shrinkRM: Shrink the size of the incidence matrix

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

Description

Shrink the size of the incidence matrix by making a prejudgement about which wholes (gene-sets) and parts (genes) have to be zeros in the optimal solution of ILP. See reference for details.

Usage

shrinkRM(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

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.

The amount of shrinkage may be dramatic, but it depends on the observed data y, the system I and system parameters alpha, gamma and p. When alpha is small and gamma is large the effects may be minimal. This function is invoked in sequentialRM.

Value

Return a list consisting of newI: the incidence matrix after shrinking, and newy: the corresponding part-level data.

Author(s)

Zhishi Wang, Michael Newton, 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)
## set the system parameters
alpha <- 0.00019
gamma <- 0.02279
p <- 0.00331
## Not run: 
## shrink the matrix
new <- shrinkRM(t2d$I, t2d$y, alpha, gamma, p)

## End(Not run)

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