Description Usage Arguments Details Value Note Author(s) References See Also Examples

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

1 |

`glmfit` |
a |

`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 |

`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 |

`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 |

`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.

`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 |

`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 |

`logCPM` |
average log2-counts per million, the average taken over all libraries. |

`PValue` |
p-values. |

`glmTreat`

was previously called `treatDGE`

in edgeR versions 3.9.10 and earlier.

Yunshun Chen and Gordon Smyth

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

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
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)
``` |

edgeR documentation built on June 25, 2018, 6 p.m.

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