitted() and residuals() methods for MArrayLM objects now give an informative error message if the object contains contrasts instead of the original coefficients. o Rewrite Filtering chapter of User's Guide. o Add Research Fields to the biocViews entry in the package DESCRIPTION file. Changes in version 3.38.0 o New function plotExonJunc() to plot results from diffSplice(). o New function logsumexp(). o New argument hl.col for volcanoplot(), allowing users to specify the color for the gene names when highlight > 0. o barcodeplot() no longer assumes that 'statistic' has unique names. Previously it returned an error if names(statistic) contained any duplicated values. o The colors "blue", "red" and "yellow" used by coolmap() changed to "blue2", "red2" and "yellow2" when used in a color panel with white. o goana.Rd now explains more explicitly that p-values are unadjusted for multiple testing. o arrayWeights.Rd now mentions minimum dimensions for expression object. o More advice on how to choose 'lfc' added to the treat() help page. o Minor bug fix to the mixed p-value from roast() and mroast() when set.statistic="floormean". o Bug fix for cumOverlap(), which was under-counting overlaps in some cases. Changes in version 3.36.0 o New arguments 'quote' and 'row.names' for write.fit(). Write.fit() now outputs row.names by default, which previously were suppressed. o coolmap() will now accept an arbitrary vector of colors. A new preset "whitered" panel is now also supported. o In the past, contrasts.fit() always returned NA contrasts for genes with any NA fitted coefficients. contrasts.fit() will now ignore an NA coefficient if the contrast multiplier is zero for that coefficient. o The treat() default for 'lfc' changed from lfc=0 to lfc=log2(1.2). o kegga() and goana() now check whether a data.frame has been input by mistake and generates an error. Previously a data.frame value for 'de' was interpreted as a list of gene sets without any error. o voomWithQualityWeights() now returns 'sample.weights' as a column of the 'targets' data.frame instead of as a separate vector. o "TestResults" objects now include a 'labels' attribute, defaulting to c("Down","NotSig","Up"). o Functions ebayes() and toptable(), long ago replaced by eBayes() and topTable(), are now formally deprecated. o Update the User's Guide case study that analyses Lrp- E. Coli samples and profiled by Affymetrix arrays. o Update the Agilent single-channel case study in Section 17.4 of the Users' Guide to use the Agilent gIsWellAboveBG detection column. o Comments added to voom.Rd and eBayes.Rd to clarify the relationship between limma-trend and voom. o In the kegga help page, clarify default gene ID system used by kegga() when species="dme". o Update wsva() help page to describe correct number of columns for output. o bug fix for goana() when 'covariate' is specified and some of the 'universe' genes don't have GO annotation. o Fix to how roast(), mroast() and fry() handle weights. The 'weight' argument can now be a matrix, or a vector of length nrow(y), or a vector of length ncol(y). This allows the functions to accept precision weights, or gene weights, or sample weights. Changes in version 3.34.0 o read.idat() can now handle idat files in SNP format. o New argument 'sort.by' for topSplice(). o diffSplice() now treats any NA geneid as a separate gene with one exon. Previously all NA geneids were treated as the same gene. o Bug fixes for plotSplice() which, in some circumstances, was not highlight significant exons. o Default value for 'style' in volcanoplot() changed to "p-value" instead of "p". This doesn't change the behavior of the function. o volcanoplot() didn't work with MArrayLM objects produced by treat(). Now fixed. o The gene.info.file argument of alias2SymbolUsingNCBI() will now optionally accept a data.frame instead of a file name. o alias2SymbolUsingNCBI() now always produces a data.frame. Previously a single-column data.frame was returned as a vector. o Bug fixes to camera() and cameraPR() when 'index' contains character row names. o New argument 'restrict.universe' for the default kegga() method. The default is now not to the restrict the universe to genes with KEGG annotation. o goana.default() code is rewritten to more closely match kegga.default. Slight speed improvement. Prior probabilities are now computed using the restricted universe with GO annotation instead of the whole universe. o Bug fix for kegga() when trend=TRUE or a prior.prob or covariate is specified. Previously the Down p-values were substantially too small and the Up p-values were too large. o plotWithHighlights() checks whether 'status' is a factor and converts to character. o Bug fixes for beadCountWeights(). Default is now set correct for 'design' and the function now works correctly when 'y' is an EList object contain bead standard errors but not standard deviations. Changes in version 3.32.0 o New function cameraPR(), which implemented a pre-ranked version of camera(). o New function alias2SymbolUsingNCBI(), which converts gene aliases or synonyms into official gene symbols using an NCBI gene-info file. o New function wsva() for weighted surrogate variable analysis. o New function coolmap(). This is essentially a wrapper for the heatmap.2() function in the ggplots package, but with sensible default settings for genomic log-expression data. o decideTests() is now an S3 generic function with a default method and a method for MArrayLM objects. decideTests() now selects all null hypotheses as rejected if p.value=1. o length() methods removed all limma data objects (objects of class EList, EListRaw, RGList, MAList or MArrayLM). length(x) will now return the number of list components in the object rather