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GSR: Simulator - LSH-GAN
| Attribute | Value |
|---|---|
| Title | LSH-GAN |
| Short Description | LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data |
| Long Description | A fundamental problem of downstream analysis of scRNA-seq data is the unavailability of enough cell samples compare to the feature size. This is mostly due to the budgetary constraint of single cell experiments or simply because of the small number of available patient samples. Here, we present an improved version of generative adversarial network (GAN) called LSH-GAN to address this issue by producing new realistic cell samples. We update the training procedure of the generator of GAN using locality sensitive hashing which speeds up the sample generation, thus maintains the feasibility of applying the standard procedures of downstream analysis. LSH-GAN outperforms the benchmarks for realistic generation of quality cell samples. Experimental results show that generated samples of LSH-GAN improves the performance of the downstream analysis such as feature (gene) selection and cell clustering. Overall, LSH-GAN therefore addressed the key challenges of small sample scRNA-seq data analysis. |
| Keywords | single-cell RNA sequencing, scRNA-seq, HDSS data, Machine learning, Computational models |
| Version | 1.0.0 |
| Project Started | 2021 |
| Last Release | 3 years, 6 months ago |
| Homepage | https://github.com/Snehalikalall/LSH-GAN |
| Citations | Lall S, Ray S, Bandyopadhyay S, LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data., Commun Biol, June 10, 2022 [ Abstract, cited in PMC ] |
| GSR Certification | Accessibility |
| Last evaluated | Dec. 9, 2022 (862 days ago) |
| Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | RNA, Single-Cell Sequencing, |
| Variations | Single Nucleotide Variation, |
| Simulation Method | Machine Learning, |
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| Sequencing Reads | |
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| Recombination | |
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| Other | |
| Interface | Command-line, |
| Development | |
| Tested Platforms | |
| Language | R, Python, |
| License | |
| GSR Certification | Support, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 0
No example publication using LSH-GAN has been provided.
Please propose new citations if you are aware of publications that use this software.