crso - Cancer Rule Set Optimization ('crso')
An algorithm for identifying candidate driver combinations
in cancer. CRSO is based on a theoretical model of cancer in
which a cancer rule is defined to be a collection of two or
more events (i.e., alterations) that are minimally sufficient
to cause cancer. A cancer rule set is a set of cancer rules
that collectively are assumed to account for all of ways to
cause cancer in the population. In CRSO every event is
designated explicitly as a passenger or driver within each
patient. Each event is associated with a patient-specific,
event-specific passenger penalty, reflecting how unlikely the
event would have happened by chance, i.e., as a passenger. CRSO
evaluates each rule set by assigning all samples to a rule in
the rule set, or to the null rule, and then calculating the
total statistical penalty from all unassigned event. CRSO uses
a three phase procedure find the best rule set of fixed size K
for a range of Ks. A core rule set is then identified from
among the best rule sets of size K as the rule set that best
balances rule set size and statistical penalty. Users should
consult the 'crso' vignette for an example walk through of a
full CRSO run. The full description, of the CRSO algorithm is
presented in: Klein MI, Cannataro V, Townsend J, Stern DF and
Zhao H. "Identifying combinations of cancer driver in
individual patients." BioRxiv 674234 [Preprint]. June 19, 2019.
<doi:10.1101/674234>. Please cite this article if you use
'crso'.
