CHANGES IN VERSION 1.38.1 ------------------------- o Removing genefilter as dependency, switching to matrixStats. This should resolve gfortran. CHANGES IN VERSION 1.31.16 -------------------------- o Turning off outlier replacement with glmGamPoi fitting. CHANGES IN VERSION 1.31.15 -------------------------- o Added 'saveCols' in results() and lfcShrink() to pass metadata columns to output. CHANGES IN VERSION 1.31.13 -------------------------- o Allow additional arguments to be passed to data-accessing functions in integrateWithSingleCell(). CHANGES IN VERSION 1.31.2 ------------------------- o Fixed interface with glmGamPoi so that normalizationFactors can be used. Thanks to Michael Schubert for spotting this and to Constantin Ahlmann-Eltze for pointing out the fix. CHANGES IN VERSION 1.30.0 ------------------------- o Major overhaul of dispersion estimation and GLM estimation functions from Constantin Ahlmann-Eltze, which will allow use of the glmGamPoi package from within DESeq2, in particular relevant for single-cell datasets. DESeq() can be directed to use glmGamPoi for dispersion and GLM fitting by specifying fitType="glmGamPoi". The glmGamPoi estimation is much faster than original DESeq2 estimation for single-cell datasets, e.g. for 30,000 cells, calling glmGamPoi was 13x faster than original DESeq2. In addition, the dispersion estimation is more accurate for genes with many small counts, as found in single-cell datasets. See glmGamPoi manuscript for details on methods, doi: 10.1101/2020.08.13.249623. o Added integrateWithSingleCell(), written by Kwame Forbes, which directs user to a menu of single-cell datasets available on Bioconductor and downloads/loads the one chosen by the user for further analysis visualization. (Interactive only) CHANGES IN VERSION 1.28.0 ------------------------- o For lfcShrink(), changed order of `type` options: "normal" will no longer be first, as it under-performed "apeglm" and "ashr" in Zhu et al (2018). The new order is type=c("apeglm", "ashr", "normal"). o Added arguments to estimateDispersions: useCR (logical), and weightThreshold (numeric, default of 1e-2). The first argument optionally avoid the calculation of Cox-Reid adjustment term. The second argument sets the threshold for subsetting the design matrix and GLM weights when calculating the adjustment term. In addition, baseMean uses weights when calculating the mean of normalized counts, if weights matrix is provided. CHANGES IN VERSION 1.27.12 -------------------------- o For lfcShrink(), changed order of `type` options: "normal" will no longer be first, as it under-performed "apeglm" and "ashr" in Zhu et al (2018). The new order is type=c("apeglm", "ashr", "normal"). CHANGES IN VERSION 1.27.9 ------------------------- o Added arguments to estimateDispersions: useCR (logical), and weightThreshold (numeric, default of 1e-2). The first argument optionally avoid the calculation of Cox-Reid adjustment term. The second argument sets the threshold for subsetting the design matrix and GLM weights when calculating the adjustment term. In addition, baseMean uses weights when calculating the mean of normalized counts, if weights matrix is provided. CHANGES IN VERSION 1.26.0 -------------------------- o Incorporation of fast code from Constantin Ahlmann-Eltze which speeds up DESeq2 for large sample sizes (n > 100) by at least an order of magnitude. In fact the speed is now linear with number of samples whereas previously DESeq2 would scale quadratically. The critical merge commits were: c96c1c0ad43280c82403d3e6bc3501332a62e7b8 (2019-07-16) 0a47a0c750aa5c31df759a171c737d6ed782d6c2 (2019-07-30) o Fixed a bug where rbind() in parallel=TRUE would proliferate metadata items. o Updated vignette to discuss tximeta (workflow also updated to show use of tximeta instead of read counting). CHANGES IN VERSION 1.22.0 ------------------------- o No replicate designs no longer supported (previous version began deprecation with a warning). o unmix() now optionally will return the correlation (in the variance stabilized space) of the fitted data to the original data, and the matrix of fitted data (format="list"). Argument 'loss' was changed to 'power'. Will give warning if the columns of 'pure' have high correlation (in the variance stabilized space). CHANGES IN VERSION 1.21.21 -------------------------- o Improved code for 'linearModelMu' (an internal fitting function used in dispersion estimation for some models) contributed by Wolfgang Huber speeds up an internal