Table 1

NPA Method Characteristics
Method Main features Assumptions Pros Cons
Strength

Linear, unbiased.

Based on log2 differential measurements (e.g., log2 fold changes of measurements).

The contributions from the noisy downstream measurables sum up to zero. Intuitive. Noisy/biased signals can artificially decrease/increase the results.
GPI

Based on log2 differential measurements.

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-log2 scale.

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 log2 differential measurements.

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

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