than the number of elements in the expression matrix. o New argument 'style' for volcanoplot(). The default is now to use -log10(p-value) for the y-axis instead of the B-statistic. o New argument 'xlab' for barcodeplot(). o New argument 'col' for plotSA(). plotSA() now longer plots a lowess curve trend, but if appropriate both high and low outlier variances are highlighted in a different color. o Argument 'replace.weights' removed from voomWithQualityWeights(). The function now always produces an EList, similar to voom(). The default behavior of the function is unchanged. o barcodeplot() now ranks statistics from low to high, instead of from high to low, following the usual style of axes in R plots. This means that left and right are now interchanged. o plotSA() now plots quarter-root variances instead of log2(variances). o Default for 'legend' argument of plotWithHighlights() changed from "topleft" to "topright". o fitFDist() now estimates the scale by mean(x) when df2 is estimated to be Inf. This will make the results from eBayes() less conservative than before when df.prior=Inf. o plotSA() now indicates, by way of an open plotting symbol, any points that have low robust df.prior values. o Clearer error message from fitFDistRobustly() when some variances are zero. o C functions are now registered using R_registerRoutines. o Bug fix for contrastAsCoef() when there is more than one contrast. Previously the coefficients for the transformed design matrix were correct only for the first contrast. o Bug fix for kegga() when the universe is explicitly specified. o Bug fix for fitFDistRobustly() when there is an extreme outlier. Previously floating point underflow for the outlier p-value could cause an error. o Bug fix to mroast(), which was ignoring 'geneid' argument. o Bug fix to printHead() for arrays with 1 column. Changes in version 3.30.0 o New function cumOverlap() to analyse the overlap between two ordered lists. o New function detectionPValues() to compute detection p-values from negative control probes. o New function fitmixture() to estimate genewise fold changes and expression values from mixture experiments. Previously this funtion was only available as part of the illumina package available from http://bioinf.wehi.edu.au/illumina o New function logcosh() to compute log(cosh(x)) accurately without floating underflow or overflow. o The default settings for the 'inter.gene.cor' and 'use.neg.cor' arguments of camera() have been changed. camera() now uses by default a preset value for the inter-gene correlation. This has the effect that it tends to rank highly co-regulated, biologically-interpretable more highly than before. o New flexibility for the roast() and mroast() functions, similar to that previously implemented for fry(). The index vector for each gene set can now be a data.frame, allowing each gene set to have its own set of gene weights. The indices can now optionally be a vector of gene names instead of a vector of indices. roast() and mroast() now support the robust empirical Bayes option of squeezeVar(). roast() and mroast() can now accept, via '...', any argument that would be normally passed to lmFit() or eBayes(). o Slight change to the standardize="posterior.sd" method for fry(). o goana(), alias2Symbol() and alias2SymbolTable() now work for any species for which an Entrez Gene based organism package exists. o The 'species' argument of kegga() can now accept any Bioconductor species abbreviation. o topGO() now breaks ties by the number of genes in the GO Term and by the name of the Term if the p-values are equal. (This is the same behavior as topKEGG.) o New argument 'plot' for plotMDS(), to optionally allow an MDS object to be returned without making a plot. o New arguments 'annotation' and 'verbose' for read.idat(). The first of these to allows users to read any required columns from the manifest file. o New arguments 'pch.y' and 'pch.z' for plotRLDF. o Unnecessary argument 'design' removed from fitted.MArrayLM. o normalizeBetweenArrays() now checks whether the input 'object' is a data.frame, and converts to a matrix if possible. o duplicateCorrelation() now expands weights using expandAsWeights(), making it consistent with lmFit(). o The lowess line drawn by plotSA() is now more robust with respect to NA variances. o More informative error message from voom() when there is only one row of data. o More informative error message from getEAWP() and lmFit() when 'object' is a non-normalized data object. o Update Phipson et al (2016) reference for robust empirical Bayes. o Bug fix to fitFDistRobustly(), which in some circumstances was trying to save extra (undocumented) results that had not been computed. o Bug fixes for fry() with robust=TRUE or when 'index' has NULL names. o Bug fix to propTrueNull() when method="hist" and all the p-values are less than 1/nbins. o Bug fix to alias2Symbol() with expand.symbol=TRUE. Changes in version 3.28.0 o Improved capabilities and performance for fry(). fry() has two new arguments, 'geneid' and 'standardize'. The index argument of fry() can now be a list of data.frames containing identifiers and weights for each set. The options introduced by the standardize argument allows fry() to be more statistically powerful when genes have unequal variances. fry() now accepts any arguments that would be suitable for lmFit() or eBayes(). The 'sort' argument of fry() is now the same as for mroast(). o roast(), mroast(), fry() and camera() now require the 'design' matrix to be set. Previously the design matrix defaulted to a single intercept column. o Two changes to barcodeplot(): new argument 'alpha' to set semitransparency of positional bars in some circumstances; the default value for 'quantiles' is