step by 2 orders of magnitude. CHANGES IN VERSION 1.21.15 -------------------------- o Rows of the weights matrix which would produce a degenerate design matrix, instead of giving an error, will produce a warning, and these rows will be treated as if they contained all zeros (mcols(dds)$allZero and mcols(dds)$weightsFail will be set to TRUE). CHANGES IN VERSION 1.21.14 -------------------------- o The nbinom{WaldTest,LRT} functions will not stop if the design produces a model matrix that is not full rank and betaPrior=FALSE (default). This was assumed by the DESeq2 code, because errors are produced at object construction and at dispersion estimation, but it was possible to call nbinomLRT() from DEXSeq after dispersion estimation and end up with a full model matrix that was not full rank. Instead testForDEU() should be called from DEXSeq. CHANGES IN VERSION 1.21.13 -------------------------- o Adding back a feature from version 1.15, where contrasts of two groups where both had all zero counts would have the LFC zero-ed out, rather than output a small but non-zero value. It's preferable for the Wald test that the LFC be set to zero for such contrasts. CHANGES IN VERSION 1.21.9 ------------------------- o DESeq() now only says one time 'using supplied model matrix', previously this was repeated three times from sub-functions. Sub-functions therefore no longer print this message. o Fixed bug when lfcShrink run directly after LRT with supplied model matrices. o Added heuristic to prevent Cook's outlier based filtering when the max Cook's sample has lower counts than 3 other samples. Restricted to two group comparison datasets. CHANGES IN VERSION 1.20.0 -------------------------- o Added 'lfcThreshold' argument to lfcShrink() for use with type="normal" and type="apeglm". For the latter, lfcShrink() will compute FSOS s-values, for bounding when the LFC will be "false sign or small", where small is defined by lfcThreshold. o Switching to a ~10x faster apeglm implementation for use in the lfcShrink() function. o Beginning the deprecation of exploratory analysis of designs without replicates. Analysis of designs without replicates will be removed in the Oct 2018 release: DESeq2 v1.22.0, after which DESeq2 will give an error. o Elevate 'minmu' to DESeq() as this proves useful for single cell applications and certain zero-inflated data. o Elevate 'useT' to DESeq(), which will use (n - p) for the degrees of freedom of the t distribution, and if weights are provided, it will use the sum of weights as 'n'. CHANGES IN VERSION 1.18.0 ------------------------- o lfcShrink() offers alternative estimators type="apeglm" and type="ashr", making use of shrinkage estimators in the 'apeglm' and 'ashr' packages, respectively. See ?lfcShrink for more details and appropriate references. The integration of these alternative shrinkage estimators is still in development. Additionally, the DESeqResults object gains priorInfo(res), which passes along details of the fitted prior on LFC. o Factor levels using characters other than letters, numbers, '_' and '.' will print a message (not a warning or error) that it is recommended to restrict to these "safe characters". This follows a suggestion from the Bioconductor support site to avoid user errors. CHANGES IN VERSION 1.16.0 ------------------------- o DESeq() and nbinomWaldTest() the default setting will be betaPrior=FALSE, and the recommended pipeline will be to use lfcShrink() for producing shrunken LFC. o Added a new function unmix(), for unmixing samples according to linear combination of pure components, e.g. "tissue deconvolution". o Added a new size factor estimator, "poscounts", which evolved out of use cases in Paul McMurdie's phyloseq package. o Ability to specify observation-specific weights, using assays(dds)[["weights"]]. These weights are picked up by dispersion and NB GLM fitting functions. CHANGES IN VERSION 1.15.40 -------------------------- o Adding a new function unmix(), for unmixing samples according to pure components, e.g. "tissue deconvolution". The pure components are added on the gene expression scale (either normalized counts or TPMs), and the loss is calculated in a variance stabilized space. CHANGES IN VERSION 1.15.39 -------------------------- o Added a new size factor estimator, "poscounts", which evolved out of use cases in Paul McMurdie's phyloseq package. CHANGES IN VERSION 1.15.36 -------------------------- o Ability to specify observation-specific weights, using assays(dds)[["weights"]]. These