ILP: Perform an integer linear programming

In atbroman/Rolemodel: Gene-set analysis via MFA-ILP

Description

Maximum a posteriori (MAP) estimate via integer linear programming (ILP).

Usage

ILP(I, y, alpha, gamma, p)

Arguments

I	The incidence 0-1 matrix with unique row and column names, where rows are parts (genes) and columns are wholes (gene-sets).
y	Gene-level 0-1 data with the same names as the row names of I.
alpha	The false positive rate in role model, numeric value between 0 and 1. See reference.
gamma	The true positive rate in role model, numeric value between 0 and 1. See reference.
p	The prior active probability of wholes in role model, numeric value between 0 and 1. See reference.

Details

R package Rglpk is used to perform the integer linear programming. Generally, alpha and gamma can be estimated from the gene-level data by users themselves (see reference for examples), and alpha is less than gamma. p can be estimated via R package MGSA with alpha and gamma fixed. Since ILP is a complex problem in the optimization field, the running time might be very long. This function is invoked in sequentialRM.

Value

The output has the same structure as Rglpk_solve_LP in the Rglpk package, which is a list consisting of optimum, solution (in the order of wholes and parts) and status.

optimum	the value of the objective function at the optimum
solution	the vector of optimal coefficients (0-1vector)
status	an integer with status information about the solution returned. If the control parameter canonicalize_status is set (the default) then it will return 0 for the optimal solution being found, and non-zero otherwise. If the control parameter is set to FALSE it will return the GLPK status codes.

Author(s)

Zhishi Wang, Michael Newton and Subhrangshu Nandi.

References

Zhishi W., Qiuling H., Bret L. and Michael N.: A multi-functional analyzer uses parameter constaints to improve the efficiency of model-based gene-set analysis (2013).

See Also

sequentialRM

Examples

data(t2d)

## Use 5 and 10 as the trimming parameters
Isub <- subRM(t2d$I, 5, 10)
## the corresponding gene-level data
ysub <- t2d$y[rownames(Isub)]

## set the system parameters
alpha <- 0.00019
gamma <- 0.02279
p <- 0.00331
## perform the ILP
res <- ILP(Isub, ysub, alpha, gamma, p)

atbroman/Rolemodel documentation built on May 10, 2019, 2:08 p.m.