Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens
Fig 5
Multi-task Group LASSO (MTG-LASSO) structured-sparsity approach for integration of protein data with expression-based regulatory module networks.
A. Illustration of MTG-LASSO framework for predicting protein regulators for one module (Methods). Horizontal separations in the X and Y data boxes represent different virus time course. Rows of X and Y represent time points. Columns of X correspond to proteins and columns of Y correspond to mRNA levels of genes in a module. B. Comparison of number of nonzero regression weights identified by MTG-LASSO and LASSO. Each dot (MTG-LASSO) or plus sign (LASSO) represents the number of non-zero regression weights for one setting of λ (the sparsity parameter) for one module. Number of nonzero weights is averaged over 10 folds of cross-validation. C. Comparison of cross-validation predictive quality between MTG-LASSO and LASSO. Results are shown for λ = {0.10, 0.75}; results for other settings are in S4 Fig. In each scatterplot, there is one point per module. Left two plots compare methods based on Pearson correlation (ρ) of predicted to actual expression values; right plots compare on the basis of root mean squared error (RMSE). Inset ρ gives Pearson correlation between MTG-LASSO and LASSO scores. Diagonal line is shown for comparison. D/E. Examples of curves used to select λ for individual modules; human (D) and mouse (E). Y-axis gives Pearson correlation (cross-validation predictive quality); X-axis gives the average number of nonzero regression weights for that module; this value is higher than the final number of high-confidence, high-weight regulators. Stars indicate the chosen value of λ for the example modules.