MInt - A package for learning direct interaction networks

Quick Start

The basic workflow for MInt includes creating a MInt model object with a design matrix and response matrix and then using this object to estimate the parameters of the Poisson-multivariate normal hierarchical model.

As an example, consider the example data located in the data directory of this package. The files y.txt and x.txt contain a sample response matrix and design matrix, respectively. We’d like to use MInt to find evidence of direct interactions between the variables in the response after controlling for the covariates in the design matrix.

We begin by loading the MInt package and creating a MInt model object:

rm(list = ls()) # Clear workspace
library(MInt)

# Specify path to the design matrix
dFile <- system.file("extdata", "x.txt", package="MInt");

# Specify path to the response matrix
rFile <- system.file("extdata", "y.txt", package="MInt");


# Create the MInt model object
m <- mint(y=rFile, x=dFile, fmla = ~ feature1 + feature2)

The fmla argument specifies that feature1 and feature2 of the design matrix additively influence each response variable. However, other specifications are possible depeneding on your data. If, for example, you believe there may be an interaction between feature1 and feature2, you may consider passing fmla = ~ feature1 + feature2 + feature1*feature2. In general, you can specify any formula you like, just as you could to any R function that accepts formula objects (e.g. glm).

To estimate parameters of the model, we simply call:

m <- estimate(m)

## Iteration:  1  max(deltaP):  0.067014  Objective:  -1061.913  Converged:  FALSE 
## Iteration:  2  max(deltaP):  0.015160  Objective:  -1061.823  Converged:  FALSE 
## Iteration:  3  max(deltaP):  0.003953  Objective:  -1061.813  Converged:  FALSE 
## Iteration:  4  max(deltaP):  0.001160  Objective:  -1061.806  Converged:  FALSE 
## Iteration:  5  max(deltaP):  0.000396  Objective:  -1061.8  Converged:  FALSE 
## Iteration:  6  max(deltaP):  0.000181  Objective:  -1061.793  Converged:  FALSE 
## Iteration:  7  max(deltaP):  0.000122  Objective:  -1061.787  Converged:  FALSE 
## Iteration:  8  max(deltaP):  0.000114  Objective:  -1061.78  Converged:  FALSE 
## Iteration:  9  max(deltaP):  0.000111  Objective:  -1061.774  Converged:  FALSE 
## Iteration:  10  max(deltaP):  0.000109  Objective:  -1061.767  Converged:  FALSE 
## Iteration:  11  max(deltaP):  0.000108  Objective:  -1061.761  Converged:  FALSE 
## Iteration:  12  max(deltaP):  0.000107  Objective:  -1061.755  Converged:  FALSE 
## Iteration:  13  max(deltaP):  0.000106  Objective:  -1061.749  Converged:  FALSE 
## Iteration:  14  max(deltaP):  0.000105  Objective:  -1061.743  Converged:  FALSE 
## Iteration:  15  max(deltaP):  0.000104  Objective:  -1061.737  Converged:  FALSE 
## Iteration:  16  max(deltaP):  0.000103  Objective:  -1061.731  Converged:  FALSE 
## Iteration:  17  max(deltaP):  0.000102  Objective:  -1061.725  Converged:  FALSE 
## Iteration:  18  max(deltaP):  0.000101  Objective:  -1061.719  Converged:  FALSE 
## Iteration:  19  max(deltaP):  0.000100  Objective:  -1061.713  Converged:  TRUE

This will read in the data from the supplied file paths for the response and design matrix, initialize the optimizer, and perform inference. The MInt model object, m, has now been updated to include the raw data, optimization details, and parameter estimates. The estimated precision matrix is given by m$param$P. The corresponding partial correlation matrix is given by -cov2cor(m$param$P). A non-zero value in entry (i,j) of these matrices is indicative of a direct interaction, whereas a zero value suggests variables i and j do not directly interact.

Let’s see how the estimated partial correlation matrix compares to the true one:

P_true <- as.matrix( read.csv(system.file("extdata", "P_true.txt", package="MInt"), header=FALSE) );

-cov2cor(P_true)

##        V1   V2   V3
## [1,] -1.0 -0.8  0.0
## [2,] -0.8 -1.0  0.4
## [3,]  0.0  0.4 -1.0

-cov2cor(m$param$P)

##             y1         y2          y3
## y1 -1.00000000 -0.7718095  0.08642571
## y2 -0.77189686 -1.0000000  0.48359664
## y3  0.08651228  0.4836316 -1.00000000

Contact

This package is maintained by Surojit Biswas: surojitbiswas@g.harvard.edu