Description
This function transforms 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 asvarianceStabilizingTransformation,though rlog is more robust in thecase when the size factors vary widely.The transformation is useful when checking for outliersor as input for machine learning techniquessuch as clustering or linear discriminant analysis.rlog takes as input a DESeqDataSet and returns aRangedSummarizedExperiment object.Usage
rlog(object, blind=TRUE, intercept, betaPriorVar, fitType="parametric")
rlogTransformation(object, blind=TRUE, intercept, betaPriorVar, fitType="parametric")
Arguments
object
a DESeqDataSet, or matrix of counts
blind
logical, whether to blind the transformation to the experimentaldesign. blind=TRUE should be used for comparing samples in an manner unbiased byprior 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.blind=FALSE will skip re-estimation of the dispersion trend, if this has already been calculated.If many of genes have large differences in counts due tothe experimental design, it is important to set blind=FALSE for downstreamanalysis.
intercept
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" rlogtransformation based on a previous dataset.You will also need to provide mcols(object)$dispFit.
betaPriorVar
a single value, the variance of the prior on the samplebetas, which if missing is estimated from the data
fitType
in case dispersions have not yet been estimated for object,this parameter is passed on to estimateDispersions (options described there).
Value
- a
DESeqTransform if a DESeqDataSet was provided,or a matrix if a count matrix was provided as input.Note that for DESeqTransform output, the matrix oftransformed values is stored in assay(rld).To avoid returning matrices with NA values, in the case of a rowof all zeros, the rlog transformation returns zeros(essentially adding a pseudocount of 1 only to these rows).Details
Note that neither rlog transformation nor the VST are used by thedifferential expression estimation inDESeq, which alwaysoccurs on the raw count data, through generalized linear modeling whichincorporates knowledge of the variance-mean dependence. The rlog transformationand VST are offered as separate functionality which can be used for visualization,clustering or other machine learning tasks. See the transformation section of thevignette for more details.The transformation does not require that one has already estimated size factorsand dispersions.
The regularization is on the log fold changes of the count for each sampleover an intercept, for each gene. As nearby count values for low counts genesare 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 varianceStabilizingTransformation).
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 ofthe 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 varianceStabilizingTransformation.
The transformed values, rlog(K), are equal to$rlog(K_ij) = log2(q_ij) = beta_i0 + beta_ij$,with formula terms defined in DESeq.
The parameters of the rlog transformation from a previous datasetcan be frozen and reapplied to new samples. See the 'Data quality assessment'section of the vignette for strategies to see if new samples aresufficiently similar to previous datasets. The frozen rlog is accomplished by saving the dispersion function,beta prior variance and the intercept from a previous dataset,and running rlog with 'blind' set to FALSE(see example below).
References
Reference for regularized logarithm (rlog):Michael I Love, Wolfgang Huber, Simon Anders: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014, 15:550. http://dx.doi.org/10.1186/s13059-014-0550-8
See Also
plotPCA, varianceStabilizingTransformation, normTransformExamples
dds <- makeExampleDESeqDataSet(m=6,betaSD=1)rld <- rlog(dds)dists <- dist(t(assay(rld)))plot(hclust(dists))# run the rlog transformation on one datasetdesign(dds) <- ~ 1dds <- estimateSizeFactors(dds)dds <- estimateDispersions(dds)rld <- rlog(dds, blind=FALSE)# apply the parameters to a new sampleddsNew <- makeExampleDESeqDataSet(m=1)mcols(ddsNew)$dispFit <- mcols(dds)$dispFitbetaPriorVar <- attr(rld,"betaPriorVar")intercept <- mcols(rld)$rlogInterceptrldNew <- rlog(ddsNew, blind=FALSE, intercept=intercept, betaPriorVar=betaPriorVar) Run the code above in your browser using DataLab
