```
"estimateTagwiseDisp"(y, prior.df=10, trend="movingave", span=NULL, method="grid", grid.length=11, grid.range=c(-6,6), tol=1e-06, verbose=FALSE, ...)
"estimateTagwiseDisp"(y, group=NULL, lib.size=NULL, dispersion, AveLogCPM=NULL, prior.df=10, trend="movingave", span=NULL, method="grid", grid.length=11, grid.range=c(-6,6), tol=1e-06, verbose=FALSE, ...)
```

y

matrix of counts or a

`DGEList`

object.prior.df

prior degrees of freedom.

trend

method for estimating dispersion trend. Possible values are

`"movingave"`

(default), `"loess"`

and `"none"`

.span

width of the smoothing window, as a proportion of the data set.

method

method for maximizing the posterior likelihood.
Possible values are

`"grid"`

(default) for interpolation on grid points or `"optimize"`

to call the function of the same name.grid.length

for

`method="grid"`

, the number of points on which the interpolation is applied for each tag.grid.range

for

`method="grid"`

, the range of the grid points around the trend on a log2 scale.tol

for

`method="optimize"`

, the tolerance for Newton-Rhapson iterations.verbose

logical, if

`TRUE`

then diagnostic ouput is produced during the estimation process.group

vector or factor giving the experimental group/condition for each library.

lib.size

numeric vector giving the total count (sequence depth) for each library.

dispersion

common dispersion estimate, used as an initial estimate for the tagwise estimates.

AveLogCPM

numeric vector giving average log2 counts per million for each tag

...

other arguments that are not currently used.

- prior.df
- prior degrees of freedom.
- prior.n
- estimate of the prior weight.
- tagwise.dispersion
- numeric vector of the tagwise dispersion estimates.
- span
- width of the smoothing window, in terms of proportion of the data set.

`estimateTagwiseDisp.DGEList`

adds the following components to the input `DGEList`

object:
`estimateTagwiseDisp.default`

returns a numeric vector of the tagwise dispersion estimates.
The prior values for the dispersions are determined by a global trend. The individual tagwise dispersions are then squeezed towards this trend. The prior degrees of freedom determines the weight given to the prior. The larger the prior degrees of freedom, the more the tagwise dispersions are squeezed towards the global trend. If the number of libraries is large, the prior becomes less important and the tagwise dispersion are determined more by the individual tagwise data.

If `trend="none"`

, then the prior dispersion is just a constant, the common dispersion.
Otherwise, the trend is determined by a moving average (`trend="movingave"`

) or loess smoother applied to the tagwise conditional log-likelihood.
`method="loess"`

applies a loess curve of degree 0 as implemented in `loessByCol`

.

`method="optimize"`

is not recommended for routine use as it is very slow.
It is included for testing purposes.

Note that the terms `tag' and `gene' are synonymous here. The function is only named `Tagwise' for historical reasons.

`estimateCommonDisp`

is usually run before `estimateTagwiseDisp`

.`movingAverageByCol`

and `loessByCol`

implement the moving average or loess smoothers.

```
# True dispersion is 1/5=0.2
y <- matrix(rnbinom(250*4,mu=20,size=5),nrow=250,ncol=4)
dge <- DGEList(counts=y,group=c(1,1,2,2))
dge <- estimateCommonDisp(dge)
dge <- estimateTagwiseDisp(dge)
```

Run the code above in your browser using DataLab