On the other hand, simulation studies provide the full controls over the model and noise parameters to generate tailored data, whose underlying reference network is known for objective comparison with inferred networks [331]. GeneNetWeaver is a stochastic differential equation-based simulation tool and has been used to evaluate bulk-based GRNs such as DREAM4 challenge [366]. Chen and Mar generated simulated data to resemble scRNA-seq with a combined approach of GeneNetWeaver and added artificial dropout events to evaluate bulk-based, and single-cell-based GRN approaches [367]. Pratapa et al. introduced BEELINE as a single-cell transcriptome simulation framework leveraging Boolean network models [207]. Unlike GeneNetWeaver, BEELINE can simulate stochastic data with underlying cell trajectory, a hallmark feature in single-cell transcriptomes [207].
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