ihclust: Iterative Hierarchical Clustering (IHC)

In ihclust: Iterative Hierarchical Clustering (IHC)

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)

ihclust documentation built on April 27, 2022, 9:05 a.m.