Table 2.

An explanation of the parameters used by Forest.

w	This parameter influences the number of separate trees detected, which can aid in identifying functionally distinct processes. Higher values of w lead to more trees in the optimal forest, while lower values force most prizes to be found in the same tree. Values usually range from 1–10. See Tuncbag et al 2013[14] for a more detailed explanation.
b	This parameter linearly scales the prizes, thereby changing the relative weighting of edge weights and node prizes. Higher values lead to larger trees, including some low-confidence edges, while lower values force networks to be small and use only high confidence edges, and lead to the possible exclusion of some prize terminals. Values usually range from 1–20.
D	This parameter sets the maximum depth from the dummy node, or root of the tree, to the leaf nodes. Higher values lead to long pathways, while lower values lead to shorter disparate pathways. Values usually range from 5–15.
mu	This parameter controls negative prizes in Forest. Negative prizes are explained in detail above. The default value is zero, and if you want to use negative prizes, values usually range from 0.0001 to 0.1.
garnetBeta	This parameter controls the relative weighting of TF scores derived from Garnet and prize values on proteomic nodes. Higher values will encourage the inclusion of more TF nodes in the network, while lower values force networks to include only the most significant or pathway-relevant TF nodes. Typically, the value for this parameter is set to the median value of the proteomic prizes divided by the median value of the TF scores