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.