Extended Data Fig. 8: Analysis of LPS and PAM-treated macrophages.
(a–c) Receiver operating characteristic (ROC) curves of 5-fold cross-validation of a logistic regression classifier that is trained on (a) 5-minute data, (b) 2-hour data, and (c) 12-hour data. The black dashed lines in (a–c) indicate random classification. (d) Violin plots showing the log-transformed count of the top three PLA products of the logistic regression model that is trained on 2-hour data. P-values are calculated using two-sided Welch’s t-test (n = 31, 32, 32, and 36 single cells for the control, LPS, PAM, and both treatment groups, respectively). (e) Schematic showing how the logistic regression classifier is used to predict response (LPS-like, PAM-liked, and mixed) in cells treated with both LPS and PAM after 2 h. (f) Bar plot showing the proportion of LPS/PAM-treated cells that show LPS-like, PAM-like and mixed response. n indicates the number of cells in each response group. (g) Violin plots showing the log-transformed count of the top logistic regression coefficients (Fig. 6b) in each predicted response group for cells treated with both ligands. (h, i) Heatmaps showing (h) the relative PLA product levels and (i) the relative protein levels of the LPS-like, PAM-like and mixed response groups. The PLA product (or protein) counts are log-transformed, then averaged by response group, and finally standardized. Hierarchical clustering is performed on the PLA products (or proteins) and response groups using Euclidean distance and complete linkage. (j) ROC curves of a logistic regression classifier trained on protein levels from different time points. The classifier is trained to predict whether a single cell was stimulated with LPS or PAM. Each ROC curve represents the mean ROC curve from 5-fold cross-validation. The area under the curve (AUC) metric of each time point is presented as mean ± s.d. of the AUC metrics from 5-fold cross-validation for that particular time point.
