External Embeddings

You can generate volumes with RECOVAR's kernel regression from a latent space produced by another method (e.g. cryoDRGN). Pass the external embedding and a set of target coordinates, and RECOVAR reconstructs the volumes at those points.

Usage

recovar reconstruct_from_external_embedding particles.star \
    --poses poses.pkl --ctf ctf.pkl \
    -o external_output \
    --embedding z.pkl \
    --target coords.txt

Arguments

Flag	Default	Description
particles	Required	Input particles (.mrcs, .star, .cs, or .txt)
--poses	Required	Poses file (.pkl)
--ctf	Required	CTF parameters (.pkl)
-o, --outdir	Required	Output directory
--embedding	Required	External latent coordinates (.pkl, shape N x zdim)
--target	Required	Points at which to generate volumes (.txt)
--Bfactor	0	B-factor sharpening
--n-bins	50	Bins for kernel regression
--zdim1	False	Enable for 1D latent space
--tilt-series	False	Use tilt-series data

Poses and CTF must be .pkl files

Unlike recovar pipeline, this command does not auto-extract poses and CTF from a .star/.cs file. Even when particles is a .star or .cs, you must pass --poses and --ctf as separate .pkl files (for example, the poses.pkl / ctf.pkl that cryoDRGN writes during preprocessing).

Example: using cryoDRGN embeddings

1. Run cryoDRGN to get latent coordinates (z.pkl)
2. Pick target points (e.g., k-means centers): np.savetxt("coords.txt", centers)
3. Generate volumes using cryoDRGN's latent space with RECOVAR's kernel regression:

recovar reconstruct_from_external_embedding particles.mrcs \
    --poses poses.pkl --ctf ctf.pkl \
    -o cryodrgn_recovar \
    --embedding z.24.pkl \
    --target coords.txt --Bfactor=50

The volumes are reconstructed by kernel regression at cryoDRGN's latent coordinates.