hedenfalk: P-values and test-statistics from the Hedenfalk et al. (2001)...

In StoreyLab/qvalue: Q-value estimation for false discovery rate control

P-values and test-statistics from the Hedenfalk et al. (2001) gene expression dataset

Description

The data from the breast cancer gene expression study of Hedenfalk et al. (2001) were obtained and analyzed. A comparison was made between 3,226 genes of two mutation types, BRCA1 (7 arrays) and BRCA2 (8 arrays). The data included here are p-values, test-statistics, and permutation null test-statistics obtained from a two-sample t-test analysis on a set of 3170 genes, as described in Storey and Tibshirani (2003).

Usage

data(hedenfalk)

Value

A list called hendfalk containing:

p	Vector of 3,170 p-values of tests comparing BRCA1 to BRCA2.
stat	Vector of 3,170 absolute two-sample t-statistics comparing BRCA1 to BRCA2.
stat0	A 3,170 by 100 matrix of absolute two-sample t-statistics from 100 independent permutations of the BRCA1 and BRCA2 labels; the row stat0[i,]. contains the permutation statistics corresponding to observed statistic stat[i].

References

Hedenfalk I et al. (2001). Gene expression profiles in hereditary breast cancer. New England Journal of Medicine, 344: 539-548.

Storey JD and Tibshirani R. (2003). Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences, 100: 9440-9445.
http://www.pnas.org/content/100/16/9440.full

See Also

qvalue, empPvals

Examples

# import data
data(hedenfalk)
stat <- hedenfalk$stat
stat0 <- hedenfalk$stat0 #vector from null distribution

p.pooled <- empPvals(stat=stat, stat0=stat0)
p.testspecific <- empPvals(stat=stat, stat0=stat0, pool=FALSE)

#compare pooled to test-specific p-values
qqplot(p.pooled, p.testspecific); abline(0,1)

# calculate q-values and view results
qobj <- qvalue(p.pooled)
summary(qobj)
hist(qobj)
plot(qobj)

StoreyLab/qvalue documentation built on Sept. 4, 2023, 11:24 p.m.