Unexpected links reflect the noise in networks
Anatoly Yambartsev1, Michael A Perlin2, Yevgeniy Kovchegov3
1Department of Statistics, Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo, SP, Brazil.
Biology Direct
|October 15, 2016
Summary
This study introduces a novel statistical method to improve gene regulatory network reconstruction. Our approach effectively identifies and removes approximately half of all erroneous edges, enhancing network accuracy.
Area of Science:
- Systems Biology
- Bioinformatics
- Computational Biology
Background:
- Gene covariation networks are crucial for studying biological processes.
- Inferring these networks from observational data is difficult due to high dimensionality and limited replicates.
Purpose of the Study:
- To develop a new statistical method for estimating erroneous edges in gene regulatory networks.
- To enhance existing network inference approaches.
Main Methods:
- Utilized a relationship between correlation sign and gene regulation directionality.
- Applied Bayesian networks and positive correlation inequalities for mathematical foundation.
- Developed a method to identify and remove erroneous network edges.
Main Results:
- The proposed method estimates and removes about half of erroneous edges.
- Results show a strong correlation with False Discovery Rate (FDR) on biological datasets.
- Simulation analysis indicates superior network error estimation compared to FDR.
Conclusions:
- The study presents a robust approach for improving gene covariation network reconstruction.
- The method offers a significant advancement in network inference accuracy.
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