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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GRB2 unpublished dataset 1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GRB2 unpublished dataset 2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