Table 1.

Factors considered and their levels or possible values, together with acronyms used through the text

Factor	Description	Values
Model	Evolutionary model of cancer progression	exp, Bozic, McF_4, McF_6
sh	Penalization of deviations from monotonicity	0, Inf (for ∞)
True graph	The true graph: the structure that encodes the order restrictions. All possible combinations of Number of nodes and Conjunction	11-A, 11-B, 9-A, 9-B, 7-A, 7-B
Number of nodes (NumNodes)	Number of genes or alterations	11, 9, 7
Conjunction	Whether or not the graph has conjunctions	Yes, No
Sample size (S.Size)	Number of samples used for reconstructing the graph	100, 200, 1000
Sampling time (S.Time)	When the sample is taken	Last, unif (for uniform)
Sampling type (S.Type)	How tissue is collected	singleC (for single cell), wholeT_0.5 (whole tumor, detection threshold=0.5), wholeT_0.01 (whole tumor, detection threshold=0.1)
Filtering	Method for selecting drivers, or filtering passengers, when the true drivers are not known	S1, S5, J1, J5 (for frequency of Single event and Joint frequency of events, with thresholds 1% and 5% respectively)
Method	Method for inferring the order restrictions	CBN, CBN-A, DiP, DiP-A, OT, OT-A

The within-data set factors, Filtering and Method (see text), are shown in italics. All other factors are among-data set factors. Sampling scheme, used through the text, refers to when (S.Time) and how (S.Type) we sample.