Classification by bootstrapping in single particle methods

In single-particle reconstruction methods, projections of macromolecules at random orientations are collected. Often, several classes of conformations or binding states coexist in a biological sample, which requires classification, so that each conformation can be reconstructed separately. In this work, we examine bootstrap techniques for classifying the projection data. When these techniques are applied to variance estimation, the projection images (particles) are randomly sampled with replacement from the data set and a bootstrap volume is reconstructed from each sample. In a recent extension of the bootstrap technique to classification, each particle is assigned to a volume in the space spanned by the bootstrap volumes, such that the projection of the assigned volume best matches the particle. In this work we explain the rationale of these techniques by discussing the nature of the bootstrap volumes and provide some statistical analyses.