ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

doi:10.1186/1471-2105-7-S1-S7

BMC Bioinformatics

Volume 7
Suppl 1

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Margolin AA
Nemenman I
Basso K
Wiggins C
Stolovitzky G
Favera RD
Califano A

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Margolin AA
Nemenman I
Basso K
Wiggins C
Stolovitzky G
Favera RD
Califano A
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This article is part of the supplement: NIPS workshop on New Problems and Methods in Computational Biology .

Proceedings

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Adam A Margolin¹^,2 , Ilya Nemenman² , Katia Basso³ , Chris Wiggins²^,4 , Gustavo Stolovitzky⁵ , Riccardo Dalla Favera³ and Andrea Califano¹^,2

¹Department of Biomedical Informatics, Columbia University, New York, NY 10032

²Joint Centers for Systems Biology, Columbia University, New York, NY 10032

³Institute for Cancer Genetics, Columbia University, New York, NY 10032

⁴Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10032

⁵IBM T.J. Watson Research Center, Yorktown Heights, NY 10598

author email corresponding author email

BMC Bioinformatics 2006, 7(Suppl 1):S7doi:10.1186/1471-2105-7-S1-S7


Published:	20 March 2006

Additional files

Additional File 1:

Determination of mutual information statistical significance. P-values are assigned to MI thresholds using a Monte Carlo simulation for different kernel widths, sample sizes (M) and for 10⁵gene pairs so that reliable estimates are produced up to p = 10^-4(solid lines). Extrapolation to smaller p-values is done using (dotted lines).

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Additional File 2:

Prediction errors as a function of DPI tolerance. The number of inferred errors, N_FP+ N_FN, are plotted as a function of the DPI tolerance, τ, for (a) the Erdös-Rényi and (b) the scale-free topologies. Raising τ to a value of 0.2 results in a modest increase in false positives, while larger values of τ produce a much sharper increase. Therefore, a moderate choice for the tolerance can help elucidate additional interactions without introducing an excessive number of false positives. Results are calculated for a statistical significance threshold of 10^-4and a synthetic microarray size of 1,000.

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Additional File 3:

MI distribution for different shortest path lengths for the Erdös-Rényi topology. Red and black arrows are explained in the legend of Figure 5. Since there are no large in-degree hubs, decorrelation is slower than for the scale-free network, and MI statistics even for fifth neighbors is still distinguishable from the background.

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