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'.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.5.0), foreach |
| Imports: |
stats, utils |
| Suggests: |
knitr, rmarkdown |
| Published: |
2019-07-07 |
| Author: |
Michael Klein |
| Maintainer: |
Michael Klein <michael.klein at yale.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
crso results |
Documentation:
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