DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations: Experiment Data

Details of experiments are given in the paper, titled 'DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations'.

Supporting code is available on GitHub at: https://github.com/marcottelab/2D_projection_clustering/

Details of the data are given below:

Folder CryoEM_data contains 6 datasets - one experimental, and 5 synthetic with 2D projection images of CryoEM particles, common-lines based similarity graph of the images, and ground truth clusters of the images corresponding to the same particles.

Folder DeepSLICEM_graph_node_embeddings contains graph node embeddings of the common-lines based similarity graph of the 2D projection images for each of the 6 datasets.

Folder DeepSLICEM_clustering_results contains algorithm predicted clusters of 2D projection images belonging to the same particles in each of the 6 datasets, along with the embeddings used for clustering, such as combined image and graph node embeddings.

Folder DeepSLICEM_siamese_neural_network_models contains models trained to obtain fine-tuned embeddings of 2D projection images based on similarity w.r.t the same particle's projections.