Home

Welcome to the FBCanalysis wiki!

A detailed manual can be found and downloaded here: Reference manual

The following manual gives an overview of the included applicable functions within the FBCanalysis package.

General functionalities for biomedical time series data preprocessing:

patient_list: Process patient time series data by interpolation options and store data in an object of type list
patient_boxplot: Visualize patient(s) time series data in a boxplot for indicated parameter
patient_hist: Visualize patient time series data in a histogram for indicated parameter
patient_ts_plot: Visualize patient time series data in a time series plot for indicated parameter

Functionalities to determine and process Earth Mover’s Distances between time series distribution data pairs:

emd_heatmap: Visualize an Earth Mover’s Distance Square Matrix as a heatmap
emd_matrix: Generate a Earth Mover’s Distance Matrix out of time series data list entries
max_fluc: Determine pair of maximum fluctuation difference in a list storing time series data

Earth Mover’s Distance Matrix clustering:

clust_matrix: Cluster Earth Mover Distance Square Matrix data

Functionalities to perform enrichment analysis on clustered time series data based on Earth Mover’s Distances:

add_enrich: Add enrichment data and preprocess for analysis
add_clust2enrich: Add clustering assignments to enrichment data frame
add_clust2ts: Add clustering assignments to time series data
enr_obs_clust: Observe specific cluster for overview and p-values
sim_sample_enr: Simulate random sampling for NA entries in enrichment data and check stability of resulting p-values for the enrichment parameters

Functionalities to perform a workflow to measure and evaluate commonly used clustering validation measures:

clValid_flow: Start interactive console workflow to calculate and evaluate cluster validation measures
init_clValid: Initialize Cluster Validation Measure Analysis in the context of Fluc- tuation Based Clustering (FBC) analysis

Functionalities to test time series data clustering model stability upon random data removal:

rnd_dat_rm: Remove random data from time series data list
jaccard_run_cognate: Simulate random data removal from time series data list and determine Jaccard index via Cognate Cluster approach for multiple random data removal steps
jaccard_run_emd: Simulate random data removal from time series data list and determine Jaccard index via Earth Mover’s Distance approach for multiple
random data removal steps
sim_jaccard_cognate: Simulate random data removal from time series data list and determine Jaccard index via Cognate Cluster approach
sim_jaccard_emd: Simulate random data removal from time series data list and determine Jaccard index via Earth Mover’s Distance approach

Helper function that is frequently used:

znorm: z-normalise data