increased slightly. o kegga() has several new arguments and now supports any species supported by KEGG. kegga() can now accept annotation as a data.frame, meaning that it can work from user-supplied annotation. o New functions getGeneKEGGLinks() and getKEGGPathwayNames() to get pathway annotation from the rest.kegg website. o topKEGG() now breaks tied p-values by number of genes in pathway and by name of pathway. o goana() now supports species="Pt" (chimpanzee). o plotRLDF() now produces more complete output and the output is more completely documented. It can now be used as a complete LDF analysis for classification based on training data. The argument 'main' has been removed as an explicit argument because it and other plotting arguments are better passed using the ... facility. New arguments 'ndim' and 'plot' have been added. The first allows all possible discriminant functions to be completed even if only two are plotted. The second allows dicriminant functions to be computed without a plot. plotRLDF() also now uses squeezeVar() to estimate how by how much the within-group covariance matrix is moderated. It has new arguments 'trend' and 'robust' as options to the empirical Bayes estimation step. It also has new argument 'var.prior' to allow the prior variances to be optionally supplied. Argument 'df.prior' can now be a vector of genewise values. o New argument 'save.plot' for voom(). o diffSplice() now returns genewise residual variances. o removeExt() has new 'sep' argument. o Slightly improved algorithm for producing the df2.shrunk values returned by fitFDistRobustly(). fitFDistRobustly() now returns tail.p.value, prob.outlier and df2.outlier as well as previously returned values. The minimum df.prior value returned by eBayes(fit, robust=TRUE) may be slightly smaller than previously. o tmixture.vector() now handles unequal df in a better way. This will be seen in slightly changed B-statistics from topTable when the residual or prior df are not all equal. o If a targets data.frame is provided to read.maimages() through the 'files' argument, then read.maimages() now overwrites its rownames on output to agree with the column names of the RGList object. o More explicit handling of namespaces. Functions needed from the grDevices, graphics, stats and utils packages are now imported explicitly into the NAMESPACE, avoiding any possible masking by other packages. goana(), alias2Symbol(), alias2SymbolTable() now load the name spaces of GO.db the relevant organism package org.XX.eg.db instead of loading the packages. This keeps the user search path cleaner. o Various minor bug fixes and improvements to error messages. Changes in version 3.26.0 o New functions kegga() and topKEGG() to conduct KEGG pathway over-representation analyses. kegga() is an S3 generic function with a method for MArrayLM fitted model objects as well a default method. It uses the KEGGREST package to access up-to-date KEGG pathways using an internet connection. o goana() with trend now chooses the span of the tricube smoother based on the number of differentially expressed genes. Larger spans give smoother trends and are used for smaller numbers of genes. Arguments 'species' and 'plot' are no longer part of goana.MArrayLM() and are instead passed if present in a call to goana.default(). o topGO() has a new argument 'truncate.term' to limit number of characters appearing in a column of the output data.frame. o Improved power for the romer() function. The empirical Bayes shrinkage of the residuals, removed on 4 April, has been re-instated in a corrected form. It can optionally be turned off using the 'shrink.resid' argument. o camera() has a new argument inter.gene.cor. This allows a preset inter gene correlation to be set for each test test, resulting in potentially less conservative tests. camera() no longer gives NA results when a gene has a residual variance exactly zero. o mroast() now sorts significant gene sets by proportion of genes changing as well as by p-value and set size. When p-values are equal, sets with a higher proportion of changing genes will now rank higher in ordered results. The contrast argument of roast() can now optionally be a character string giving the name of a column of the design matrix. o fry() now produces "mixed" (non-directional) p-values and FDRs as well as directional p-values and FDRs. Bug fix for fry() when the gene set has a small number of genes. o camera(), fry(), mroast() and romer() now give more informative error messages when the index list is empty. o Speedup for ids2indices(). o tricubeMovingAverage() has new argument 'power' and old argument 'full.length' is removed. tricubeMovingAverage() now checks whether span is correctly specified. It now gives identical results for any span>=1 or any span<=0. o lmFit() and friends now always produce an output component 'rank' that returns the column rank of the design matrix. Previously it depended on the options used whether this component was given. o lmFit() now assumes that if the 'object' of expression values is a simple vector, that it represents a single gene rather than a single sample. o lmFit() now always treats infinite expression values as NA. In the past, this was done somewhat inconsistently. o Checking for NA coefficients in lmFit() is now done more efficiently, meaning that lmFit() is now faster on large datasets. o More informative error message from lmscFit() when correlation is NA. o getEAWP() now works on all eSet objects, not just ExpressionSet, provided there is an data element called "exprs". This allows all the linear modelling functions to handle general eSet objects. o plotMD() can now accept arguments 'array' or 'coef' as appropriate as synonyms for 'column'. This