PT  - JOURNAL ARTICLE
AU  - Yuezhe Li
AU  - Tiffany Jann
AU  - Paola Vera-Licona
TI  - Benchmarking Time-Series Data Discretization on Inference Methods
AID  - 10.1101/378620
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 378620
4099  - http://biorxiv.org/content/early/2018/08/01/378620.short
4100  - http://biorxiv.org/content/early/2018/08/01/378620.full
AB  - The rapid development in quantitatively measuring DNA, RNA, and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider 4 published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods’ performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed.Availability All the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Licona’s lab Github repository: https://github.com/VeraLiconaResearchGroup/Benchmarking_TSDiscretizationsMSEmean squared errorCSRcue-signal-responsePKNprior knowledge networkTDBNtime-delayed dynamic Bayesian networkROC curvereceiver operating characteristic curveAUCarea under the curve.AUROCarea under the ROC curvebikmeansXbidirectional kmeans discretization with X levelserdalsErdal’s etal discretization method (Erdal et al., 2004)TDTtarget discretization thresholdiXequal width discretization with X levelsqXequal frequency discretization with X levelsmeandiscretization through comparing to mean valuekmeansXkmeans discretization with X levelsji&tanJi and Tan’s discretization method (Ji and Tan, 2004)soinovSoinov’s change of state method (Soinov et al., 2003)mean-sdmean plus standard discretization methodTSDtransilational state discretizationmaxYMax - Y% Max discretizationtopYTop%Y discretizationDiscreeTestTwo-stEp DIscretization Evaluation.