glmTreat {edgeR} | R Documentation

## Test for Differential Expression Relative to a Threshold

### Description

Conduct genewise statistical tests for a given coefficient or contrast relative to a specified fold-change threshold.

### Usage

    glmTreat(glmfit, coef = ncol(glmfit$design), contrast = NULL, lfc = log2(1.2),
             null = "interval")

### Arguments

`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.
`null` | character string, choices are `"worst.case"` or `"interval"`. If `"worst.case"`, then the null hypothesis asssumes that the true logFC is on the boundary of the possible values, either at `lfc` or `-lfc`, whichever gives the largest p-value. This gives the most conservative results. If `"interval"`, then the null hypotheses assumes the true logFC to belong to a bounded interval of possible values.

### Details

`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.

Note that the `lfc` testing threshold used to define the null hypothesis is not the same as a log2-fold-change cutoff, as the observed log2-fold-change needs to substantially larger than `lfc` for the gene to be called as significant. In practice, modest values for `lfc` such as `log2(1.1)`, `log2(1.2)` or `log2(1.5)` are usually the most useful. In practice, setting `lfc=log2(1.2)` or `lfc=log2(1.5)` will usually cause most differentially expressed genes to have estimated fold-changes of 2-fold or greater, depending on the sample size and precision of the experiment.

Note also that `glmTreat` constructs test statistics using the unshrunk log2-fold-changes(`unshrunk.logFC`) rather than the log2-fold-changes that are usually reported (`logFC`). If no shrinkage has been applied to the log-fold-changes, i.e., the glms were fitted with `prior.count=0`, then `unshrunk.logFC` and `logFC` are the same and the former is omitted from the output object.

`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.

`glmTreat` with positive `lfc` gives larger p-values than would be obtained with `lfc=0`. If `null="worst.case"`, then `glmTreat` conducts a test closely analogous to the `treat` function in the limma package. This conducts a test if which the null hypothesis puts the true logFC on the boundary of the `[-lfc,lfc]` interval closest to the observed logFC. If `null="interval"`, then the null hypotheses assumes an interval of possible values for the true logFC. This approach is somewhat less conservative.

Note that, unlike other edgeR functions such as `glmLRT` and `glmQLFTest`, `glmTreat` can only accept a single contrast. If `contrast` is a matrix with multiple columns, then only the first column will be used.

### Value

`glmTreat` produces an object of class `DGELRT` with the same components as for `glmfit` plus the following:

`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 `table` contains the following columns:

`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.

### Note

`glmTreat` was previously called `treatDGE` in edgeR versions 3.9.10 and earlier.

### Author(s)

Yunshun Chen and Gordon Smyth

### References

McCarthy, D. J., and Smyth, G. K. (2009). Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25, 765-771. http://bioinformatics.oxfordjournals.org/content/25/6/765

### See Also

`topTags` displays results from `glmTreat`.

`treat` is the corresponding function in the limma package, designed for use with normally distributed log-expression data rather than for negative binomial counts.

### Examples

    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)

[Package edgeR version 3.32.1 Index]