may help users transitioning from plotMA ot plotMD. o Arguments hi.pch, hi.col and hi.cex of plotWithHighlights() renamed to hl.pch, hl.col and hl.cex. ('hl' is short for 'highlight'.) o New 'tolerance' argument for read.idat() to allow manifest and idat to differ by a certain number of probes o The 'genes' argument of controlStatus() now accepts EListRaw or Elist objects. o All data classes (EListRaw, EList, RGList, MAList, MArrayLM) now require two arguments for subsetting. This restores the behavior in limma version 3.19.10 and earlier. In limma 3.19.11 through 3.25.13, subsetting by one argument y[i] was equivalent to y[i,]. This will now give an error message. o The dimnames<- method for EListRaw objects now sets rownames for the background matrix Eb as well as for the foreground matrix E. o avearrays() now gives a more informative error message if input argument 'x' is not a matrix. o auROC() now allows for tied stat values. o Bug fix to write.fit() when method="global" and fit contains more than one column. Changes in version 3.24.0 o Limma review article (Ritchie et al, Nucleic Acids Research, 2015) is now published, and becomes the primary citation for limma. References in User's Guide, help files and CITATION have been updated to refer to this article. o New function plotMD() for mean-difference plots. This will take over the functionality of the plotMA() function. o mdplot() has new arguments 'columns' and 'main'. o Arguments 'pch', 'cex' and 'col' of plotWithHighlights() renamed to 'hi.pch', 'hi.cex' and 'hi.col'. Improved help page for plotWithHighlights(). o plot() methods removed for EList, MAList and MArrayLM objects. Use plotMD() instead. o New function fry() provides a fast version of mroast() when there is little heteroscedasticity between genes. o Minor change to mroast() output so that FDR values are never smaller than p-values, even when mid-p-values are used. o camera(), roast(), mroast() and romer() now give a warning if any arguments found in ... are not used. o romer() is now an S3 generic function. Speed improvements for romer() when the "mean" or "floormean" set statistics are used. The empirical Bayes shrinkage of the residuals, introduced 5 October 2009, has been removed - this means that romer() results will revert to being somewhat more conservative. o topGO() can now sort gene ontology results by several p-value columns at once, using the minimum of the columns. The default is now to sort results using all the p-value columns found in the results instead of just by the first column. o goana() has new arguments 'plot' and 'covariate'. The plot argument allows users to view any estimated trend in the genewise significance values. The help files goana.Rd and goana.MArrayLM.Rd are consolidated into one file. o plotDensities() has a new argument 'legend', allowing user to reposition the legend. o voomWithQualityWeights() has a new argument 'col', allowing the barplot of sample weights to be colour-coded. o Improvements and bug fixes to plotMDS(). plotMDS now stores 'axislabel', depending on the method used to compute distances, and uses this to make more informative axis labels. plotMDS now traps problems when the distances are all zero or if one or more of the dimensions are degenerate. Also, a bug when gene.selection="common" and top=nrow(x) has been fixed. o The arguments for predFCm() have been renamed. Old argument 'VarRel' replaced by logical argument 'var.indep.of.fc'. Old argument 'prop' replaced by 'all.de'. New argument 'prop.true.null.method' passes through method to propTrueNull(). o vennDiagram() now fills circles with specified colors. o plotMA() now has a method for EListRaw objects. o voomWithQualityWeights() now stores the sample weights in the output EList object. The values stored in $weights are unchanged, they are still the combined observation and sample weights. o nec() and neqc() now remove the background Eb values if they exist in the EListRaw object. o Acknowledgements edited in User's Guide. o fix URLs for a number of references in help pages. o Improvements to lowess C code. o When loading suggested packages within function calls, usages of require() have been converted to requireNamespace() wherever possible. Functions from suggested packages are called using `::'. o topTable() now gives an information error message if eBayes() or treat() have not already been run on the fitted model object. o genas() now gives an error message when applied to fitted model for which the coefficient correlations are gene-specific. o Fix a bug in the MAList method for plotDensities that was introduced with the use of NextMethod() in version 3.21.16. Changes in version 3.22.0 o New functions goana() and topGO() provide gene ontology analyses of differentially genes from a linear model fit. The tests include the ability to adjust for gene length or abundance biases in differential expression detection, similar to the goseq package. o Improvements to diffSplice. diffSplice() now calculates Simes adjusted p-values for gene level inferences, in addition to the exon level t-tests and gene level F-tests. topSplice() now has three ranking methods ("simes", "F" or "t"), with "simes" now becoming the default. diffSplice() also has a new argument 'robust' giving access to robust empirical Bayes variance moderation. o New function plotExons() to plot log-fold-changes by exon for a given gene. o New function voomWithQualityWeights() allows users to estimate sample quality weights or allow for heteroscedasticity between treatment groups when doing an RNA-seq analysis. o Improvement to arrayQualightyWeights(). It now has a new argument 'var.design' which allows users to model variability by treatment group or