scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data
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DOI: 10.1038/s41467-024-45227-w
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- David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
- Xiuwei Zhang & Chenling Xu & Nir Yosef, 2019. "Simulating multiple faceted variability in single cell RNA sequencing," Nature Communications, Nature, vol. 10(1), pages 1-16, December.
- Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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