Table 1.
Network inference methods.
| ID | Synopsis | Reference | |
|---|---|---|---|
| Regression: Transcription factors are selected by target gene specific (1) sparse linear regression and (2) data resampling approaches. | |||
| 1 | Trustful Inference of Gene REgulation using Stability Selection (TIGRESS): (1) Lasso; (2) the regularization parameter selects five transcription factors per target gene in each bootstrap sample. | 33a | |
| 2 | (1) Steady state and time series data are combined by group lasso; (2) bootstrapping. | 34a | |
| 3 | Combination of lasso and Bayesian linear regression models learned using Reversible Jump Markov Chain Monte Carlo simulations. | 35a | |
| 4 | (1) Lasso; (2) bootstrapping. | 36 | |
| 5 | (1) Lasso; (2) area under the stability selection curve. | 36 | |
| 6 | Application of the Lasso toolbox GENLAB using standard parameters. | 37 | |
| 7 | Lasso models are combined by the maximum regularization parameter selecting a given edge for the first time. | 36a | |
| 8 | Linear regression determines the contribution of transcription factors to the expression of target genes. | —a,b | |
| Mutual Information: Edges are (1) ranked based on variants of mutual information and (2) filtered for causal relationships. | |||
| 1 | Context likelihood of relatedness (CLR): (1) Spline estimation of mutual information; (2) the likelihood of each mutual information score is computed based on its local network context. | 11a,b | |
| 2 | (1) Mutual information is computed from discretized expression values. | 38a,b | |
| 3 | Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE): (1) kernel estimation of mutual information; (2) the data processing inequality is used to identify direct interactions. | 9a,b | |
| 4 | (1) Fast kernel-based estimation of mutual information; (2) Bayesian Local Causal Discovery (BLCD) and Markov blanket (HITON-PC) algorithm to identify direct interactions. | 39a | |
| 5 | (1) Mutual information and Pearson’s correlation are combined; (2) BLCD and HITON-PC algorithm. | 39a | |
| Correlation: Edges are ranked based on variants of correlation. | |||
| 1 | Absolute value of Pearson’s correlation coefficient. | 38 | |
| 2 | Signed value of Pearson’s correlation coefficient. | 38a,b | |
| 3 | Signed value of Spearman’s correlation coefficient. | 38a,b | |
| Bayesian networks optimize posterior probabilities by different heuristic searches. | |||
| 1 | Simulated annealing (catnet R package, http://cran.r-project.org/web/packages/catnet), aggregation of three runs. | — | |
| 2 | Simulated annealing (catnet R package, http://cran.r-project.org/web/packages/catnet). | — | |
| 3 | Max-Min Parent and Children algorithm (MMPC), bootstrapped datasets. | 40 | |
| 4 | Markov blanket algorithm (HITON-PC), bootstrapped datasets. | 41 | |
| 5 | Markov boundary induction algorithm (TIE*), bootstrapped datasets. | 42 | |
| 6 | Models transcription factor perturbation data and time series using dynamic Bayesian networks (Infer.NET toolbox, http://research.microsoft.com/infernet). | —a | |
| Other Approaches: Network inference by heterogeneous and novel methods. | |||
| 1 | Genie3: A random forest is trained to predict target gene expression. Putative transcription factors are selected as tree nodes if they consistently reduce the variance of the target. | 19a | |
| 2 | Co-dependencies between transcription factors and target genes are detected by the non-linear correlation coefficient η2 (two-way ANOVA). Transcription factor perturbation data are up-weighted. | 20a | |
| 3 | Transcription factors are selected maximizing the conditional entropy for target genes, which are represented as Boolean vectors with probabilities to avoid discretization. | 43a | |
| 4 | Transcription factors are preselected from transcription factor perturbation data or by Pearson’s correlation and then tested by iterative Bayesian Model Averaging (BMA). | 44 | |
| 5 | A Gaussian noise model is used to estimate if the expression of a target gene changes in transcription factor perturbation measurements. | 45 | |
| 6 | After scaling, target genes are clustered by Pearson’s correlation. A neural network is trained (genetic algorithm) and parameterized (back-propagation). | 46a | |
| 7 | Data is discretized by Gaussian mixture models and clustering (Ckmeans); Interactions are detected by generalized logical network modeling (χ2 test). | 47a | |
| 8 | The χ2 test is applied to evaluate the probability of a shift in transcription factor and target gene expression in transcription factor perturbation experiments. | 47a | |
| Meta predictors (1) apply multiple inference approaches and (2) compute aggregate scores. | |||
| 1 | (1) Z-scores for target genes in transcription factor knockout data, time-lagged CLR for time series, and linear ordinary differential equation models constrained by lasso (Inferelator); (2) resampling approach. | 48a | |
| 2 | (1) Pearson’s correlation, mutual information, and CLR; (2) rank average. | — | |
| 3 | (1) Calculates target gene responses in transcription factor knockout data, applies full-order, partial correlation and transcription factor-target co-deviation analysis; (2) weighted average with weights trained on simulated data. | —a | |
| 4 | (1) CLR filtered by negative Pearson’s correlation, least angle regression (LARS) of time series, and transcription factor perturbation data; (2) combination by z-scores. | 49 | |
| 5 | (1) Pearson’s correlation, differential expression (limma), and time series analysis (maSigPro); (2) Naïve Bayes. | —a | |
Methods have been manually categorized based on participant-supplied descriptions. Within each class, methods are sorted by overall performance (see Figure 2a). Note that generic references have been used if more specific ones were not available.
a
Detailed method description included in Supplementary Note 10;
b
Off-the-shelf algorithm applied by challenge organizers.