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Time course measurement of single molecules on a cell surface providesdetailed information on the dynamics of the molecules, which is otherwiseinaccessible. To extract the quantitative information, single particle tracking(SPT) is typically performed. However, trajectories extracted by SPT inevitablyhave linking errors when the diffusion speed of single molecules is highcompared to the scale of the particle density. To circumvent this problem wedeveloped an algorithm to estimate diffusion constants without relying on SPT.We demonstrated that the proposed algorithm provides reasonable estimation ofdiffusion constants even when other methods fail due to high particle densityor inhomogeneous particle distribution. In addition, our algorithm can be usedfor visualization of time course data from single molecular measurements.

Probabilistic Nearest Neighbor Estimation of Diffusion Constants from Single Molecular Measurement without Explicit Tracking