# cryoNeFEN
**Repository Path**: beatxx/cryoNeFEN
## Basic Information
- **Project Name**: cryoNeFEN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-30
- **Last Updated**: 2025-04-30
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# CryoNeFEN: High-resolution real-space reconstruction of cryo-EM structures using a neural field network
CryoNeFEN is a neural network based algorithm for cryo-EM reconstruction. In particular, the method models an isotropic representation of 3D structures using neural fields in the spatial domain.
# Installation:
```
# clone the repo.
git clone https://github.com/yuehuang2023/cryoNeFEN.git
cd CryoNeFEN
# Make a conda environment.
conda create -n cryonefen python=3.9 pytorch==1.13.0 torchvision==0.14.0 pytorch-cuda=11.6 -c pytorch -c nvidia
conda activate cryonefen
# Install required packages
conda install matplotlib
pip install starfile mrcfile scipy
```
Note: Please ensure that you have met the prerequisites for [PyTorch](https://pytorch.org/), the code is tested with pytorch version 1.13, 2.0, and 2.1.
Dependencies (click to expand)
- pytorch
- starfile
- mrcfile
- matplotlib
- scipy
# Quickstart: cryo-EM reconstruction
## 1. Data preprocessing
Perform a **homogeneous refinement** in cryoSPARC software. We will use the poses and CTF parameters from this "consensus reconstruction".
- In cryoSPARC, 1) import the particles, 2) run an ab initio reconstruction job, and 3) run a homogeneous refinement job, all with default parameters.
- CryoNeFEN extracts image poses from a `.cs` file directly. Copy the path of cryoSPARC's metadata file (`.cs` file) that contains particle poses and CTF parameters.
## 2. CryoNeFEN training
When the input image stack (`.cs` file) has been prepared, a cryoNeFEN model can be trained with `python train.py`:
python train.py -h
usage: train.py [-h] -o OUTDIR [--poses POSES] [--ctf pkl] [--mask mrc] [--split {1,2}] [--load WEIGHTS.PKL] [--checkpoint CHECKPOINT] [--log-interval LOG_INTERVAL] [--seed SEED] [--uninvert-data] [--no-window]
[--window-r WINDOW_R] [--ind IND] [--lazy] [--datadir DATADIR] [-n NUM_EPOCHS] [-b BATCH_SIZE] [--wd WD] [--lr LR] [--norm NORM NORM] [--layers LAYERS] [--dim DIM] [--l-extent L_EXTENT]
[--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,gaussian,none}] [--pe-dim PE_DIM] [--activation {relu,leaky_relu}]
particles
positional arguments:
particles Input particles (.mrcs, .star, .cs, or .txt)
optional arguments:
-h, --help show this help message and exit
-o OUTDIR, --outdir OUTDIR
Output directory to save model
--poses POSES Image poses (.pkl)
--ctf pkl CTF parameters (.pkl)
--mask mrc Optional mask (.mrc, default: sphere mask)
--split {1,2} Split dataset for computing GSFSC
--load WEIGHTS.PKL Initialize training from a checkpoint
--checkpoint CHECKPOINT
Checkpointing interval in N_EPOCHS (default: 1)
--log-interval LOG_INTERVAL
Logging interval in N_IMGS (default: 100)
--seed SEED Random seed
--symmetry SYMMETRY Symmetry for training
Dataset loading:
--uninvert-data Do not invert data sign
--no-window Turn off real space windowing of dataset
--window-r WINDOW_R Windowing radius (default: 0.85)
--ind IND Filter particle stack by these indices
--lazy Lazy loading if full dataset is too large to fit in memory
--datadir DATADIR Path prefix to particle stack if loading relative paths from a .star or .cs file
Training parameters:
-n NUM_EPOCHS, --num-epochs NUM_EPOCHS
Number of training epochs (default: 20)
-b BATCH_SIZE, --batch-size BATCH_SIZE
Minibatch size (default: 4)
--wd WD Weight decay in Adam optimizer (default: 0)
--lr LR Learning rate in Adam optimizer (default: 0.001)
--norm NORM NORM Data normalization as shift, 1/scale (default: 0, 1)
Network Architecture:
--layers LAYERS Number of hidden layers (default: 2)
--dim DIM Number of nodes in hidden layers (default: 256)
--l-extent L_EXTENT Coordinate lattice size (if not using positional encoding) (default: 0.5)
--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,gaussian,none}
Type of positional encoding (default: geom_ft)
--pe-dim PE_DIM Number of sinusoid features in positional encoding (default: 32)
--activation {relu,leaky_relu}
Activation (default: relu)
python analysis.py -h
usage: analysis.py [-h] [--mask mrc] volumes
positional arguments:
volumes Half-maps directory (.mrc)
optional arguments:
-h, --help show this help message and exit
--mask mrc FSC mask (.mrc)