weights are picked up by dispersion and NB GLM fitting functions. CHANGES IN VERSION 1.15.28 -------------------------- o Remove some code that would "zero out" LFCs when both groups involved in a contrast had zero counts. This lead to inconsistency when similarly contrasts were performed by refactoring. CHANGES IN VERSION 1.15.12 -------------------------- o DESeq() and nbinomWaldTest() the default setting will be betaPrior=FALSE, and the recommended pipeline will be to use lfcShrink() for producing shrunken log2 fold changes for visualization and ranking. Explanation for the change is presented in the vignette section: "Methods changes since the 2014 DESeq2 paper" CHANGES IN VERSION 1.15.9 ------------------------- o Adding prototype function lfcShrink(). o Vignette conversion to Rmarkdown / HTML. CHANGES IN VERSION 1.15.3 ------------------------- o Removing betaPrior option for nbinomLRT, in an effort to clean up and reduce old un-used functionality. CHANGES IN VERSION 1.13.8 ------------------------- o Use a linear model to estimate the expected counts for dispersion estimation in estDispGeneEst() if the number of groups in the model matrix is equal to the number of columns of the model matrix. Should provide a speed-up for dispersion estimation for model matrices with many samples. CHANGES IN VERSION 1.13.3 ------------------------- o Fixed bug: fpm() and fpkm() for tximport. o Fixed bug: normalization factors and VST. o Added an error if tximport lengths have 0. o Added an error if user matrices are not full rank. o More helpful error for constant factor in design. CHANGES IN VERSION 1.12.0 ------------------------- o Added DESeqDataSetFromTximport() to import counts using tximport. o Added vst() a fast wrapper for the VST. o Added support for IHW p-value adjustment. CHANGES IN VERSION 1.11.42 -------------------------- o Update summary() to be IHW-results-aware. o Small change to fitted mu values to improve fit stability when counts are very low. Inference for high count genes is not affected. o Galaxy script inst/script/deseq2.R moves to Galaxy repo. CHANGES IN VERSION 1.11.33 -------------------------- o Changed 'filterFun' API to accommodate IHW: independent hypothesis weighting in results(), see vignette for example code. Thanks to Nikolaos Ignatiadis, maintainer of IHW package. CHANGES IN VERSION 1.11.18 -------------------------- o Added a function vst(), which is a fast wrapper for varianceStabilizingTransformation(). The speed-up is accomplished by subsetting to a smaller number of genes for the estimation of the dispersion trend. CHANGES IN VERSION 1.11.5 ------------------------- o Adding in functionality to import estimated counts and average transcript length offsets from tximport, using DESeqDataSetFromTximport(). CHANGES IN VERSION 1.10.0 ------------------------- o Added MLE argument to plotMA(). o Added normTransform() for simple log2(K/s + 1) transformation, where K is a count and s is a size factor. o When the design contains an interaction, DESeq() will use betaPrior=FALSE. This makes coefficients easier to interpret. o Independent filtering will be less greedy, using as a threshold the lowest quantile of the filter such that the number of rejections is within 1 SD from the maximum. See ?results. o summary() and plotMA() will use 'alpha' from results(). CHANGES IN VERSION 1.9.42 ------------------------- o New function 'normTranform', for making DESeqTransform objects from normalized counts plus a pseudocount (default 1) then applying a transformation (default log2). o Added MLE argument to plotMA(), if results() was run with addMLE=TRUE, this allows for comparison of shrunken and unshrunken estimates of fold change. o summary() and plotMA() use the 'alpha' which was specified in results() rather than defaulting to 0.1. o Removed rlog's fast option, and instead recommending VST for very large matrices of counts (100s of samples). CHANGES IN VERSION 1.9.17 ------------------------- o Independent filtering: results() no longer uses the maximum of the number of rejections as calculated by the filter_p() function from the genefilter package. Small numbers of rejections at a high quantile of the filter threshold could result in a high filter threshold. Instead, now the results() function will use the lowest quantile of the filter for which the number of rejections is close to the peak of a lowess curve fit through the number of rejections over the filter quantiles. 