Statistical evaluation of a bottom-up clustering for single particle molecular images.

Abstract

We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signal-to-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method was found to be fairly robust against noise. Clustering tests of four known protein structures were performed at three levels of noise and with three levels of smoothing. A significant effect of smoothing was confirmed in our results for images with noise suggesting an effective degree of smoothing depending on the noise and structural features of the target molecule. The consistency of clustering results was evaluated by the average solid angle of projection, and the precision of our clustering results was checked by the average image correlation between the obtained cluster image and the true projection. Once image features are extracted appropriately, the average solid angle also represents the degree of clustering precision.