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:

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(qvalue)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/qvalue/hedenfalk.Rd_%03d_medium.png", width=480, height=480)
> ### Name: hedenfalk
> ### Title: P-values and test-statistics from the Hedenfalk et al. (2001)
> ###   gene expression dataset
> ### Aliases: hedenfalk
> ### Keywords: dataset, hedenfalk
> 
> ### ** 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)

Call:
qvalue(p = p.pooled)

pi0:	0.669926	

Cumulative number of significant calls:

          <1e-04 <0.001 <0.01 <0.025 <0.05 <0.1   <1
p-value       15     76   265    424   605  868 3170
q-value        0      0     1     73   162  319 3170
local FDR      0      0     3     30    85  167 2241

> hist(qobj)
> plot(qobj)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>