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

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)

Opioid Dispensing Rates

Description

A dataset containing estimated opioid dispensing rate per 100 persons in United States, 2006-2020.

Usage

data(opioidData)

Format

data.frame; columns: fips = FIPS county code, State = State, County = County, X2006-X2020 = estimated opioid dispensing rate per 100 persons in each year

Source

https://www.cdc.gov/drugoverdose/rxrate-maps/index.html

simcurve

Description

This function generates two kinds of datasets. 1. Randomly generates curves with change/no change. 2. Generates true curves assumed from fixed coeffecients with some random noise.

Usage

simcurve(numareas = c(300, 300, 300), p = 0.05, type, normerr = 0.1)

Arguments

Details

If type = "random", the function generates curves with change/no change. If type = "fixed", the function generates true curves assumed from fixed coefficients with some random noise. If numareas is not specified, it is assumed as a vector of c(300,300,300). If normerr is not specified, it is assumed as a value of 0.01. It is ignored when type= "random".

Value

Output from the function is a list of two items:

• data - simulated data

• parameters - parameters used to generate the data

Examples

mydata_ran <- simcurve(numareas = c(300, 300, 300), p=0.01, type="random")

mydata_fixed <- simcurve(numareas = c(300, 300, 300), p=0.01, type="fixed", normerr = 0.1)

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

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))