Extended Data Fig. 9: CryoDRGN’s learned latent space embeddings exhibit undesirable correlations with tilt image index.
From: Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN
(a) Two cryoDRGN models were tested on the unfiltered particle stack of Mycoplasma pneumoniae ribosomes from Fig. 5a. The latent space is shown as a KDE plot following UMAP dimensionality reduction. The latent embeddings were binned by the tilt image index, and the median value across each bin is annotated. (b) KDEs from panel A replotted after binning by tilt image index quartiles. (c) KDEs from panel A with annotated positions corresponding to three representative particles evaluated using their 5th, 15th, 25th, or 35th tilt images. (d) Volumes generated from cryoDRGN using the latent embeddings highlighted in panel C.
