comp.stat {DEDS} | R Documentation |
This function computes test statistics, e.g., t-statitics, F-statistics, SAM, fold changes, moderated t or F statistics, B statistics, for each row of a microarray data matrix.
comp.stat(X, L, test = c("t", "fc", "sam", "f", "modt", "modf", "B"), extra = NULL)
X |
A matrix, with m rows corresponding to variables
(hypotheses) and n columns to observations. In the case of gene
expression data, rows correspond to genes and columns to mRNA
samples. The data can be read using read.table . | ||||||||||||||
L |
A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1. | ||||||||||||||
test |
A character string specifying the statistic to be
used to test the null hypothesis of no association between the
variables and the class labels.
| ||||||||||||||
extra |
Extra parameter needed for the test specified; see
deds.genExtra . |
The function comp.stat
interfaces to a C function and
computes various statistics for differential expression in the C
environment and therefore faster than functions in R. However,
functions in R that are implemented in the DEDS packages may have
more flexibility in terms of specifications of arguments. Below is a
table the details comp.stat
and its equivalent R functions
in the DEDS package. Note that all the R functions listed in the 2nd
column of the table below return a function with bindings for a series
of arguments which accept the microarray data matrix as its single
argument and compute accordingly statistics.
Interface to C | R functions | Statistics |
deds.stat(X, L, test="t") | tTest(L=NULL, mu=0, var.equal=FALSE) | t statistics |
deds.stat(X, L, test="fc") | FC(L=NULL, is.log=TRUE, FUN=mean) | fold change |
deds.stat(X, L, test="sam") | Sam(L=NULL, prob=0.5, B=200, stat.only=TRUE, verbose=FALSE, deltas, s.step=0.01, alpha.step=0.01, plot.it=FALSE) | SAM statistics |
deds.stat(X, L, test="f") | fTest(L=NULL) | F statistics |
deds.stat(X, L, test="modt") | tmodTest(L=NULL) | moderated t statistics |
deds.stat(X, L, test="modf") | fmodTest(L=NULL) | moderated F statistics |
deds.stat(X, L, test="B") | BTest(L=NULL, proportion=0.01) | B statistics |
A vector of test statistics for each row of the matrix.
Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.
For references on B-statistics and moderated t and F statistics:
Lönnstedt, I. and Speed, T. P. (2002). Replicated microarray data. Statistica Sinica 12, 31-46.
Smyth, G. K. (2003). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. http://www.statsci.org/smyth/pubs/ebayes.pdf
deds.genExtra
,
for B statistics: lm.series
and
ebayes
X <- matrix(rnorm(1000,0,0.5), nc=10) L <- rep(0:1,c(5,5)) # genes 1-10 are differentially expressed X[1:10,6:10]<-X[1:10,6:10]+1 # t statistics tstat <- comp.stat(X, L, test="t") # SAM, fudge factor set as the median of pooled genewise standard deviations samstat <- comp.stat(X, L, test="sam") # SAM, fudge factor set as the 90% of pooled genewise standard deviations samstat <- comp.stat(X, L, test="sam", extra=c(0.9)) # moderated t modtstat <- comp.stat(X, L, test="modt") # B, proportion of differentially expressed genes is set at default, 1% Bstat <- comp.stat(X, L, test="B") # B, proportion of differentially expressed genes is set at 10% Bstat <- comp.stat(X, L, test="B", extra=c(0.1))