'Close to' is defined as within 1 residual standard deviations. CHANGES IN VERSION 1.9.16 ------------------------- o When the design formula contains interaction terms, the DESeq() function will by default not use a beta prior (betaPrior=FALSE). The previous implementation of a log fold change prior for interaction terms returned accurate inference, but was confusing for users to interpret. New instructions on building results tables for designs with interactions will be included in the software vignette. CHANGES IN VERSION 1.8.0 ------------------------ o Added support for user-supplied model matrices to DESeq(), estimateDispersions() and nbinomWaldTest(). This helps when the model matrix needs to be edited by the user. CHANGES IN VERSION 1.7.45 ------------------------- o Added a test in rlog for sparse data, mostly zero and some very large counts, which will give a warning and suggestion for alternate transformations. o Added plotSparsity() which will help diagnose issues for using rlog: data which do not resemble negative binomial due to many genes with mostly zeros and a few very large counts. CHANGES IN VERSION 1.7.43 ------------------------- o Added 'replaced' argument to counts() and plotCounts() such that the assay in "replaceCounts" will be used if it exists. Raised a minimum dispersion value used in Cook's calculation, so that other counts in a group with an outlier won't get extreme Cook's distances themselves. CHANGES IN VERSION 1.7.32 ------------------------- o Added logic to results() which will zero out the LFC, Wald statistic and set p-value to 1, for 'contrast' argument results tables where the contrasted groups all have zero count. Non-zero LFCs were otherwise occuring due to large differences in the size factors. CHANGES IN VERSION 1.7.11 ------------------------- o Added support for user-supplied model matrices to DESeq(), estimateDispersions() and nbinomWaldTest(). CHANGES IN VERSION 1.7.9 ------------------------ o Added Genome Biology citation for the DESeq2 methods. o Introduced type="iterate" for estimateSizeFactors, an alternative estimator for the size factors, which can be used even when all genes have a sample with a count of zero. See man page for details. CHANGES IN VERSION 1.7.3 ------------------------ o Fixed two minor bugs: DESeq() with parallel=TRUE was dropping rows with all zero counts, instead of propogating NAs. nbinomLRT() with matrices provided to 'full' and 'reduced' and a design of ~ 1, the matrices were being ignored. CHANGES IN VERSION 1.6.0 ------------------------ o DESeq() and results() gets a 'parallel' argument. o results() gets an 'addMLE' argument. o results() gets a 'test' argument, for constructing Wald tests after DESeq() was run using the likelihood ratio test. o results() argument 'format' for GRanges or GRangesList results. o new plotCounts() function. o Less outlier calling from Cook's distance for analyses with many samples and many conditions. o More robust beta prior variance and log fold change shrinkage. CHANGES IN VERSION 1.5.70 ------------------------- o Added 'parallel' also for results(), which can be slow if run with 100s of samples. CHANGES IN VERSION 1.5.54 ------------------------- o Added 'parallel' argument to DESeq() which splits up the analysis over genes for those steps which are easily done in parallel, leveraging BiocParallel's bplapply. CHANGES IN VERSION 1.5.50 ------------------------- o A matrix can be provided to rlog or to the VST and will return a matrix. Also 'fitType' argument is included, in case dispersions are not estimated which is passed on to estimateDispersions. CHANGES IN VERSION 1.5.49 ------------------------- o The fast=TRUE implementation of rlog is even faster, subsetting genes along the range of base mean to estimate the dispersion trend and for fitting the optimal amount of shrinkage. CHANGES IN VERSION 1.5.40 ------------------------- o Further improved code behind the robust estimation of variance for Cook's cutoff, resulting in less outlier calls due to an individual condition with few samples and high variance. CHANGES IN VERSION 1.5.28 ------------------------- o New results() argument 'addMLE' adds the unshrunken fold changes for simple contrasts or interaction terms to the results tables. CHANGES IN VERSION 1.5.27 ------------------------- o Applied the beta prior variance calculation from v1.5.22 to the regularized logarithm. o Added MLE coefficients as MLE_condition_B_vs_A columns to mcols(dds). o Fixed the statistic which is returned when lfcThreshold