Description Usage Arguments Details Value Author(s) References See Also Examples
Higher Criticism (HC) is a second-level significance testing approach
to determine which variables in a multivariate set show significant
differences in two classes. Function HCthresh
selects those p
values that are significantly different from what would be expected
from their uniform distribution under the null hypothesis.
1 |
pvec |
Vector of p values. |
alpha |
Parameter of the HC approach: the maximal fraction of differentially expressed p values. |
plotit |
Logical, whether or not a plot should be produced. |
In HC, one tests the deviation of the expected behaviour of p values
under a null distribution. Function HCthresh
implements the
approach by Donoho and Jin to find out which of these correspond to
real differences. The prerequisites are that the true biomarkers are
rare (consist of only a small fraction of all variables) and weak (are
not able to discriminate between the two classes all by themselves).
A vector containing the ordered indices of the p values satisfying the HC criterion.
Ron Wehrens
David Donoho and Jiashun Jin: Higher criticism thresholding: Optimal feature selection when useful features are rare and weak. PNAS 108:14790-14795 (2008).
Ron Wehrens and Pietro Franceschi: Thresholding for Biomarker Selection in Multivariate Data using Higher Criticism. Mol. Biosystems (2012). In press. DOI: 10.1039/C2MB25121C
get.biom
for general approaches to obtain biomarkers
based on multivariate discriminant methods and t statistics
1 2 3 4 5 6 7 8 | data(spikedApples)
bms <- get.biom(spikedApples$dataMatrix, rep(0:1, each = 10),
type = "coef", fmethod = "studentt")
bms.pvalues <- 2 * (1 - pt(abs(bms[[1]]), 18))
sum(bms.pvalues < .05) ## 15
sum(p.adjust(bms.pvalues, method = "fdr") < .05) ## 4
signif.bms <- HCthresh(bms.pvalues, plotit = TRUE)
length(signif.bms) ## 11
|
Loading required package: pls
Attaching package: 'pls'
The following object is masked from 'package:stats':
loadings
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-12
Loading required package: MASS
Loading required package: st
Loading required package: sda
Loading required package: entropy
Loading required package: corpcor
Loading required package: fdrtool
Warning message:
In get.biom(spikedApples$dataMatrix, rep(0:1, each = 10), type = "coef", :
Y has only two values: assuming discrimination!
[1] 15
[1] 4
[1] 11
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.