estimateSizeFactors
estimateDispersions
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 p-values) can be generated
using the results function. See the manual page
for results for information on independent filtering and
p-value adjustment for multiple test correction.
DESeq(object, test = c("Wald", "LRT"), fitType = c("parametric", "local", "mean"), betaPrior, full = design(object), reduced, quiet = FALSE, minReplicatesForReplace = 7, modelMatrixType, parallel = FALSE, BPPARAM = bpparam())DESeqDataSet,
DESeqDataSetFromMatrix,
DESeqDataSetFromHTSeqCount.nbinomWaldTest),
or the likelihood ratio test on the difference in deviance between a
full and reduced model formula (defined by nbinomLRT)estimateDispersions for description.nbinomWaldTest for description of the calculation
of the beta prior. By default, the beta prior is used only for the
Wald test, but can also be specified for the likelihood ratio test.test="LRT", the full model formula,
which is restricted to the formula in design(object).
alternatively, it can be a model matrix constructed by the user.
advanced use: specifying a model matrix for full and test="Wald"
is possible if betaPrior=FALSEtest="LRT", a reduced formula to compare against,
i.e., the full formula with the term(s) of interest removed.
alternatively, it can be a model matrix constructed by the userreplaceOutliers on a
sample. If there are samples with so many replicates, the model will
be refit after these replacing outliers, flagged by Cook's distance.
Set to Inf in order to never replace outliers.model.matrix using the
design formula. "expanded" includes an indicator variable for each
level of factors in addition to an intercept. for more information
see the Description of nbinomWaldTest.
betaPrior must be set to TRUE in order for expanded model matrices
to be fit.BiocParallel, see next argument BPPARAM.
A note on running in parallel using BiocParallel: it may be
advantageous to remove large, unneeded objects from your current
R environment before calling DESeq,
as it is possible that R's internal garbage collection
will copy these files while running on worker nodes.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 p-values for the last
variable in the design formula. See results for how to access results
for other variables.
$$ K_{ij} \sim \textrm{NB}( \mu_{ij}, \alpha_i) $$ $$ \mu_{ij} = s_j q_{ij} $$ $$ \log_2(q_{ij}) = x_{j.} \beta_i $$
where counts $K_ij$ for gene i, sample j are modeled using
a Negative Binomial distribution with fitted mean $mu_ij$
and a gene-specific dispersion parameter $alpha_i$.
The fitted mean is composed of a sample-specific 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 sample-specific size factors can be replaced by
gene-specific normalization factors for each sample using
normalizationFactors.
For details on the fitting of the log2 fold changes and calculation of p-values,
see nbinomWaldTest if using test="Wald",
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(dds)[["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.
nbinomWaldTest, nbinomLRT
# see vignette for suggestions on generating
# count tables from RNA-Seq data
cnts <- matrix(rnbinom(n=1000, mu=100, size=1/0.5), ncol=10)
cond <- factor(rep(1:2, each=5))
# object construction
dds <- DESeqDataSetFromMatrix(cnts, DataFrame(cond), ~ cond)
# standard analysis
dds <- DESeq(dds)
res <- results(dds)
# an alternate analysis: likelihood ratio test
ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1)
resLRT <- results(ddsLRT)
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