| toptable {limma} | R Documentation |
Extract a table of the top-ranked genes from a linear model fit.
toptable(fit,coef=1,number=10,genelist=NULL,A=NULL,eb=NULL,adjust.method="holm",sort.by="B",...) topTable(fit,coef=1,number=10,genelist=NULL,adjust.method="holm",sort.by="B")
fit |
for toptable, this is an output list from lm.series, gls.series or rlm.series.
For topTable is an object of class MArrayLM. |
coef |
column number of the effect or contrast to rank the genes on |
number |
how many genes to pick out |
genelist |
a data frame containing the gene allocation list or a vector containing the gene names |
A |
matrix of A-values or vector of average A-values. |
eb |
output list from ebayes(fit) |
adjust.method |
method to use to adjust the P-values for multiple testing, e.g., "holm" or "fdr". See p.adjust for the available options. If NULL or "none" then the P-values are not adjusted. |
sort.by |
statistic to rank genes by. Possibilities are "M", "A", "T", "P" or "B". |
... |
any other arguments are passed to ebayes if eb is NULL |
This function summarizes a fit object produced by lm.series, gls.series or rlm.series by selecting the top-ranked genes for any given contrast.
A dataframe with a row for the number top genes and the following columns:
genelist |
if genelist was included as input |
M |
estimate of the effect or the contrast, on the log2 scale |
t |
moderated t-statistic |
P.Value |
nominal P-value |
B |
log odds that the gene is differentially expressed |
Gordon Smyth
ebayes, p.adjust, lm.series, gls.series, rlm.series.
# Simulate gene expression data, # 6 microarrays and 100 genes with first gene differentially expressed M <- matrix(rnorm(100*6,sd=0.3),100,6) M[1,1:3] <- M[1,1:3] + 2 # Design matrix includes two treatments, one for first 3 and one for last 3 arrays design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1)) fit <- lm.series(M,design=design) toptable(fit)