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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.