MINE Application

The mine application is a simple script (it will be installed together the minepy library) which computes the MINE statistics on a comma-separated values (CSV) file. The first column of the file must contain the variable names and each variable must have the same number of samples. The file must be in the form:

var1_name,1,2.5,3,4,5,6,7,8,9,10
var2_name,8,7,6,5,6,6,6,1.23,4,4
var3_name,1,7,3,5,6,6,6,3,4,4.2
var4_name,...
...

Usage:

Usage: mine infile [-a <alpha>] [-c <c>] [-o <file>] [-m <var index>] [-p <var1 index> <var2 index>]

MINE Python v. 0.3.4 [Homepage: minepy.sf.net]. The mine script compares by
default all pairs of variables against each other. It writes an output file
where each column contains MIC (strength), MIC-r^2 (nonlinearity), MAS (non-
monotonicity), MEV (functionality), MCN (complexity) and Pearson (r). The
input must be a comma-separated values file where the first column must
contain the variable names. Each variable must have the same numer of samples.

Options:
  -h, --help            show this help message and exit
  -a <alpha>, --alpha=<alpha>
                        the exponent in B(n) = n^alpha (default: 0.6.) alpha
                        must be in (0, 1.0]
  -c <c>, --clumps=<c>  determines how many more clumps there will be than
                        columns in every partition. Default value is 15,
                        meaning that when trying to draw Gx grid lines on the
                        x-axis, the algorithm will start with at most 15*Gx
                        clumps (default: 15). c must be > 0
  -o <file>, --output=<file>
                        output filename (default: mine_out.csv)
  -m <var index>, --master=<var index>
                        variable <var index> vs. all <var index> must be in
                        [1, number of variables in file]
  -p <var1 index> <var2 index>, --pair=<var1 index> <var2 index>
                        variable <var1 index> vs. variable <var2 index> <var1
                        index> and <var2 index> must be in [1, number of
                        variables in file]

Examples

Spellman Gene Expression dataset

(dataset details: http://www.exploredata.net/Downloads)

Compute the MINE statistics for variable #1 (time) vs. all the other variables, with alpha=0.67 and c=15.

1. Download the dataset (http://minepy.sourceforge.net/data/Spellman.csv)

2. From a terminal, run:

   $ mine Spellman.csv -a 0.67 -c 15 -m 1 -o Spellman_MINE.txt

Baseball dataset

(dataset details: http://www.exploredata.net/Downloads)

Compute the MINE statistics all pairs of variables against each other, with alpha=0.7 and c=15.

1. Download the dataset (http://minepy.sourceforge.net/data/MLB2008.csv)

2. From a terminal, run:

   $ mine MLB2008.csv -a 0.7 -c 15 -o MLB2008_MINE.txt

Microbiome dataset

(dataset details: http://www.exploredata.net/Downloads)

Compute the MINE statistics all pairs of variables against each other, with alpha=0.551 and c=10.

1. Download the dataset (http://minepy.sourceforge.net/data/Microbiome.csv)

2. From a terminal, run:

   $ mine Microbiome.csv -a 0.551 -c 10 -o Microbiome_MINE.txt