| Title: | LPCover: Functionality for integer programming methods for covering |
|---|---|
| Description: | Integer programming functionality for different 'covering' optimizations as presented in Ke et al, "Efficient Representations of Tumor Diversity with Paired DNA-RNA Anomalies". |
| Authors: | Wikum Dinalankara <[email protected]>, Luigi Marchionni <[email protected]>, Qian Ke <[email protected]> |
| Maintainer: | Wikum Dinalankara <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.01 |
| Built: | 2024-11-01 11:17:22 UTC |
| Source: | https://github.com/marchionniLab/lpcover |
computeMinimalCovering(mat, alpha = 0.05, maxsol = 100, J = 1, solver = "")computeMinimalCovering(mat, alpha = 0.05, maxsol = 100, J = 1, solver = "")
mat |
A binary data matrix with each column corresponding to a sample and each row corresponding to a feature. |
alpha |
A value in the 0 <= alpha < 1 range indicating what proportion of samples to be considered as outlier. By default alpha = 0.05, indicating 95 \itemmaxsolThe number of optimal solutions to be returned. Default is 100. \itemJThe number of times each sample is to be covered. By default J=1, indicating that each sample is to be covered with at least one feature. \itemsolverA character string indicating whether to use gurobi or lpSolve. |
A list with items "obj": the objective returned by the optimization (as a vector), "sol": a character matrix of solutions(each column a solution), "r": a list where each element contains vectors of results obtained for x and lamba vectors, and "result": the direct output returned by the optimization (by either gurobi or lpSolve). Function for computing the minimal covering for a given binary data matrix given a minimum proportion of samples to cover optim.out = computeMinimalCovering(mat=mat, alpha=0.05, maxsol=1, J=1, solver="lpSolve")
cover, optimize