other covariates. o Improved plotting for voomaByGroup(). o barcodeplot() can now plot different weights for different genes in the set. o Improvements to roast() and mroast(). The directional (up and down) tests done by roast() now use both the original rotations and their opposite signs, effectively doubling the number of effective rotations for no additional computational cost. The two-sided tests are now done explicitly by rotation instead of doubling the smallest one-sided p-value. The two-sided p-value is now called "UpOrDown" in the roast() output. Both functions now use a fast approximation to convert t-statistics into z-scores, making the functions much faster when the number of rotations or the number of genes is large. The contrast argument can now optionally be a character string giving a column name of the design matrix. o zscoreT() can optionally use a fast approximation instead of the slower exact calculation. o symbols2indices() renamed to ids2indices(). o Improvements to removeBatchEffect(). It can now take into account weights and other arguments that will affect the linear model fit. It can now accept any arguments that would be acceptable for lmFit(). The behavior of removeBatchEffect() with design supplied has also changed so that it is now consistent with that of lmFit() when modelling batches as additive effects. Previously batch adjustments were made only within the treatment levels defined by the design matrix. o New function plotWithHighlights(), which is now used as the low-level function for plotMA() and plot() methods for limma data objects. o The definition of the M and A axes for an MA-plot of single channel data is changed slightly. Previously the A-axis was the average of all arrays in the dataset - this has been definition since MA-plots were introduced for single channel data in April 2003. Now an artificial array is formed by averaging all arrays other than the one to be plotted. Then a mean-difference plot is formed from the specified array and the artificial array. This change ensures the specified and artificial arrays are computed from independent data, and ensures the MA-plot will reduce to a correct mean-difference plot when there are just two arrays in the dataset. o plotMDS() can now optionally plot samples using symbols instead of text labels. It no longer has a 'col' argument, which instead is handled by .... o vennDiagram() now supports circles of different colors for any number of circles. Previously this was supported only up to three sets. o getEAWP() will now find a weights matrix in an ExpressionSet object if it exists. o update to helpMethods(). o Substantial updates to the two RNA-seq case studies in the User's Guide. In both cases, the short read data has been realigned and resummarized. o Improvements to many Rd files. Many keyword entries have been revised. Many usage and example lines been reformated to avoid over long lines. o biocViews keywords updated. o Subsetting columns of a MArrayLM object no longer subsets the design matrix. o Bug fix for read.maimages: default value for 'quote' was not being set correctly for source="agilent.mean" or source="agilent.median". o Bug fix to topTableF() and topTable(). The ordering of Amean values was sometimes incorrect when sorting by F-statistic and a lfc or p.value filter had been set. o Bug fix to read.ilmn() when sep=",". Changes in version 3.20.0 o New functions diffSplice(), topSplice() and plotSplice() provide functionality to analyse differential splicing using exon-level expression data from either microarrays or RNA-seq. o New Pasilla case study added to User's Guide, demonstrating differential splicing analysis of RNA-seq data. o new function weightedLowess() which fits a lowess curve with prior weights. Unlike previous implementations of lowess or loess, the weights are used in calculating which neighbouring points to include in each local regression as well as in the local regression itself. o weightedLoess() now becomes the default method used by loessFit() to fit the loess curve when there are weights. The previous locfit and loess() methods are offered as options. o linear model fit functions lm.series(), mrlm.series() and gls.series() no longer drop the dimensions of the components of the fitted object when there is just coefficient or just one gene. Previously this was done inconsistently in some cases but not others. Now the matrix components always keep dimensions. o The functions lmFit(), eBayes() and tmixture.vector() now work even when there is just one gene (one row of data). o New function subsetListOfArrays(), which is used to simplify the subsetting code for RGList, MAList, EList, EListRaw and MArrayLM objects. o new function tricubeMovingAverage() for smoothing a time series. o barcodeplot() has a new option to add enrichment worms to the plot, making use of tricubeMovingAverage(). o New plot() methods for RGList, MAList, EList and MArrayLM class objects. In each case, this produces a similar result to plotMA(). When using plot() or plotMA() on an MArrayLM object, the column is now specified by the 'coef' argument instead of by 'array'. o plotMA3by2() now works on single channel data objects as well as on MAList objects. o New function read.idat() to read files from Illumina expression beadarrays in IDAT format. o The ctrlpath argument of read.ilmn() now defaults to the same as path for regular probes. This means that only one path setting is required if the regular and control probe profiles are in the same directory. o read.ilmn() now sets the same probe IDs as rownames for both the expression matrix E and the annotation data.frame genes, providing that the probe IDs are unique. o beadCountWeights() can now work with