is used. Previously, only the p-value and adjusted p-value was changed. o plotPCA() with argument 'returnData' will return a data.frame which can be used for custom plotting. CHANGES IN VERSION 1.5.25 ------------------------- o Improved the robust variance estimate used for calculating Cook's distances. The previous estimate could lead to outlier calls in datasets with many conditions, and when a single condition had large, highly variable counts for all its samples. CHANGES IN VERSION 1.5.22 ------------------------- o Adding an alternate method for beta prior variance calculation in nbinomWaldTest. This helps to produce more robust prior variance estimates when many genes have small counts and highly variable MLE log fold changes. CHANGES IN VERSION 1.5.15 ------------------------- o For likelihood ratio test, expanded model matrices not default. Some improvements in fit time from handling of genes with dispersions that do not converge using line search. CHANGES IN VERSION 1.5.13 ------------------------- o Adding test argument to results(), which allows users to perform a Wald test after DESeq(dds, test="LRT") / nbinomLRT has been run. CHANGES IN VERSION 1.5.11 ------------------------ o Swapping in ggplot2 for lattice for the plotPCA function. CHANGES IN VERSION 1.5.9 ------------------------ o Added a VST for fitType = mean. Allowed designs with ~ 0 and betaPrior = FALSE. Fixed some potential metadata column insertion bugs. CHANGES IN VERSION 1.5.8 ------------------------ o Suppress the glm.fit convergence warning from parametric dispersion curve fitting procedure, instead use this for the iterative convergence test. CHANGES IN VERSION 1.5.3 ------------------------ o Speeding up and reducing copying for DESeqDataSet construction. CHANGES IN VERSION 1.5.2 ------------------------ o Added 'format' argument to results, which will attach results to GRangesList or GRanges if requested (default is DataFrame). CHANGES IN VERSION 1.4.4 ------------------------ o Fixed a hang which could occur in the GLM fitting procedure. CHANGES IN VERSION 1.4.3 ------------------------ o Fixed simple bug when using normalizationFactors and running nbinomWaldTest, error was "no method for coercing this S4 class to a vector". CHANGES IN VERSION 1.4.2 ------------------------ o Fixed bugs: estimating beta prior for interaction between factor and numeric; not returning row names for counts(); construction of DESeqDataSet gives wrong error when there are empty levels: instead now drops the levels for the user. CHANGES IN VERSION 1.4.1 ------------------------ o Fixed bug where DESeqDataSetFromHTSeqCount() imported the special rows, "_ambiguous", etc. CHANGES IN VERSION 1.4.0 ------------------------ o *** USAGE NOTE *** Expanded model matrices are now used when betaPrior = TRUE (the default). Therefore, level comparison results should be extracted using the 'contrast' argument to the results() function. Expanded model matrices produce shrinkage of log fold changes that is independent of the choice of base level. Expanded model matrices are not used in the case of designs with an interaction term between factors with only 2 levels. o The order of the arguments 'name' and 'contrast' to the results() function are swapped, to indicate that 'contrast' should be used for the standard comparisons of levels against each other. Calling results() with no arguments will still produce the same comparison: the fold change of the last level of the last design variable over the first level of the last design variable. See ?results for more details. o The DESeq() function will automatically replace count outliers flagged by Cook's distance when there are 7 or more replicates. The DESeq() argument 'minReplicatesForReplace' (default 7) is used to decide which samples are eligible for automatic replacement. This default behavior helps to prevent filtering genes based on Cook's distance when there are many degrees of freedom. CHANGES IN VERSION 1.3.58 ------------------------- o Added a list() option to the 'contrast' argument of results(). See examples in ?results. CHANGES IN VERSION 1.3.24 ------------------------- o rlogTransformation() gains an argument 'fast', which switches to an approximation of the rlog transformation. Speed-up is ~ 2x. o A more robust estimator for the beta prior variance is used: instead of taking the mean of squared MLE betas, the prior variance is found by matching an upper quantile of the absolute value of MLE betas with an upper quantile of a