Figure 6. Partial correlation analysis.

(a) Model for partial correlation analysis using three variables, X, Y, and Z, which are all connected by pair-wise relationships (solid lines, left). Correlations describing specifically the relationships between X and Y, as well as X and Z (middle). The contribution of X to both Y and Z is then subtracted from each (grey arrows), and the relationship between the residual Y and Z is calculated, giving the partial correlation (red line, right). (b-g) Comparison between total and partial correlations for all combinations of Asef, Rho GTPase, and edge velocity (total correlation, black line; partial correlation, red line ((n = cells, m = windows); Cdc42 (n=5, m=729); Rac1, (n=6, m=719)). Edge to GTPase correlations, controlled for RhoGEF input (b, c). GTPase to RhoGEF correlations, controlled for edge input (d, e). Edge to RhoGEF correlations, controlled for GTPase input (f, g). Dotted lines show the correlation coefficient above which the coupling between two variables is considered significant with 95% confidence. This depends on the number of windows (see Methods). Shading represents 95% C.I. about the mean correlation computed from m windows. The width of this interval depends on the consistency of the correlations across windows and cells (see Methods). (h) Spatiotemporal integration of Asef-, Cdc42- and Rac1- signaling in the control of constitutive cell protrusion-retraction cycles. Asef directly activates Cdc42 to control ~30% of the effect of Cdc42 on edge motion. Asef also contributes indirectly to the activation of Rac1, but this contribution does not play a role in controlling the edge. Instead, other GEFs must be responsible for the motion-relevant signaling of Rac1, which is stronger than Cdc42’s contribution. The relative localizations of each protein are shown. Note that activation of Asef is tightly localized compared to Rac1 and Cdc42, supporting localized GTPase activation by Asef followed by GTPase diffusion or transport to the edge.