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Identifying groups of similar objects using clustering approaches is one ofthe most frequently employed first steps in exploratory biomedical dataanalysis. Many clustering methods have been developed that pursue differentstrategies to identify the optimal clustering for a data set.  We previously published TiCoNE, an interactive clustering approach coupledwith de-novo network enrichment of identified clusters. However, in this firstversion time-series and network analysis remained two separate steps in thatonly time-series data was clustered, and identified clusters mapped to andenriched within a network in a second separate step.  In this work, we present TiCoNE 2: An extension that can now seamlesslyincorporate multiple data types within its composite clustering model.Systematic evaluation on 50 random data sets, as well as on 2,400 data setscontaining enriched cluster structure and varying levels of noise, shows thatour approach is able to successfully recover cluster patterns embedded inrandom data and that it is more robust towards noise than non-composite modelsusing only one data type, when applied to two data types simultaneously.  Herein, each data set was clustered using five different similarity functionsinto k=10/30 clusters, resulting to ~5,000 clusterings in total. We evaluatedthe quality of each derived clustering with the Jaccard index and an internalvalidity score. We used TiCoNE to calculate empirical p-values for allgenerated clusters with different permutation functions, resulting in ~80,000cluster p-values. We show, that derived p-values can be used to reliablydistinguish between foreground and background clusters.  TiCoNE 2 allows researchers to seamlessly analyze time-series data togetherwith biological interaction networks in an intuitive way and thereby providesmore robust results than single data type cluster analyses.

TiCoNE 2: A Composite Clustering Model for Robust Cluster Analyses on Noisy Data