Description Usage Arguments Value Author(s) References Examples
This function inputs gene-level statistics and returns a vector of estimated posterior probabilities of differential expression for each gene.
1 2 |
obs.stat |
Vector of observed gene-level statistics. |
null |
Vector of null gene-level statistics or a character (such as "rnorm") indicating the random generating mechanism for the null statistics. |
B |
Number of simulations under the null. |
pos |
If set to |
p0 |
Value (between |
K |
Number of intervals used in probability estimation. A good value in
practice seems to be the length of |
dim.basis |
Basis dimension ( |
... |
If |
Vector of the estimated gene-level probabilities of differential expression.
John D. Storey, Jeffrey T. Leek, Simina M. Boca
Storey, J., J. Akey, and L. Kruglyak (2005): Multiple locus linkage analysis of genomewide expression in yeast, PLoS Biology, 3.
Boca S.M., H. Corrada Bravo, B. Caffo, J.T. Leek, and G. Parmigiani (2010): A decision-theory approach to interpretable set analysis for high-dimensional data, JHU Biostat Working Paper 211, http://www.bepress.com/jhubiostat/paper211/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | set.seed(10831)
stat <- rnorm(1000,sd=sqrt(1/10),mean=c(rep(1,50),rep(0,950)))
set.seed(23134)
stat0 <- matrix(rnorm(1000*20,sd=sqrt(1/10)),nrow=1000)
post.prob.genes.1 <-
postProb(stat,
null = stat0,
K = 1000)
set.seed(23134)
post.prob.genes.2 <-
postProb(stat,
null = "rnorm",
B = 20,
K = 1000,
mean = 0, sd = sqrt(1/10))
##The results are the same, since in both case we are considering 20
##simulations of 1000 null statistics from a N(0, 1/10) distribution,
##using the same seed!
identical(post.prob.genes.1, post.prob.genes.2)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.