2SDR: Applying Kronecker Envelope PCA to denoise Cryo-EM Images

by   Szu-Chi Chung, et al.

Principal component analysis (PCA) is arguably the most widely used dimension reduction method for vector type data. When applied to image data, PCA demands the images to be portrayed as vectors. The resulting computation is heavy because it will solve an eigenvalue problem of a huge covariance matrix due to the vectorization step. To mitigate the computation burden, multilinear PCA (MPCA) that generates each basis vector using a column vector and a row vector with a Kronecker product was introduced, for which the success was demonstrated on face image sets. However, when we apply MPCA on the cryo-electron microscopy (cryo-EM) particle images, the results are not satisfactory when compared with PCA. On the other hand, to compare the reduced spaces as well as the number of parameters of MPCA and PCA, Kronecker Envelope PCA (KEPCA) was proposed to provide a PCA-like basis from MPCA. Here, we apply KEPCA to denoise cryo-EM images through a two-stage dimension reduction (2SDR) algorithm. 2SDR first applies MPCA to extract the projection scores and then applies PCA on these scores to further reduce the dimension. 2SDR has two benefits that it inherits the computation advantage of MPCA and its projection scores are uncorrelated as those of PCA. Testing with three cryo-EM benchmark experimental datasets shows that 2SDR performs better than MPCA and PCA alone in terms of the computation efficiency and denoising quality. Remarkably, the denoised particles boxed out from the 2SDR-denoised micrographs allow subsequent structural analysis to reach a high-quality 3D density map. This demonstrates that the high resolution information can be well preserved through this 2SDR denoising strategy.


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