Run the LSH-GAN_parabolasimulation.py from folder Parabola_simulation_plots_codes. It will generate the parabola figures for different iteration.
Download the Raw demo dataset (yan.rds). Put the dataset and codes in same folder.
Run the DataProcessing.R file to preprocess the raw datasets. There are three user parameters: min_Reads, min_Cell, min_Gene.
It will generate preprocessdata.csv. (The preprocessed Data)
Run the file LSH-GAN.py. It will generate the samples with different size (#0.25p, 0.5p, 0.75p, 1p, 1.25p, 1.5p) , p is the feature size.
The user input for number of iter in LSH-GAN for LSH step is given 1 as default for given dataset (yan.rds) . It can be changed by users depending upon sample size of datasets, iter is 2 for klein dataset.
The another user input is number of epoch for training the LSH-GAN. Default is 10000. The optimal sample size for datasets used in our work is given in main paper.
The Wasserstein metric computation code is given in Validation_Wasserstein.py file.
The Feature Selection (FS) and Adjusted Rand Index (ARI) computation code is given in Validation_FS_ARI.R file.
R version 4.0.2
R packages: SingleCellExperiment, scDataset.
Python 3.7
Python packages: sklearn-0.19.2, multiprocessing, tensorflow