## Table 1 |
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NPA Method Characteristics |
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Method |
Main features |
Assumptions |
Pros |
Cons |

Strength |
Linear, unbiased. Based on log |
The contributions from the noisy downstream measurables sum up to zero. | Intuitive. | Noisy/biased signals can artificially decrease/increase the results. |

GPI |
Based on log Down-weights weak differential measurements using false non-discovery rates. |
The noisy downstream measurables have low false non-discovery rates which can be used to minimize their contributions. |
Intuitive. False non-discovery rate depends on the number of experimental replicates. |
False non-discovery rate depends on the number of experimental replicates. |

MASS |
Linear and unbiased in absolute non-log Dependent on absolute changes in measurements. |
Absolute changes in measurements are more important than relative changes. | Intuitive. | Measurements must be directly comparable across all downstream measurables. |

EPI |
Based on log Up-weights strong differential measurements without using false non-discovery rates. |
The downstream measurables with higher differential values should have stronger contributions than those with lower differential values. |
More robust to noisy signals than Strength. Highest sensitivity to strong differential measurements. |
Less intuitive. Bootstrapping is needed for calculating Uncertainty. |

Martin * et al.*

Martin * et al.* *BMC Systems Biology* 2012 **6**:54 doi:10.1186/1752-0509-6-54