zero-centered Normal distribution. CHANGES IN VERSION 1.3.17 ------------------------- o It is possible to use a log2 fold change prior (beta prior) and obtain likelihood ratio test p-values, although the default for test="LRT" is still betaPrior=FALSE. CHANGES IN VERSION 1.3.15 ------------------------- o The DESeq() function will automatically replace count outliers flagged by Cook's distance when there are 7 or more replicates. The DESeq() argument 'minReplicatesForReplace' (default 7) is used to decide which samples are eligible for automatic replacement. This default behavior helps to prevent filtering genes based on Cook's distance when there are many degrees of freedom. o The results() function produces an object of class 'DESeqResults' which is a simple subclass of 'DataFrame'. This class allows for methods to be written specifically for DESeq2 results. For example, plotMA() can be called on a 'DESeqResults' object. CHANGES IN VERSION 1.3.12 ------------------------- o Added a check in nbinomWaldTest which ensures that priors on logarithmic fold changes are only estimated for interactions terms, in the case that interaction terms are present in the design formula. CHANGES IN VERSION 1.3.6 ------------------------ o Reduced the amount of filtering from Cook's cutoff: maximum no longer includes samples from experimental groups with only 2 samples, the default F quantile is raised to 0.99, and a robust estimate of dispersion is used to calculate Cook's distance instead of the fitted dispersion. CHANGES IN VERSION 1.3.5 ------------------------ o New arguments to results(), 'lfcThreshold' and 'alternativeHypothesis', allow for tests of log fold changes which are above or below a given threshold. o plotMA() function now passes ellipses arguments to the results() function. CHANGES IN VERSION 1.1.32 ------------------------- o By default, use QR decomposition on the design matrix X. This stabilizes the GLM fitting. Can be turned off with the useQR argument of nbinomWaldTest() and nbinomLRT(). o Allow for "frozen" normalization of new samples using previous estimated parameters for the functions: estimateSizeFactors(), varianceStabilizingTransformation(), and rlogTransformation(). See manual pages for details and examples. CHANGES IN VERSION 1.1.31 ------------------------- o The adjustment of p-values and use of Cook's distance for outlier detection is moved to results() function instead of nbinomWaldTest(), nbinomLRT(), or DESeq(). This allows the user to change parameter settings without having to refit the model. CHANGES IN VERSION 1.1.24 ------------------------- o The results() function allows the user to specify a contrast of coefficients, either using the names of the factor and levels, or using a numeric contrast vector. Contrasts are only available for the Wald test differential analysis. CHANGES IN VERSION 1.1.23 ------------------------- o The results() function automatically performs independent filtering using the genefilter package and optimizing over the mean of normalized counts. CHANGES IN VERSION 1.1.21 ------------------------- o The regularized log transformation uses the fitted dispersions instead of the MAP dispersions. This prevents large, true log fold changes from being moderated due to a large dispersion estimate blind to the design formula. This behavior is also more consistent with the variance stabilizing transformation. CHANGES IN VERSION 1.0.10 ------------------------- o Outlier detection: Cook's distances are calculated for each sample per gene and the matrix is stored in the assays list. These values are used to determine genes in which a single sample disproportionately influences the fitted coefficients. These genes are flagged and the p-values set to NA. The argument 'cooksCutoff' of nbinomWaldTest() and nbinomLRT() can be used to control this functionality. CHANGES IN VERSION 1.0.0 ------------------------ o Base class: SummarizedExperiment is used as the superclass for storing the data. o Workflow: The wrapper function DESeq() performs all steps for a differential expression analysis. Individual steps are still accessible. o Statistics: Incorporation of prior distributions into the estimation of dispersions and fold changes (empirical-Bayes shrinkage). A Wald test for significance is provided as the default inference method, with the likelihood ratio test of the previous version also available. o Normalization: it is possible to provide a matrix of sample- *and* gene-specific normalization factors