either probe-wise standard errors or probe-wise standard deviations. o treat() has new arguments robust and winsor.tail.p which are passed through to robust empirical Bayes estimation. o topTreat() now includes ... argument which is passed to topTable(). o topTable() with confint=TRUE now produces confidence intervals based on the t-distribution instead of on the normal distribution. It also now accepts a numeric value for the confint argument to specify a confidence level other the default of 0.95. o topTable() will now work on an MArrayLM fit object that is missing the lods component, for example as produced by treat(). o roast() and mroast() now permit array weights and observation weights to both be specified. o camera(), roast() and mroast() now use getEAWP() to interpret the data object. This means that they now work on any class of data object that lmFit() will. o romer() now uses propTrueNull(method="lfdr") instead of convest(). This makes it substantially faster when the number of genes is large. o genas() now uses fit$df.total from the MArrayLM object. This prevents df.total from exceeding the total pooled residual df for the dataset. The genas() results will change slightly for datasets for which df.prior was very lage. o plotDensities() is now an S3 generic function with methods for RGList, MAList, EListRaw and EList objects. o plotFB is now an S3 generic function with methods for RGList and EList data objects. o New topic help pages 10GeneSetTests.Rd and 11RNAseq.Rd. The page 10Other.Rd is deleted. All topic help pages are now listed under 'See also' in the package introduction page accessible by ?limma. o avereps() was never intended to be applied to RGList or EListRaw objects. It now gives an error when applied to these objects instead of returning a matrix of questionable value. o Bug fix: fitFDistRobustly() was failing when there were missing values or zero df values and covariate was NULL. o Bug fix: vennDiagram() wasn't passing extra arguments (...) to plot() when the number of sets was greater than 3. o Bug fix to topTreat(). Rownames were incorrectly ordered when p<1. o bug fix to genas(), which was not handling vector df.prior correctly when the fit object was generated using robust=TRUE. o bug fix to squeezeVar(). Previously there was an error when robust=TRUE and trend=FALSE and some of the estimated df.prior were infinite. o bug fix to topTable() and topTableF() when sorting by F-statistic combined with p-value or lfc cutoffs. Changes in version 3.18.0 o new function beadCountWeights() to estimate quantitative weights from the bead counts for each probe for Illumina BeadArrays. o New function contrastAsCoef(), which reforms a design matrix so that one or more specified contrasts become coefficients. This function is called by roast(). o plotMA() is now an S3 generic function. o The legend argument to plotMA() can now take a character value giving the position to place the legend. o toptable(), topTable() and topTreat() now preserve the rownames of the fit object, unless the fit object has duplicated names, in which case the rownames are copied to the ID column. Empty rownames are replaced with 1:nrow(fit). o read.ilmn() no longer adds the probe IDs to the gene annotation data.frame, leaving them instead as rownames of the expression matrix. It longer creates a targets file since the sample names are already preserved as column names of the expression matrix. o loessFit() now uses the locfit.raw in the locfit package when weights are provided instead of loess in the stats package. The function now runs very efficiently even on very long data vectors. The output results will change slightly when weights are provided. o voom() now outputs lib.size as a column of targets instead of as a separate component. o cbind for EList and EListRaw objects now recognizes a design matrix if it is present. o plotMDS() now checks explicitly that there are at least 3 samples to plot. o normexp.fit.detection.p() now tolerates some non-monotonicity in the detection p-pvalues as a function of expression. o fitFDistRobustly() now uses a smoother for the smallest df.prior values. This may result in smaller tail values than before when a group of input x values appear to be outliers but the largest value is not individually a stand-out value. o New merge methods for EList and EListRaw objects. o topTable() and treat() now give more informative error messages when the argument fit is not a valid MArrayLM object. o roast() now calls mroast() if the index vector is a list. Bug fix to mroast(), which had been ignoring the weights. o Updates to genas() function. argument chooseMethod renamed to subset and option "n" renamed to "all". Function now returns NA results and a message when no genes satisfy the criterion for inclusion in the analysis. Some editing of help page and streamlining of code. o Roles of contributors now specified in author field of DESCRIPTION file using standard codes. o Additions and updates to references in the help pages. Removed defunct Berkeley Press links to published Smyth (2004) article in several Rd files. Replacing with link to Preprint. Added link to Phipson (2013) thesis in two Rd files. Add Majewski et al reference to genas.Rd. Add Phipson et al and Sartor et al references to squeezeVar.Rd. Add Phipson et al reference to eBayes.Rd. Update lmscFit and voom references. o Update mammmary stem cell case study in User's Guide. As well as reflecting changes to read.ilmn() and topTable(), this now demonstrates how to find signature genes for particular cell type. o documentation about rownames and column names and the use of rownames(fit) and colnames(fit) added to lmFit.Rd. o improvements to help pages for data classes. o