Table of Top Genes from Linear Model Fit
Extract a table of the top-ranked genes from a linear model fit.
topTable(fit, coef=NULL, number=10, genelist=fit$genes, adjust.method="BH",
sort.by="B", resort.by=NULL, p.value=1, lfc=0, confint=FALSE)
topTableF(fit, number=10, genelist=fit$genes, adjust.method="BH",
sort.by="F", p.value=1, lfc=0)
topTreat(fit, coef=1, sort.by="p", resort.by=NULL, ...)fit |
list containing a linear model fit produced by |
coef |
column number or column name specifying which coefficient or contrast of the linear model is of interest. For |
number |
maximum number of genes to list |
genelist |
data frame or character vector containing gene information.
For |
adjust.method |
method used to adjust the p-values for multiple testing. Options, in increasing conservatism, include |
sort.by |
character string specifying which statistic to rank the genes by.
Possible values for |
resort.by |
character string specifying statistic to sort the selected genes by in the output data.frame. Possibilities are the same as for |
p.value |
cutoff value for adjusted p-values. Only genes with lower p-values are listed. |
lfc |
minimum absolute log2-fold-change required.
|
confint |
logical, should confidence 95% intervals be output for |
... |
other |
These functions summarize the linear model fit object produced by lmFit, lm.series, gls.series or mrlm by selecting the top-ranked genes for any given contrast, or for a set of contrasts.
topTable assumes that the linear model fit has already been processed by eBayes.
topTreat assumes that the fit has been processed by treat.
If coef has a single value, then the moderated t-statistics and p-values for that coefficient or contrast are used.
If coef takes two or more values, the moderated F-statistics for that set of coefficients or contrasts are used.
If coef is left NULL, then all the coefficients or contrasts in the fitted model are used, except that any coefficient named (Intercept) will be removed.
The p-values for the coefficient/contrast of interest are adjusted for multiple testing by a call to p.adjust.
The "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.
See 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 1/ngenes where ngenes is the number of genes with non-missing p-values.
The 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" or "t" for absolute t-statistic; "P" or "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.
For example, 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.
Or 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.
By default number probes are listed.
Alternatively, by specifying p.value and number=Inf, all genes with adjusted p-values below a specified value can be listed.
The argument lfc gives the ability to filter genes by log-fold change.
This argument is not available for topTreat because treat already handles fold-change thresholding in a more sophisticated way.
The function topTableF is scheduled for removal in a future version of limma.
It is equivalent to topTable with coef=NULL.
A dataframe with a row for the number top genes and the following columns:
genelist |
one or more columns of probe annotation, if genelist was included as input |
logFC |
estimate of the log2-fold-change corresponding to the effect or contrast (for |
CI.L |
left limit of confidence interval for |
CI.R |
right limit of confidence interval for |
AveExpr |
average log2-expression for the probe over all arrays and channels, same as |
t |
moderated t-statistic (omitted for |
F |
moderated F-statistic (omitted for |
P.Value |
raw p-value |
adj.P.Value |
adjusted p-value or q-value |
B |
log-odds that the gene is differentially expressed (omitted for |
If fit had 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 fit.
If fit had duplicated row names, then these are preserved in the ID column of the data.frame, or in ID0 if genelist already contained an ID column.
Although topTable enables users to set p-value and lfc cutoffs simultaneously, this is not generally recommended.
If the fold changes and p-values are not highly correlated, then the use of a fold change cutoff can increase the false discovery rate above the nominal level.
Users wanting to use fold change thresholding are usually recommended to use treat and topTreat instead.
In general, the adjusted p-values returned by adjust.method="BH" remain valid as FDR bounds only when the genes remain sorted by p-value.
Resorting the table by log-fold-change can increase the false discovery rate above the nominal level for genes at the top of resorted table.
Gordon Smyth
An overview of linear model and testing functions is given in 06.LinearModels.
See also p.adjust in the stats package.
# See lmFit examples
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