| ihclust {ihclust} | R Documentation |
Iterative Hierarchical Clustering (IHC)
Description
This function identifies inhomogeneous clusters using iterative hierarchical clustering (IHC) method.
Usage
ihclust(
data,
smooth = TRUE,
cor_criteria = 0.75,
max_iteration = 100,
verbose = TRUE
)
Arguments
data |
a numeric matrix, each row representing a time-series and each column representing a time point |
smooth |
if smooth = 'TRUE', a smooth function is applied before clustering |
cor_criteria |
pre-specified correlation criteria |
max_iteration |
maximum number of iterations |
verbose |
if verbose = 'TRUE', the result of a progress is printed |
Details
ihclust
The IHC algorithm implements the three steps as outlined below. First, the Initialization step clusters the data using hierarchical clustering. Second, cluster centers are obtained as an average of all the data points in the cluster. The Merging step considers each of the cluster centers (exemplars) as ‘new data point’, and use the same procedure described in the Initialization step to merge the exemplars into a new set of clusters. Third, the Pruning step streamlines the clusters and removes inconsistencies by reassessing the cluster membership by each data point.
Value
Output from the function is a list of three items:
Cluster_Label - the cluster label for each data point
Num_Iterations - total number of iterations
Unique_Clusters_in_Iteration - unique clusters in each iteration
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[1:500,4:18])
testchange_results <- testchange(data=mydata,perm=FALSE,time=seq(1,15,1))
data_change <- testchange_results$sig.change
clustering_results <- ihclust(data=data_change, smooth = TRUE,
cor_criteria = 0.75, max_iteration = 100, verbose = TRUE)