Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs a default analysis through the steps:
estimation of size factors: estimateSizeFactors
estimation of dispersion: estimateDispersions
Negative Binomial GLM fitting and Wald statistics: nbinomWaldTest
For complete details on each step, see the manual pages of the respective
functions. After the DESeq
function returns a DESeqDataSet object,
results tables (log2 fold changes and pvalues) can be generated
using the results
function. See the manual page
for results
for information on independent filtering and
pvalue adjustment for multiple test correction.
1 2 3 4 
object 
a DESeqDataSet object, see the constructor functions

test 
either "Wald" or "LRT", which will then use either
Wald significance tests (defined by 
fitType 
either "parametric", "local", or "mean"
for the type of fitting of dispersions to the mean intensity.
See 
betaPrior 
whether or not to put a zeromean normal prior on
the nonintercept coefficients
See 
full 
the full model formula, this should be the formula in

reduced 
a reduced formula to compare against, e.g. the full model with a term or terms of interest removed, only used by the likelihood ratio test 
quiet 
whether to print messages at each step 
minReplicatesForReplace 
the minimum number of replicates required
in order to use 
modelMatrixType 
either "standard" or "expanded", which describe
how the model matrix, X of the GLM formula is formed.
"standard" is as created by 
parallel 
if FALSE, no parallelization. if TRUE, parallel
execution using 
BPPARAM 
an optional parameter object passed internally
to 
The differential expression analysis uses a generalized linear model of the form:
K_ij ~ NB(mu_ij, alpha_i)
mu_ij = s_j q_ij
log2(q_ij) = x_j. beta_i
where counts K_ij for gene i, sample j are modeled using
a Negative Binomial distribution with fitted mean mu_ij
and a genespecific dispersion parameter alpha_i.
The fitted mean is composed of a samplespecific size factor
s_j and a parameter q_ij proportional to the
expected true concentration of fragments for sample j.
The coefficients beta_i give the log2 fold changes for gene i for each
column of the model matrix X.
The samplespecific size factors can be replaced by
genespecific normalization factors for each sample using
normalizationFactors
. For details on the fitting of the log2
fold changes and calculation of pvalues see nbinomWaldTest
(or nbinomLRT
if using test="LRT"
).
Experiments without replicates do not allow for estimation of the dispersion
of counts around the expected value for each group, which is critical for
differential expression analysis. If an experimental design is
supplied which does not contain the necessary degrees of freedom for differential
analysis, DESeq
will provide a message to the user and follow
the strategy outlined in Anders and Huber (2010)
under the section 'Working without replicates', wherein all the samples
are considered as replicates of a single group for the estimation of dispersion.
As noted in the reference above: "Some overestimation of the variance
may be expected, which will make that approach conservative."
Furthermore, "while one may not want to draw strong conclusions from such an analysis,
it may still be useful for exploration and hypothesis generation."
The argument minReplicatesForReplace
is used to decide which samples
are eligible for automatic replacement in the case of extreme Cook's distance.
By default, DESeq
will replace outliers if the Cook's distance is
large for a sample which has 7 or more replicates (including itself).
This replacement is performed by the replaceOutliers
function. This default behavior helps to prevent filtering genes
based on Cook's distance when there are many degrees of freedom.
See results
for more information about filtering using
Cook's distance, and the 'Dealing with outliers' section of the vignette.
Unlike the behavior of replaceOutliers
, here original counts are
kept in the matrix returned by counts
, original Cook's
distances are kept in assays(dds)[["cooks"]]
, and the replacement
counts used for fitting are kept in assays(object)[["replaceCounts"]]
.
Note that if a log2 fold change prior is used (betaPrior=TRUE)
then expanded model matrices will be used in fitting. These are
described in nbinomWaldTest
and in the vignette. The
contrast
argument of results
should be used for
generating results tables.
a DESeqDataSet
object with results stored as
metadata columns. These results should accessed by calling the results
function. By default this will return the log2 fold changes and pvalues for the last
variable in the design formula. See results
for how to access results
for other variables.
Michael Love
DESeq2 reference:
Michael I Love, Wolfgang Huber, Simon Anders: Moderated estimation of fold change and dispersion for RNAseq data with DESeq2. Genome Biology 2014, 15:550. http://dx.doi.org/10.1186/s1305901405508
DESeq reference:
Simon Anders, Wolfgang Huber: Differential expression analysis for sequence count data. Genome Biology 2010, 11:106. http://dx.doi.org/10.1186/gb20101110r106
1 2 3 4 5  dds < makeExampleDESeqDataSet(betaSD=1, n=100)
dds < DESeq(dds)
res < results(dds)
ddsLRT < DESeq(dds, test="LRT", reduced= ~ 1)
resLRT < results(ddsLRT)

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