`varianceStabilizingTransformation`

(VST)
that provides much faster estimation of the dispersion trend used to determine
the formula for the VST. The speed-up is accomplished by
subsetting to a smaller number of genes in order to estimate this dispersion trend.
The subset of genes is chosen deterministically, to span the range
of genes' mean normalized count.
This wrapper for the VST is not blind to the experimental design:
the sample covariate information is used to estimate the global trend
of genes' dispersion values over the genes' mean normalized count.
It can be made strictly blind to experimental design by first
assigning a `design`

of `~1`

before running this function,
or by avoiding subsetting and using `varianceStabilizingTransformation`

.`vst(object, blind = TRUE, nsub = 1000, fitType = "parametric")`

object

a DESeqDataSet or a matrix of counts

blind

logical, whether to blind the transformation to the experimental
design (see

`varianceStabilizingTransformation`

)nsub

the number of genes to subset to (default 1000)

fitType

for estimation of dispersions: this parameter
is passed on to

`estimateDispersions`

(options described there)- a DESeqTranform object or a matrix of transformed, normalized counts

```
dds <- makeExampleDESeqDataSet(n=20000, m=20)
vsd <- vst(dds)
```

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