DeMAND R-system package

This is an R-system package implementation of the DeMAND (Detecting Mechanism of Action based on Network Dysregulation) algorithm.

Download software

Download DeMAND version from Bioconductor or GitHub.

Installation

Please install DeMAND according to the instructions on Bioconductor.

Getting started

To run DeMAND using the data provided the DeMAND package, please see the followings:

> library(DeMAND)

> data(inputExample)

> dobj <- demandClass(exp=bcellExp, anno=bcellAnno, network=bcellNetwork)

> printDeMAND(dobj)

> dobj <- runDeMAND(dobj, fgIndex=caseIndex, bgIndex=controlIndex)

> printDeMAND(dobj)

Networks and descriptions

To run DeMAND, the users need a molecular interaction network. Here we provide all the networks used in the reference paper. The users can download the following network to run DeMAND. The users may want to use their own networks to run DeMAND.

*PDI: Transcription Factor (protein) – target (DNA) interaction

**PPI: Protein – Protein interaction

Context specific networks

The context specific information for these networks was obtained from GEPs derived from the same context whereas context independent information is obtained from large number of experimental and computational evidences. Finally, we used Naïve Bayes Classifier to integrate various evidences for an interaction to obtain final interactome. A detailed description on how to generate interactome is available in Lefebvre et al.

[NEED TO RESOLVE LINKS IN FOLLOWING BULLET LIST.]

• Bcell-U95av2 To generate U95av2 human B-cell interactome we used 254 B cell lymphoma gene expression profiles obtained from Mani et al.

• Bcell-U133p2 To generate U133p2 human B-cell interactome we used 226 B cell lymphoma gene expression profiles obtained from Lefebvre et al.

• BRCA-MCF7 To generate the breast cancer network, we used gene expression profiles of 448 breast cancer patients, which were obtained from CMAP2 dataset (Lamb et al). Sample selection was done to remove redundancy and quality control. More details are available in Woo et al.

Context-free network

Context independent protein-protein interactions were used to evaluate the effectiveness of using context-specific network for DeMAND (Woo et al).

• STRING To generate the context-free network, we downloaded protein-protein interactions, experimentally validated only (Franceschini et al).

References

Citation

Woo JH, Shimoni Y, Yang WS, Subramaniam P, Iyer A, Nicoletti P, Martinez MR, Lopez G, Mattioli M, Realubit R, Karan C, Stockwell BR, Bansal M, Califano A. Elucidating Compound Mechanism of Action by Network Perturbation Analysis. Cell. 2015 Jul 16;162(2):441-51.

Additional references

Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, Wang K, Sumazin P, Kustagi M, Bisikirska BC, Basso K, Beltrao P, Krogan N, Gautier J, Dalla-Favera R, Califano A. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol. 2010 Jun 8;6:377.

Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, Dalla-Favera R, Califano A. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol Syst Biol. 2008;4:169.

Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006 Sep 29;313(5795):1929-35.

Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15.

Contact

Author & Maintainer: Jung Hoon Woo (Email: jw2853@columbia.edu)