Published June 28, 2023
| Version v1
Dataset
Open
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
Authors/Creators
- 1. Department of Computer Science and Information Systems, Birkbeck, University of London, United Kingdom
- 2. Department of Computer Science, University College London, United Kingdom
Description
The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.
Files
Binary_scRNAseq_datasets.zip
Files
(276.7 MB)
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md5:b890d398bdff999785c4443b9f790b45
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Additional details
References
- Tamim Abdelaal, Lieke Michielsen, Davy Cats, Dylan Hoogduin, Hailiang Mei, Marcel J. T. Reinders, and Ahmed Mahfouz. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biology, 20(194), 2019.
- Jiarui Ding, Xian Adiconis, Sean K Simmons, Monika S Kowalczyk, Cynthia C Hession, Nemanja D Marjanovic, Travis K Hughes, Marc H Wadsworth, Tyler Burks, Lan T Nguyen, et al. Systematic comparison of single- cell and single-nucleus RNA-sequencing methods. Nature biotechnology, 38(6):737–746, 2020.
- Liang Chen, Weinan Wang, Yuyao Zhai, and Minghua Deng. Deep soft K-means clustering with self-training for single-cell RNA sequence data. NAR genomics and bioinformatics, 2(2):lqaa039, 2020.