Fig. 2

a Temporal-longitudinal scenario: Time in seconds required by glmmLasso and SES equipped with different conditional independence tests on the GSD5088 dataset. The number of randomly selected predictors is reported on the x-axis, while y-axis reports the required computational time: glmmLasso rapidly becomes computationally more expensive than any SES variant. b Gene expression over time for the target gene CSHL1 in dataset GDS5088 (one line for each subject). c Average relative change for the target gene and predictors reported in model 10. The expression of the genes was averaged over subjects for each time point, and the logarithm of the change with respect to the first time point was then computed. The target gene appears as bold line, whereas the 5 predictor genes are reported as dashed lines. d Differences in performance between SESglmm and glmmLasso for the 20 replications on each dataset. Negative values indicate SESglmm outperforming glmmLasso; SESglmm is always comparable or better than glmmLasso, especially in dataset GDS5088 (excluded for sake of clarity). e Static-longitudinal scenario: Expressions over time of gene TSIX, selected by SES for dataset GDS4146. The plot show one line for each subject: there is a clear separation between the two classes included in the dataset (dashed and solid lines, respectively). f Static-distinct scenario: Expressions over time of gene Ppp1r42, selected by SES for dataset GDS2882. The dotted and dashed lines correspond to the average trend of the gene in two different classes; differences in intercept and trend are easily noticeable