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Table 2 DiMSum error model parameters and error model performance in leave-one-out cross-validation across twelve DMS datasets

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

DMS dataset No. of replicates Error model parameters (avg ± s.d. across replicates) Estimated error magnitude relative to data
minput moutput \( \sqrt{a} \)^ Variance-based Bayesian reg. of variance [51] Count-based Enrich2 [36] DiMSum
FOS-JUN [20] 3 1.1 ± 0.0 1.6 ± 0.7 0.02 ± 0.01 0.02 0.65 0.88 0.98 1.04
FOS [20] 3 6.3 ± 0.4 5.0 ± 0.3 0.02 ± 0.01 0.01 0.65 0.41 0.58 0.97
GB1 [5] 3 1.1 ± 0.1 1 ± 0 0.04 ± 0.02 0.001 0.6 0.83 0.99 0.98
GRB2 unpublished dataset 1 3 6.2 ± 1.2 1.1 ± 0.1 0.05 ± 0.02 0.024 0.63 0.37 0.59 0.84
GRB2 unpublished dataset 2 3 1 ± 0 1 ± 0 0.03 ± 0.02 0.017 0.62 0.79 0.93 0.98
TDP-43 (290-331) [6] 3 1.3 ± 0.4 1.5 ± 0.1 0.07 ± 0.05 0.003 0.64 0.76 0.91 0.98
TDP-43 (332-373) [6] 4 1.5 ± 0.6 1.2 ± 0.4 0.1 ± 0.06 0.27 0.85 0.71 0.9 0.92
tRNA NaCl + 37C [7] 6 1 ± 0 1.1 ± 0.1 0.03 ± 0.02 0.72 0.94 0.92 1.09 0.96
tRNA 23C [50] 5 85 ± 55 96 ± 78 0.15 ± 0.04 0.54 0.87 0.064 0.57 0.81
tRNA 30C [50] 5 201 ± 187 121 ± 99 0.14 ± 0.07 0.58 0.90 0.059 0.59 0.88
tRNA 37C [50] 3 38 ± 19 39 ± 11 0.04 ± 0.01 0.01 0.63 0.15 0.32 0.96
tRNA DMSO [50] 3 101 ± 48 192 ± 124 0.04 ± 0.01 0.066 0.63 0.08 0.24 0.97
  1. The inverse standard deviation of the z-score distribution from leave-one-out cross-validation (see the “Methods” section)
  2. ^Square root of additive error term a gives a standard deviation-based estimate of lower variability bound