ARACNe-AP

Network Reverse Engineering through AP inference of Mutual Information

Overview

ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks with Adaptive Partitioning) is a complete overhaul of the first ARACNe implementation, originally published by Margolin and colleagues in BMC Bioinformatics in 2006.

Lachmann A, Giorgi FM, Lopez G, Califano A. ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics. 2016 Jul 15;32(14):2233-5. doi: 10.1093/bioinformatics/btw216. Epub 2016 Apr 23.

Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006 Mar 20;7 Suppl 1:S7. doi: 10.1186/1471-2105-7-S1-S7

Building ARACNe-AP

ARACNe-AP requires JDK > 1.8 and ANT. Use the following command in the repository root directory to build the jar and documentation:

ant main

The jar will be placed in dist/aracne.jar. The documentation can be found in docs/index.html.

Using ARACNe-AP

Input files needed to run ARACNe

See below for file format specification (or download the test files from our repository)

1. Gene expression matrix.
2. List of regulators (e.g. Transcription Factors)

Steps required to run ARACNe

1. Calculate a threshold for Mutual Information
2. Run ARACNe on bootstraps of the input matrix
3. Consolidate, i.e. combine the bootstraps into a final network file

Optional ways to run ARACNe

1. Removing DPI (Data Process Inequality) will preserve every edge that passes the Mutual Information threshold.
2. ARACNe by default operates on a bootstrapped version of the input matrix. It is possible to turn this feature off.
3. During the consolidation step, a Bonferroni correction is applied to the p-values obtained from the Poisson distribution used to determine the significance of each edge according to their appearance in different bootstraps. It is possible to turn this correction off, with the result of having a slightly bigger output network.

Output of ARACNe-AP

Apart from the individual bootstraps, the consolidation step of ARACNe-AP will produce a file called network.txt in the output folder provided by the user. This file shows every significant interaction in four columns

1. The regulator.
2. The target.
3. The MI (Mutual Information) of the pair.
4. The pvalue of the pair (assessed during the consolidation step by integrating the bootstraps).

Input file format

Gene lists

A text file, containing one gene symbol per line, e.g.

g165
g196
g257
g367
g401
g1390

Dataset

A text file, tab separated, with genes on rows and samples on columns

gene    Sample1   Sample2   Sample3
g1   1.8 5.2 4.1
g2   5.7 8.3 2.0
g3   6.2 3.1 9.2
g4   7.2 9.1 0.6

Parameters

-e is the expression file

-d is an optional expression file for the targets (meaning you can specify an expression file for tfs, and one for targets, with the same sample names. This is for aracne plus)

-t is the TF list

-o is the output folder

--consolidate is telling java to run aracne in consolidate mode (that is, you point it to a directory with bootstraps, and they will be consolidated)

--calculateThreshold is telling Java to run it in threshold mode

-p is the p-value threshold for the MI to be significant (1E-8 usually)

--consolidatepvalue is the p-value threshold for the Poisson test of edge significance in multi-bootstrap mode (if omitted, it is set to 0.05

-s is the optional seed, to make the threshold mode and the bootstrap reproducible

--threads is the number of threads (it is used only in standard mode, i.e. bootstrap)

--nodpi tells ARACNE not to run DPI

--nobootstrap tells ARACNE not to do bootstrapping

--nobonferroni removes the Bonferroni correction

Examples

Note: the examples have been written based on the provided test sets: test/matrix.txt (the gene expression matrix) and test/tfs.txt (the list of regulators). Also, example 3 (the running of 100 bootstraps) is written using a “for loop” as a useful method to run 100 bootstraps with a controlled seed.

Example 1: calculate threshold with a fixed seed

java -Xmx5G -jar aracne.jar -e test/matrix.txt  -o outputFolder --tfs test/tfs.txt --pvalue 1E-8 --seed 1 \
--calculateThreshold

Example 2: run ARACNe on a single bootstrap

java -Xmx5G -jar aracne.jar -e test/matrix.txt  -o outputFolder --tfs test/tfs.txt --pvalue 1E-8 --seed 1

Example 3: run 100 reproducible bootstraps

UNIX loop

for i in {1..100}
do
java -Xmx5G -jar aracne.jar -e test/matrix.txt  -o outputFolder --tfs test/tfs.txt --pvalue 1E-8 --seed $i
done

Windows loop

for /l %i in (1, 1, 100) do java -Xmx5G -jar aracne.jar -e test/matrix.txt  -o outputFolder --tfs test/tfs.txt \
--pvalue 1E-8 --seed %i

Example 4: consolidate bootstraps in the output folder

java -Xmx5G -jar Aracne.jar -o outputFolder --consolidate

Example 5: run a single ARACNE with no bootstrap and no DPI

java -Xmx5G -jar Aracne.jar -e test/matrix.txt  -o outputFolder --tfs test/tfs.txt --pvalue 1E-8 --seed 1 \
--nobootstrap --noDPI

Example 6: consolidate bootstraps without Bonferroni correction

java -Xmx5G -jar Aracne.jar -o outputFolder --consolidate --nobonferroni