GeneCycle (version 1.1.5)

fisher.g.test: Fisher's Exact g Test for Multiple (Genetic) Time Series

Description

fisher.g.test calculates the p-value(s) according to Fisher's exact g test for one or more time series. This test is useful to detect hidden periodicities of unknown frequency in a data set. For an application to microarray data see Wichert, Fokianos, and Strimmer (2004).

Usage

fisher.g.test(x, …)

Arguments

x

vector or matrix with time series data (one time series per column).

…

arguments passed to periodogram

Value

A vector of p-values (one for each time series). Multiple testing may then be done using the the false discover rate approach (function fdrtool).

Details

Fisher (1929) devised an exact procedure to test the null hypothesis of Gaussian white noise against the alternative of an added deterministic periodic component of unspecified frequency. The basic idea behind the test is to reject the null hypothesis if the periodogram contains a value significantly larger than the average value (cf. Brockwell and Davis, 1991). This test is useful in the context of microarray genetic time series analysis as a gene selection method - see Wichert, Fokianos and Strimmer (2004) for more details. Note that in the special case of a constant time series the p-value returned by fisher.g.test is exactly 1 (i.e. the null hypothesis is not rejected).

References

Fisher, R.A. (1929). Tests of significance in harmonic analysis. Proc. Roy. Soc. A, 125, 54--59.

Brockwell, P.J., and Davis, R.A. (1991). Time Series: Theory and Methods (2nd ed). Springer Verlag. (the g-test is discussed in section 10.2).

Wichert, S., Fokianos, K., and Strimmer, K. (2004). Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20:5-20.

See Also

fdrtool.

Examples

# NOT RUN {
# load GeneCycle library
library("GeneCycle")

# load data set
data(caulobacter)

# how many samples and and how many genes?
dim(caulobacter)

# p-values from Fisher's g test
pval.caulobacter <- fisher.g.test(caulobacter)
pval.caulobacter

# compute Fdr and fdr values
fdr.out <- fdrtool(pval.caulobacter, statistic="pvalue")

# how many significant?
sum(fdr.out$qval < 0.05) # tail area-based Fdr
sum(fdr.out$lfdr < 0.2)  # density-based local fdr

# }