HCthresh {BioMark} | R Documentation |

## Biomarker thresholding by Higher Criticism

### Description

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.

### Usage

```
HCthresh(pvec, alpha = 0.1, plotit = FALSE)
```

### Arguments

`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. |

### Details

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).

### Value

A vector containing the ordered indices of the p values satisfying the HC criterion.

### Author(s)

Ron Wehrens

### References

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

### See Also

`get.biom`

for general approaches to obtain biomarkers
based on multivariate discriminant methods and t statistics

### Examples

```
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
```

*BioMark*version 0.4.5 Index]