Fig. 7

Multi-task group LASSO (MTG-LASSO) to predict regulators of transitioning genes. A MTG-LASSO was applied to infer significant regulatory features for each transitioning gene set. Shown is a model of predicting expression (Y) for a set of genes using the predictor features (X) of the genes and coefficients (B). Each gene (column of Y) is a task and each row of B corresponds to the regression of a predictor for all genes. MTG-LASSO picks the same regulators for all genes in a set but allows for different regression weights. The regression weights for a regulator (row) is a group. B Visualization of the top 1000 predicted TF-gene network edges, ranked by regression weight magnitude from MTG-LASSO. C Example transitioning gene sets showing corresponding gene expression and motif accessibility profiles for regulators of interest (IBM1, MTF1, EIN3, EDN3). For each cluster, we show genes with significant change in accessibility between 0–2 and 4–24 h (T-test P-value < 0.05) for at least one regulatory feature per cluster. D Ranking of all regulators selected in the MTG-LASSO-based regulatory network. Regulators are ranked by the number of predicted targets. The motifs that were mapped to a common name are shown. The ranking highlights regulators identified at the DRMN module level (Fig. 6) and additional regulators like TIFY and CYCLOPS