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