| testchange {ihclust} | R Documentation |
testchange
Description
This function identifies geographic areas with significant change over time.
Usage
testchange(data, time, perm = FALSE, nperm = 100, numclust = 4, topF = 500)
Arguments
data |
a numeric matrix, each row representing a time-series and each column representing a time point |
time |
defines the time sequence |
perm |
if perm = 'TRUE', a permutation is performed |
nperm |
number of permuations |
numclust |
defines the number of clusters for the parallel processing |
topF |
number of top F values to be selected when perm = 'FALSE' |
Details
number of permutations of >=10,000 is ideal
Value
Output if perm = 'TRUE' is a list of three items:
perm.F - F values obtained from permutation tests
p.values - p-values obtained from permutation tests
p.adjusted - p-values adjusted by Benjamini-Hochberg method
Output if perm = 'False' is a list of three items:
obs.F - conventional F-statistic values
sig.change - areas with significant change over time pattern selected by top F-statistic values
sel.F - top F-statistic values selected
References
1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018). Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations. International Journal of Computational Biology and Drug Design, 11,135-153. doi: 10.1504/IJCBDD.2018.10011910. 2. Wu, S., & Wu, H. (2013). More powerful significant testing for time course gene expression data using functional principal component analysis approaches. BMC Bioinformatics, 14:6. 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016). Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans. Infectious Disease Modeling, 1, 28-39.
Examples
# This is an example not using the permutation approach
opioid_data_noNA <- opioidData[complete.cases(opioidData), ] #remove NAs
mydata <- as.matrix(opioid_data_noNA[,4:18])
testchange_results <- testchange(data=mydata,perm=FALSE,time=seq(1,15,1))