Abstract
Background A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past twenty years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization.
Results In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoögbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods.
Conclusions Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction - which combines CMIA, and the KSG-MI estimator - achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or choose gene candidates for experimental validations.
Competing Interest Statement
The authors have declared no competing interest.
List of abbreviations
- GRN
- Gene regulatory network
- ODE
- Ordinary differential equations
- MI
- Mutual information
- Probability density functions
- FB
- Fixed (width) binning
- AP
- Adaptive partitioning
- kNN
- k-nearest neighbor
- KDE
- Kernel density estimator
- CLR
- Context likelihood of relatedness
- CMIA
- Conditional mutual information augmentation
- KSG
- Kraskov-Stoögbauer-Grassberger
- RL
- Relevance networks
- ARACNE
- Algorithm for the Reconstruction of Accurate Cellular Networks
- SA-CLR
- Synergy-Augmented CLR
- ML
- Maximum likelihood
- MM
- Miller-Madow
- KL
- Kozachenko-Leonenko
- TC
- Total correlation
- MI3
- Three-way MI
- II
- Interaction information
- CMI
- Conditional mutual information
- DREAM
- Dialogue for reverse engineering assessments and methods
- AUPR
- Area under precision-recall curve
- CMI2rt
- Luo et al. inference algorithm named MI3
- DPI
- Data Processing Inequality