```
glmTreat(glmfit, coef=ncol(glmfit$design), contrast=NULL, lfc=0)
treatDGE(glmfit, coef=ncol(glmfit$design), contrast=NULL, lfc=0)
```

glmfit

a

`DGEGLM`

object, usually output from `glmFit`

or `glmQLFit`

.coef

integer or character vector indicating which coefficients of the linear model are to be tested equal to zero. Values must be columns or column names of

`design`

. Defaults to the last coefficient. Ignored if `contrast`

is specified.contrast

numeric vector specifying the contrast of the linear model coefficients to be tested against the log2-fold-change threshold. Length must equal to the number of columns of

`design`

. If specified, then takes precedence over `coef`

.lfc

numeric scalar specifying the absolute value of the log2-fold change threshold above which differential expression is to be considered.

- lfc
- absolute value of the specified log2-fold-change threshold.
- table
- data frame with the same rows as
`glmfit`

containing the log2-fold-changes, average log2-counts per million and p-values, ready to be displayed by`topTags`

. - comparison
- character string describing the coefficient or the contrast being tested. The data frame
- logFC
- shrunk log2-fold-change of expression between conditions being tested.
- unshrunk.logFC
- unshrunk log2-fold-change of expression between conditions being tested. Exists only when
`prior.count`

is not equal to 0 for`glmfit`

. - logCPM
- average log2-counts per million, the average taken over all libraries.
- PValue
- p-values.

`glmTreat`

produces an object of class `DGELRT`

with the same components as for `glmfit`

plus the following:
`table`

contains the following columns:
`glmTreat`

implements a test for differential expression relative to a minimum required fold-change threshold.
Instead of testing for genes which have log-fold-changes different from zero, it tests whether the log2-fold-change is greater than `lfc`

in absolute value.
`glmTreat`

is analogous to the TREAT approach developed by McCarthy and Smyth (2009) for microarrays.`glmTreat`

detects whether `glmfit`

was produced by `glmFit`

or `glmQLFit`

.
In the former case, it conducts a modified likelihood ratio test (LRT) against the fold-change threshold.
In the latter case, it conducts a quasi-likelihood (QL) F-test against the threshold.

If `lfc=0`

, then `glmTreat`

is equivalent to `glmLRT`

or `glmQLFTest`

, depending on whether likelihood or quasi-likelihood is being used.

If there is no shrinkage on log-fold-changes, i.e., fitting glms with `prior.count=0`

, then `unshrunk.logFC`

and `logFC`

are essentially the same. Hence they are merged into one column of `logFC`

in `table`

.
Note that `glmTreat`

constructs test statistics using `unshrunk.logFC`

rather than `logFC`

.

`glmTreat`

was previously called `treatDGE`

.
The old function name is now deprecated and will be removed in a future release of edgeR.

`topTags`

displays results from `glmTreat`

.
ngenes <- 100 n1 <- 3 n2 <- 3 nlibs <- n1+n2 mu <- 100 phi <- 0.1 group <- c(rep(1,n1), rep(2,n2)) design <- model.matrix(~as.factor(group)) ### 4-fold change for the first 5 genes i <- 1:5 fc <- 4 mu <- matrix(mu, ngenes, nlibs) mu[i, 1:n1] <- mu[i, 1:n1]*fc counts <- matrix(rnbinom(ngenes*nlibs, mu=mu, size=1/phi), ngenes, nlibs) d <- DGEList(counts=counts,lib.size=rep(1e6, nlibs), group=group) gfit <- glmFit(d, design, dispersion=phi) tr <- glmTreat(gfit, coef=2, lfc=1) topTags(tr)