Abstract
Predictions of physical and functional links between cellular components are often based on correlations between experimental measurements, such as gene expression. However, correlations are affected by both direct and indirect paths, confounding our ability to identify true pairwise interactions. Here we exploit the fundamental properties of dynamical correlations in networks to develop a method to silence indirect effects. The method receives as input the observed correlations between node pairs and uses a matrix transformation to turn the correlation matrix into a highly discriminative silenced matrix, which enhances only the terms associated with direct causal links. Against empirical data for Escherichia coli regulatory interactions, the method enhanced the discriminative power of the correlations by twofold, yielding ≥50% predictive improvement over traditional correlation measures and 6% over mutual information. Overall this silencing method will help translate the abundant correlation data into insights about a system's interactions, with applications ranging from link prediction to inferring the dynamical mechanisms governing biological networks.
| Original language | English |
|---|---|
| Pages (from-to) | 720-725 |
| Number of pages | 6 |
| Journal | Nature Biotechnology |
| Volume | 31 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2013 |
| Externally published | Yes |
Bibliographical note
Funding Information:We thank B. Alipanahi and B. Frey for their valuable insights, A. Sharma, F. Simini, J. Menche, S. Rabello, G. Ghoshal, Y.-Y. Liu, T. Jia, M. Pósfai, C. Song, Y.-Y. Ahn, N. Blumm, D. Wang, Z. Qu, M. Schich, D. Ghiassian, S. Gil, P. Hövel, J. Gao, M. Kitsak, M. Martino, R. Sinatra, G. Tsekenis, L. Chi, B. Gabriel, Q. Jin and Y. Li for discussions, and S.S. Aleva, S. Morrison, J. De Nicolo and A. Pawling for their support. This work was supported by the US National Institutes of Health (NIH), Center of Excellence of Genomic Science (CEGS), Grant number NIH CEGS 1P50HG4233; and the NIH, award number 1U01HL108630-01; DARPA Grant Number 11645021; DARPA Social Media in Strategic Communications project under agreement number W911NF-12-C-0028; the Network Science Collaborative Technology Alliance sponsored by the US Army Research Laboratory under agreement number NS-CTA W911NF-09-02-0053; the Office of Naval Research under agreement number N000141010968; and the Defense Threat Reduction Agency awards WMD BRBAA07-J-2-0035 and BRBAA08-Per4-C-2-0033.
Funding
We thank B. Alipanahi and B. Frey for their valuable insights, A. Sharma, F. Simini, J. Menche, S. Rabello, G. Ghoshal, Y.-Y. Liu, T. Jia, M. Pósfai, C. Song, Y.-Y. Ahn, N. Blumm, D. Wang, Z. Qu, M. Schich, D. Ghiassian, S. Gil, P. Hövel, J. Gao, M. Kitsak, M. Martino, R. Sinatra, G. Tsekenis, L. Chi, B. Gabriel, Q. Jin and Y. Li for discussions, and S.S. Aleva, S. Morrison, J. De Nicolo and A. Pawling for their support. This work was supported by the US National Institutes of Health (NIH), Center of Excellence of Genomic Science (CEGS), Grant number NIH CEGS 1P50HG4233; and the NIH, award number 1U01HL108630-01; DARPA Grant Number 11645021; DARPA Social Media in Strategic Communications project under agreement number W911NF-12-C-0028; the Network Science Collaborative Technology Alliance sponsored by the US Army Research Laboratory under agreement number NS-CTA W911NF-09-02-0053; the Office of Naval Research under agreement number N000141010968; and the Defense Threat Reduction Agency awards WMD BRBAA07-J-2-0035 and BRBAA08-Per4-C-2-0033.
| Funders | Funder number |
|---|---|
| US National Institutes of Health | |
| National Institutes of Health | 1U01HL108630-01 |
| Office of Naval Research | N000141010968 |
| National Human Genome Research Institute | P50HG004233 |
| Defense Advanced Research Projects Agency | W911NF-12-C-0028, 11645021 |
| Defense Threat Reduction Agency | BRBAA08-Per4-C-2-0033, WMD BRBAA07-J-2-0035 |
| Army Research Laboratory | NS-CTA W911NF-09-02-0053 |