 
WT simulations

KO Simulations


Parameter

Changepoint sensitivity

Changepoint PPV

Edges sensitivity

Edges PPV

Changepoint sensitivity

Changepoint PPV

Edges sensitivity

Edges PPV



0.2

94.2

95.1

73.9

99.2

100

100

100

98.5


0.4

90.8

94.1

79.3

97.9

100

99

100

98.2


0.6

87.8

92.5

73.9

96.9

96

97.1

99.2

95.4


0.8

75.4

96.3

78.8

96.4

81.4

97.8

97.6

91.4

Noise

1

80.5

96.6

74.7

97.5

69.1

95

95.1

88.8


1.2

71.7

96.2

78.4

97.6

28.1

82.4

97.2

87.6


1.4

58.7

94.6

79

95.9

23.6

89.7

92.7

87.5


1.6

52.9

91.8

74.4

97.2

10.8

75.9

79.1

86.8


1.8

60.8

94.5

76

95.6

4.8

81.8

76.8

85.6


1

78.5

97.5

1.3

100

79

98.8

18.1

82.4


2

92

92

24.7

98.5

97

96.5

98.7

92.5

Phase size

3

90

94.2

50.8

98.6

99.5

99

100

93.6


4

94.5

94

74.5

98.6

99.5

99.5

100

96.4


5

96

99

76.9

96.8

99.5

97.5

100

94.7


12

100

99

92.6

98

99

97.1

100

97.8


5

93.7

95.5

82.4

99.1

_

_

_

_

# of parent

10

81.1

88.3

69.7

96.4

_

_

_

_

genes

20

62.4

83.4

51.2

97.3

_

_

_

_


40

54

77.2

33.1

96.4

_

_

_

_

 To evaluate ARTIVA performances, two types of data were used: WT simulations corresponding to timeseries expression data with no knowledge of potential transcription factors and KO simulations corresponding to several timeseries expression data of a simulated wildtype strain and knockout strains for each known transcription factor (see Methods and Additional file 2 for more details). The default values of the parameters used for the simulation study are: # of timepoints n = 12; # of changepoints k~\mathcal{U}(\{1,\mathrm{..},n/4\}); maximal # of edges = 5; parent to target coefficient ~\mathcal{U}([2,0.1]\cup [0.1,2]); phase sizes ~\mathcal{U}(\{3,\mathrm{..},n\});\# # of replicates r = 8 for WT simulations, r = 4 for KO simulations; noise standard deviation ~\mathcal{N}(0,0.5); total # of simulations = 200 for each condition. In this table, the value of noise intensity, phase size and number of parent genes change according to the parameter under study (all other parameters were set to default). In each condition, the ability of ARTIVA to detect all true phase changepoints and model edges (Sensitivity) and to detect only true positives (Positive Predictive Value, PPV) was calculated. Overall, this simulation study allows us to gain confidence in ARTIVA results (≃ 80% of PPV and sensitivity) for a given set of parameters (noise standard error is on the order of the mean value of the regression coefficients, number of measurements in a regulatory phase > 8 and less than 20 parent genes).