AlignPCA-2D: PCA-Reduced Euclidean Vector Alignment for 2D Classification in Cryo-EM

Cryogenic Electron Microscopy (cryo-EM) has transformed structural biology by enabling high-resolution reconstruction of macromolecular complexes from noisy projection images. However, the intrinsic heterogeneity and low signal-to-noise ratio of cryo-EM datasets make 2D classification a critical and computationally demanding step in the processing work-flow. Here, we introduce AlignPCA-2D, a PCA-space Euclidean vector alignment method for fast, interpretable 2D classification in cryo-EM. By projecting particle images and class representations into a compressed latent PCA space, AlignPCA-2D reduces data dimensionality while pre-serving meaningful structural variability. The image-to-class assignment is then performed using Euclidean distance, enabling efficient and accurate classification. We benchmark AlignPCA-2D against established cryo-EM software, such as RELION and cryoSPARC, and demonstrate that it achieves competitive alignment accuracy while substantially reducing computational cost. This approach provides a lightweight alternative for large-scale 2D classification tasks, and its modular design makes it compatible with existing cryo-EM processing pipelines. O_TBL View this table: org.highwire.dtl.DTLVardef@1e384org.highwire.dtl.DTLVardef@2474org.highwire.dtl.DTLVardef@15966c1org.highwire.dtl.DTLVardef@6934adorg.highwire.dtl.DTLVardef@1017b4e_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONParticle retention and overlap among 2D classification methods C_TABLECAPTION C_TBL