data matrix. Note that the columns correspond to variables (“genes”)
and the rows to samples.
L
factor containing class labels for the two groups.
Details
diffmean.* computes the difference of means (i.e. the fold-change for log-transformed data).
rankprod.* computes the two-sided rank products statistic, i.e. the geometric mean of the
ranks of the pairwise absolute mean differences (Breitling et al. 2004). Note that for consistency with the other functions in this package the complement of the averaged ranks is returned
(i.e. rank 1 becomes ncol(X), rank 2 becomes ncol(X)-1, etc.).
Value
The *.stat functions directly return the respective statistic for each variable.
The corresponding *.fun functions return a function that produces the respective
statistics when applied to a data matrix (this is very useful for simulations).
This function is in part based on code from Henry Wirth.
References
Breitling, R., et al. 2004. Rank products: a simple, yet powerful, new method
to detect differentially regulated genes in replicated microarray experiments.
FEBS Letters 573:83-9.
See Also
studentt.stat,shrinkt.stat.
Examples
# load st library
library("st")
# load Choe et al. (2005) data
data(choedata)
X <- choe2.mat
dim(X) # 6 11475
L <- choe2.L
L
# L may also contain some real labels
L = c("group 1", "group 1", "group 1", "group 2", "group 2", "group 2")
# difference of means resp. fold change statistic
score = diffmean.stat(X, L)
order(abs(score), decreasing=TRUE)[1:10]
# [1] 4790 6620 1022 10979 970 35 2693 5762 5885 2
# two-sided rank products statistic
score = rankprod.stat(X, L)
order(score, decreasing=TRUE)[1:10]
# [1] 4790 1022 10979 6620 35 2693 970 5762 5885 2