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Table 1 Benchmarked statistical models to estimate measurement error in fitness scores

From: DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies

Error model type Based on Additional input? Information sharing across variants? Model parameters
Variance-based Empirical variance of replicate fitness No No 0
Bayesian regularization of variance [51] Empirical variance of replicate fitness Yes: Bayesian prior to regression on seq. counts Yes: prior estimated across all variants 3
Count-based Sequencing counts follow Poisson distribution No No 0
Enrich2 [36] Sequencing counts follow Poisson distribution Yes: mixed-effects from empirical variance No # variants
DiMSum Sequencing counts follow Poisson distribution Yes: modifier terms from empirical variance Yes: replicate-specific modifier terms estimated across all variants 3 × # replicates