Description Usage Arguments Details Value Author(s) References Examples

Use the sequential approach introduced in the reference to speed up the running of integer linear programming (ILP).

1 | ```
sequentialRM(I, y, nupstart, by = 1, alpha, gamma, p)
``` |

`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. |

`nupstart` |
The starting upper bound used in the sequential approach. |

`by` |
The increment of the upper bound used in the sequential approach, default value 1. |

`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. |

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.

We first perform the ILP on an initial incidence matrix (the smaller matrix) with lower bound equal lower bound of I and upper bound nupstart; then do another ILP, making use of the results obtained from the last ILP, on the bigger incidence matrix with upper bound equal nupstart + by. This process is repeated until the original incidence matrix I is reached. The suggested value for nupstart is 10. `sequentialRM`

is our main function to perform the ILP calculation. `ILP`

, `shrinkRM`

and `optimalRM`

are all invoked in this function.

Return a list consisting of `onwholes`

: the active wholes, i.e., the MFA-ILP (MAP) estimate, and `sol`

: has the same structure with the output of `ILP`

,

`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. |

Zhishi Wang, Michael Newton and Subhrangshu Nandi.

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).

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(t2d)
## Isub <- subRM(t2d$I,5,20)
## ysub <- t2d$y[rownames(Isub)]
## set the system parameters
alpha <- 0.00019
gamma <- 0.02279
p <- 0.00331
## use the sequential approach to get the MAP estimate on a smaller
## example of type 2 diabetes
## res <- sequentialRM(Isub, ysub, nupstart = 10, by =1, alpha, gamma, p)
``` |

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