and applied SFA to these transformed data. After experimenting with different numbers of factors we chose eleven factors as providing the most reproducible and interpretable results, yielding an 11 by 381 matrix of measurements. Of the 11 factors identified, one was strongly correlated with age, and so we disregarded that factor for all subsequent analysis. Each of the remaining factors (F1–F10, Figure S2) captured distinct, and generally interpretable, features of the data. For example, F1 almost exclusively consists of measurements of response from the 10 mg session, and so we refer to this factor as “10 mg response”. Factor F2 is primarily a function of baseline measures of positive affect from all three sessions (e.g., positive loadings on Friendliness, Elation, and Vigor; negative loadings on Anxiety and Confusion), and we refer to this factor as “positive affect at baseline” (Table S3). Although some factors might be considered better candidates for being influenced by genetic factors than others, we conducted association mapping on all 10 factors since a non-genetic factor should produce null results. The interpretability of factors obtained using SFA compared favorably to alternative dimension reduction techniques such as PCA (see Supporting Information S1 for further discussion).