Edits to normalizeBetweenArrays help page (i) to further clarify which normalization methods are available for single-channel data and which are available for two-color data and (ii) to give a cross citation to the neqc() function for Illumina BeadChips. o Edits to voomaByGroup help page. o duplicateCorrelation() now uses the weights matrix when block is set. Previously the weights were ignored when block was used. o Bug fix to subsetting for MArrayLM objects: the df.total component was not being subsetted. o bug fix to eBayes(robust=TRUE) when some of the df.prior values are infinite. o Bug fix to ebayes(), which was not passing the 'robust' argument correctly on to squeezeVar(). o Bug fix to fitFDistRobustly(), which affected the estimated scale when df2 is estimated to be Inf. Changes in version 3.16.0 o New section in User's Guide on time course experiments with many time points. The RNA-seq case study in User's Guide has also been revised. o Improvements to various help pages including read.maimages.Rd, squeezeVar.Rd, fitFDist.Rd, trigammaInverse.Rd, normalizeRobustSpline.Rd, genas.Rd and roast.Rd. Previously the meaning of source="agilent" was mis-stated in read.maimages.Rd. o New robust method for estimating the empirical Bayes prior, called by specifying robust=TRUE in the call to eBayes(). When this is TRUE the output df.prior is now a vector instead of a scalar. o New function fitFDistRobustly() estimates the parameters of a scaled F-distribution robustly using Winsorized values. Outlier observations receive smaller values for df.prior than non-outliers. This permits robust methods for squeezeVar(), ebayes() and eBayes(), all of which now have a new argument wins.tail.p to specify the tail proportions for Winsorizing. o fitFDist() now permits infinite values for the covariate. It also gracefully handles cases where the covariate takes only a small number of distinct values. Similarly for eBayes() and squeezeVar() that call fitFDist(). o All the functions that perform gene set tests have been revised to make the input and output formats more consistent. roast(), mroast() and camera() are now S3 generic functions, with methods for EList and MAList objects. The order of arguments has been changed for roast(), mroast() and camera() so that the first argument is now y. All functions that perform gene sets now use the argument 'index' to specify which genes are included in the test set. Previously this argument was called 'iset' for roast() and romer() and 'indices' for camera(). camera() and mroast() now produce a data.frames. Instead of separate up and down p-value columns, there is now a two-sided p-value and a column indicating direction of change. There are new columns giving FDR values and the number of genes in each set. There is a new argument 'sort' to indicate whether output results should be sorted by p-value. mroast() has a new argument 'weights' for observational weights, to bring it into line with roast(), o vennDiagram() can now plot up to five sets (previously limited to three). o genas() now optionally draws a plot in which ellipses are used to represent the technical and biological components of correlation. It also now has the ability to automatically select which probes are used for the correlation analysis, and a new argument controls the method used for this selection. o New options for the method argument of propTrueNull(). o New functions vooma() and voomaByGroup() for computing precision weights based on a mean-variance trend. vooma() is similar to voom() but for microarray data instead of RNA-Seq. voomaByGroup() allows different groups to have systematically different variances. o New function predFCm() to compute predictive (shrunk) log fold changes. o New function fitGammaIntercept() for estimating the intercept of a gamma glm with an offset. Used by genas(). o New function zscoreHyper() for computing z-score equivalents of deviates from a hypergeometric distribution. o New function qqf() for qq-plots relative to an F-distribution. o normalizeWithinArrays() with method="robustspline" now longer requires the layout argument to be set. The layout argument for normalizeRobustSpline() now defaults to a single print-tip group. o fitFDist() now coerces degrees of freedom df1 below 1e-15 to zero. o Due to changes in R, loessFit() no longer makes direct calls to foreign language code in the stats package, and instead calls R functions. Unfortunately, this makes loessFit() about 25-30% slower than previously when weights are used. o Bug fix to read.maimages(), which was not accepting source="agilent.mean". o Bug fix for contrasts.fit() when the covariance matrix of the coefficients (cov.coefficients) is not found in the fitted model object. This situation doesn't arise using any of the standard limma analysis pipelines. o Bug fix to lmscFit() when the residual df = 1. o Bug fix to readTargets() to avoid warning message when targets$Label is used to set row names but targets$Label contains duplicated entries. Changes in version 3.14.0 o limma license upgraded to GPL (>=2) instead of LGPL to match R itself. o Many updates to the User's Guide. Sections have been added on reading single channel Agilent and Illumina data. The chapter on experimental designs has been split into three chapters on single-channel, common reference and two-color designs respectively. The material on the fixed effect approach to technical replication has been deleted. There are new sections on nested interactions for factorial designs and on multi-level designs. o The links to the Apoa1, Weaver and Bob1 datasets in the User's Guide have been updated to help users download the data themselves if they wish to repeat the case study analyses. o