This function transform the count data to the log2 scale in a way
which minimizes differences between samples for rows with small counts,
and which normalizes with respect to library size.
The rlog transformation produces a similar variance stabilizing effect as
rlog is more robust in the
case when the size factors vary widely.
The transformation is useful when checking for outliers
or as input for machine learning techniques
such as clustering or linear discriminant analysis.
rlog takes as input a
DESeqDataSet and returns a
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logical, whether to blind the transformation to the experimental design. blind=TRUE should be used for comparing samples in an manner unbiased by prior information on samples, for example to perform sample QA (quality assurance). blind=FALSE should be used for transforming data for downstream analysis, where the full use of the design information should be made. If many of genes have large differences in counts due to the experimental design, it is important to set blind=FALSE for downstream analysis.
if TRUE, an approximation to the rlog transformation is made, wherein an optimal amount of shrinkage of sample-wise log fold changes is calculated for each gene. optimal is defined by maximizing the same posterior as in the standard rlog transformation.
by default, this is not provided and calculated automatically. if provided, this should be a vector as long as the number of rows of object, which is log2 of the mean normalized counts from a previous dataset. this will enforce the intercept for the GLM, allowing for a "frozen" rlog transformation based on a previous dataset.
a single value, the variance of the prior on the sample betas, which if missing is estimated from the data
in case dispersions have not yet been estimated for
Note that neither rlog transformation nor the VST are used by the
differential expression estimation in
DESeq, which always
occurs on the raw count data, through generalized linear modeling which
incorporates knowledge of the variance-mean dependence. The rlog transformation
and VST are offered as separate functionality which can be used for visualization,
clustering or other machine learning tasks. See the transformation section of the
vignette for more details.
The transformation does not require that one has already estimated size factors and dispersions.
The regularization is on the log fold changes of the count for each sample
over an intercept, for each gene. As nearby count values for low counts genes
are almost as likely as the observed count, the rlog shrinkage is greater for low counts.
For high counts, the rlog shrinkage has a much weaker effect.
The fitted dispersions are used rather than the MAP dispersions
(so similar to the
The prior variance for the shrinkag of log fold changes is calculated as follows:
a matrix is constructed of the logarithm of the counts plus a pseudocount of 0.5,
the log of the row means is then subtracted, leaving an estimate of
the log fold changes per sample over the fitted value using only an intercept.
The prior variance is then calculated by matching the upper quantiles of the observed
log fold change estimates with an upper quantile of the normal distribution.
A GLM fit is then calculated using this prior. It is also possible to supply the variance of the prior.
See the vignette for an example of the use and a comparison with
The transformed values, rlog(K), are equal to
rlog(K_ij) = log2(q_ij) = beta_i0 + beta_ij,
with formula terms defined in
The parameters of the rlog transformation from a previous dataset
can be frozen and reapplied to new samples. See the 'Data quality assessment'
section of the vignette for strategies to see if new samples are
sufficiently similar to previous datasets.
The frozen rlog is accomplished by saving the dispersion function,
beta prior variance and the intercept from a previous dataset,
rlog with 'blind' set to FALSE
(see example below).
SummarizedExperiment if a
DESeqDataSet was provided,
or a matrix if a count matrix was provided.
Note that for
SummarizedExperiment output, the matrix of
transformed values is stored in
To avoid returning matrices with NA values, in the case of a row
of all zeros, the rlog transformation returns zeros
(essentially adding a pseudocount of 1 only to these rows).
Reference for the regularized logarithm (rlog):
Michael I Love, Wolfgang Huber, Simon Anders: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014, 15:550. http://dx.doi.org/10.1186/s13059-014-0550-8
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dds <- makeExampleDESeqDataSet(m=6,betaSD=1) rld <- rlog(dds, blind=TRUE) dists <- dist(t(assay(rld))) plot(hclust(dists)) # run the rlog transformation on one dataset design(dds) <- ~ 1 dds <- estimateSizeFactors(dds) dds <- estimateDispersions(dds) rld <- rlog(dds, blind=FALSE) # apply the parameters to a new sample ddsNew <- makeExampleDESeqDataSet(m=1) mcols(ddsNew)$dispFit <- mcols(dds)$dispFit betaPriorVar <- attr(rld,"betaPriorVar") intercept <- mcols(rld)$rlogIntercept rldNew <- rlog(ddsNew, blind=FALSE, intercept=intercept, betaPriorVar=betaPriorVar)
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