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