The help page for camera() now cites published paper Wu and Smyth (NAR, 2012). In view of the results of this paper, the claim is no longer made on help page for geneSetTest() that genes might be treated as independent when the experimental units are genetically identical mice. o Minor edits to CITATION file. o New function propTrueNull() for fast estimation of the proportion of true null hypotheses from a vector of p-values. o New function zscore() to compute z-score equivalents for deviates from any continuous distribution. Includes the functionality of the older functions zscoreGamma() and zscoreT() as special cases. o roast() now accepts observation level weights, through a new argument 'weights'. o loessFit() now applies minimum and maximum bounds by default to avoid zero or infinite weights. Equal weights are now treated as if the weights were NULL, even all zero weights, so that the lowess code is called instead of the loess code. o When there are no weights, loessFit() now extracts residuals directly from the C code output instead of computing in R. o fitFDist() now permits missing values for x or zero values for df1 even when there is a covariate. This means that squeezeVar() and eBayes() now work with trends even when not all the data values are informative. o New argument 'file' for convest(), implementing edits contributed by Marcus Davy. Arguments doplot and dereport renamed to 'plot' and 'report'. o Two improvements for plotMDS(). It now coerces labels to be character, and now makes extra room on the plot when the text labels are wide. o plotMDS() no longer gives an error when the requested number of top genes is greater than the total number of rows of data. o Code speed-up for alias2SymbolTable() o any(duplicated()) replaced by anyDuplicated() in several functions. o Fix to voom() so that it computes weights correctly even when the design matrix is not of full rank. o Bug fix for roast() when the fitted model has only one coefficient. Changes in version 3.12.0 o read.maimages() with source="agilent" now reads median foreground estimates instead of mean foreground. New option source= "agilent.mean" preserves earlier meaning of source="agilent". o Agilent single-channel case study added to User's Guide. o removeBatchEffect() now corrects for continuous covariates as well as qualitative factors. o new function camera() performs competitive gene set tests while adjusting for inter-gene correlation. o new function interGeneCorrelation() estimates the average intergene correlation for a set of genes. o columns in output from roast() have been re-ordered. o arguments 'selected' and 'selected2' renamed to 'index' and 'index2' in functions barcodeplot(), geneSetTest() and wilcoxGST(). o default labels for barcodeplot() are now somewhat more explicit. o new function rankSumTestWithCorrelation extends the Wilcoxon-Mann-Whitney test to allow for correlation between cases in one of the groups. geneSetTest() now calls this function instead of wilcox.test, with a consequence improvement in speed. o The lfc (log-fold-change) cutoff argument of topTable() is now applied to the minimum absolute logFC when ranking by F-statistic. Previously lfc was only used when ranking by t-statistic. o new methods "fast" and "affy" for normalizeCyclicLoess(), with "fast" becoming the default method. New argument 'cyclic.method' for normalizeBetweenArrays() gives access to the different cyclic loess methods. o There were problems with using the argument gene.weights in mroast(). This argument is now permitted to be of the same length as the number of probes in the data set. It is then automatically subsetted for each gene set. o mroast() now uses mid-p-values by default when adjusting for multiple testing. o neqc(), nec() and normexp.fit.control() now give user-friendly error messages when no negative control probes or no regular probes are found. Changes in version 3.10.0 o New function voom() allows RNA-Seq experiments to be analysed using the standard limma pipeline. An RNA-Seq case study is added to User's Guide. o treat(), roast() and mroast() can now estimate and work with a trend on the prior variance, bringing them into line with eBayes(). o barcodeplot() and barcodeplot2() merged into one function. o removeBatchEffect() can now correct for two batch factors. o plotMDS is now an S3 generic function. This allows MDS plots to be redrawn with new labels without needing to repeat the distance or scaling calculations. New S4 class "MDS" to hold the multidimensional scaling information output from plotMDS. o getEAWP() now gets probe annotation from the expression rownames of an EList object, if no other probe annotation is available. o topRomer() now ranks gene sets by secondary columns as well the primary criterion specified, to give a more meaningful ranking when the p-values are tied. o wilcoxGST() now accepts signed or unsigned test statistics. Change to p-value calculation in geneSetTest() when rank.only=FALSE to avoid zero p-values and follow recommendation of Phipson and Smyth (SAGMB, 2010). o plotMA() now recognizes ElistRaw and EList objects appropriately. o Default span for normalizeCyclicLoess increased from 0.4 to 0.7. Speed improved when weights=NULL. o Weaver case study (Section 11.5) in User's Guide is updated and rewritten. Data classes ElistRaw and Elist now described in the quick start section of the User's Guide. Other minor updates to User's Guide. o Bug fix for normalizeBetweenArrays() when object is an EListRaw and method="cyclicloess". Previously this function was applying cyclicloess to the raw intensities, then logging. Now it logs first, then applies cyclicloess. o Bug fix to avereps() for EList objects x when x$other is not empty.