Test for Differential Expression Relative to a Threshold
Conduct genewise statistical tests for a given coefficient or contrast relative to a specified fold-change threshold.
glmTreat(glmfit, coef=ncol(glmfit$design), contrast=NULL, lfc=0) treatDGE(glmfit, coef=ncol(glmfit$design), contrast=NULL, lfc=0)
DGEGLMobject, usually output from
- 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
- 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
- numeric scalar specifying the absolute value of the log2-fold change threshold above which differential expression is to be considered.
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
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.
glmTreat is equivalent to
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
logFC are essentially the same. Hence they are merged into one column of
glmTreat constructs test statistics using
unshrunk.logFC rather than
glmTreat was previously called
The old function name is now deprecated and will be removed in a future release of edgeR.
- absolute value of the specified log2-fold-change threshold.
- data frame with the same rows as
glmfitcontaining the log2-fold-changes, average log2-counts per million and p-values, ready to be displayed by
- character string describing the coefficient or the contrast being tested. The data frame
- shrunk log2-fold-change of expression between conditions being tested.
- unshrunk log2-fold-change of expression between conditions being tested. Exists only when
prior.countis not equal to 0 for
- average log2-counts per million, the average taken over all libraries.
glmTreatproduces an object of class
DGELRTwith the same components as for
glmfitplus the following:
tablecontains the following columns:
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
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)