This repository holds the code for training and running the model for the class ranking functionality of RELION.
The class ranker is part of the cryogenic electron microscopy (cryo-EM) dataset processing pipeline in RELION. It is used to automatically select suitable particles (EM images) assigned to 2D class averages for further downstream processing.
This model comprises two main components: a CNN responsible for extracting image features, and an MLP that combines the image features with additional statistics to assigns a score ranging from zero to one for each 2D class average.
The selection is subsequently done in RELION through a user defined cutoff for the predicted score.
For training, a supervised approach is adopted, where pairs of 2D classes and corresponding human-assigned scores are used to teach the model.
For more details see this paper.
To create a dataset, you need a STAR-file containing the following columns:
- ClassScore - Ground truth score
- SubImageStack - Input image path and index
- NormalizedFeatureVector - Input feature vector
See EMPIAR data base (entry-ID 10812) for more details on how to generate datasets from raw data.
To train the model the STAR-file and all the images need to be preprocessed and serialized. Use make_dataset.py to do this:
python train/make_dataset.py <dataset root> <star file> --nr_valid <nr valid> --output <output file>
In the above, <dataset root> is the root directory of the references in the STAR-file (<star file>). <nr valid> is the number of images that will be reserved for validation.
After generating the dataset file above. Training can be done using the train.py, as follows:
python train/train.py <dataset file> --output <log directiry> --gpu 0
Published in Biochemical Journal 2021 (Volume 478, Issue 24). Bibtex:
@article{kimanius2021new,
title={New tools for automated cryo-EM single-particle analysis in RELION-4.0},
author={Kimanius, Dari and Dong, Liyi and Sharov, Grigory and Nakane, Takanori and Scheres, Sjors HW},
journal={Biochemical Journal},
volume={478},
number={24},
pages={4169--4185},
year={2021},
publisher={Portland Press Ltd.}
}