Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution
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 p-values) can be generated
using the results function.
Shrunken LFC can then be generated using the lfcShrink function.
All support questions should be posted to the Bioconductor
support site: http://support.bioconductor.org.
DESeq(
object,
test = c("Wald", "LRT"),
fitType = c("parametric", "local", "mean", "glmGamPoi"),
sfType = c("ratio", "poscounts", "iterate"),
betaPrior,
full = design(object),
reduced,
quiet = FALSE,
minReplicatesForReplace = 7,
modelMatrixType,
useT = FALSE,
minmu = if (fitType == "glmGamPoi") 1e-06 else 0.5,
parallel = FALSE,
BPPARAM = bpparam()
)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", "mean", or "glmGamPoi"
for the type of fitting of dispersions to the mean intensity.
See |
sfType |
either "ratio", "poscounts", or "iterate"
for the type of size factor estimation. See
|
betaPrior |
whether or not to put a zero-mean normal prior on
the non-intercept coefficients
See |
full |
for |
reduced |
for |
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 |
useT |
logical, passed to |
minmu |
lower bound on the estimated count for fitting gene-wise dispersion
and for use with |
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 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. Analysis without replicates was deprecated in v1.20 and is no longer supported since v1.22.
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.
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 p-values for the last
variable in the design formula. See results for how to access results
for other variables.
Michael Love
Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. https://doi.org/10.1186/s13059-014-0550-8
For fitType="glmGamPoi":
Ahlmann-Eltze, C., Huber, W. (2020) glmGamPoi: Fitting Gamma-Poisson Generalized Linear Models on Single Cell Count Data. bioRxiv. https://doi.org/10.1101/2020.08.13.249623
link{results}, lfcShrink, 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) # moderated log2 fold changes resultsNames(dds) resLFC <- lfcShrink(dds, coef=2, type="apeglm") # an alternate analysis: likelihood ratio test ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1) resLRT <- results(ddsLRT)
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