topTable(fit, coef=NULL, number=10, genelist=fit$genes, adjust.method="BH", sort.by="B", resort.by=NULL, p.value=1, lfc=0, confint=FALSE) toptable(fit, coef=1, number=10, genelist=NULL, A=NULL, eb=NULL, adjust.method="BH", sort.by="B", resort.by=NULL, p.value=1, lfc=0, confint=FALSE, ...) topTableF(fit, number=10, genelist=fit$genes, adjust.method="BH", sort.by="F", p.value=1, lfc=0) topTreat(fit, coef=1, sort.by="p", resort.by=NULL, ...)
fitshould be an object of class
MArrayLMas produced by
topTable, can also be a vector of column subscripts, in which case the gene ranking is by F-statistic for that set of contrasts.
topTableonly, this defaults to
topTableonly, this defaults to
NULL, this will be automatically generated.
p.adjustfor the complete list of options. A
NULLvalue will result in the default adjustment method, which is
"none". (Permitted synonyms are
"P".) Possibilities for
"none". Possibilities for
topTreatare as for
topTableFinclude only genes with (at least one) absolute log-fold-changes greater than
topTreatdoes not remove genes but ranks genes by evidence that their log-fold-change exceeds
logFC? Alternatively, can take a numeric value between zero and one specifying the confidence level required.
toptable, other arguments are passed to
topTreat, other arguments are passed to
numbertop genes and the following columns:
topTableFthere may be several columns of log-fold-changes)
topTableunless more than one coef is specified)
fithad unique rownames, then the row.names of the above data.frame are the same in sorted order. Otherwise, the row.names of the data.frame indicate the row number in
fithad duplicated row names, then these are preserved in the
IDcolumn of the data.frame, or in
genelistalready contained an
toptableis an earlier interface and is retained only for backward compatibility.
These functions summarize the linear model fit object produced by
mrlm by selecting the top-ranked genes for any given contrast.
topTableF assume that the linear model fit has already been processed by
topTreat assumes that the fit has been processed by
The p-values for the coefficient/contrast of interest are adjusted for multiple testing by a call to
"BH" method, which controls the expected false discovery rate (FDR) below the specified value, is the default adjustment method because it is the most likely to be appropriate for microarray studies.
Note that the adjusted p-values from this method are bounds on the FDR rather than p-values in the usual sense.
Because they relate to FDRs rather than rejection probabilities, they are sometimes called q-values.
help("p.adjust") for more information.
Note, if there is no good evidence for differential expression in the experiment, that it is quite possible for all the adjusted p-values to be large, even for all of them to be equal to one.
It is quite possible for all the adjusted p-values to be equal to one if the smallest p-value is no smaller than
ngenes is the number of genes with non-missing p-values.
sort.by argument specifies the criterion used to select the top genes.
The choices are:
"logFC" to sort by the (absolute) coefficient representing the log-fold-change;
"A" to sort by average expression level (over all arrays) in descending order;
"t" for absolute t-statistic;
"p" for p-values; or
"B" for the
lods or B-statistic.
Normally the genes appear in order of selection in the output table.
If a different order is wanted, then the
resort.by argument may be useful.
topTable(fit, sort.by="B", resort.by="logFC") selects the top genes according to log-odds of differential expression and then orders the selected genes by log-ratio in decreasing order.
topTable(fit, sort.by="logFC", resort.by="logFC") would select the genes by absolute log-fold-change and then sort them from most positive to most negative.
topTableF ranks genes on the basis of moderated F-statistics for one or more coefficients.
topTable is called and
coef has two or more elements, then the specified columns will be extracted from
topTableF called on the result.
coef=NULL is the same as
topTableF, unless the fitted model
fit has only one column.
Toptable output for all probes in original (unsorted) order can be obtained by
write may be preferable if the intention is to write the results to a file.
A related method is
as.data.frame(fit) which coerces an
MArrayLM object to a data.frame.
number probes are listed.
Alternatively, by specifying
number=Inf, all genes with adjusted p-values below a specified value can be listed.
lfc gives the ability to filter genes by log-fold change.
This argument is not available for
treat already handles fold-change thresholding in a more sophisticated way.