Figure 3

SVMs combined with a logistic equation provides quantitative LiD score corresponding to the probability of a MDD diagnosis. A logistic equation was fit on the boundary inferred by the linear SVM from the 10 most predictive transcripts. For illustrative purposes, we show the same method on the two most predictive pairwise genes, DGKA and CDR2; the full predictive model uses 10 transcripts and would be impractical to visualize. The thick line corresponds to the logistic regression inflection point and the thin lines correspond to deciles of probability of a MDD diagnosis fitted from logistic regression. The LiD score range is shown for each region. The overlaps in transcript measurements between MDD and ND control subjects highlights the inherent noise in MDD diagnoses as well as biological experiments. However, the probabilistic interpretation from logistic regression offers a diagnostic tool useful for clinicians. LiD, likelihood of depression; MDD, major depressive disorder; ND, no-disorder; SVM, support vector machine.