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Data Harmonirization.md

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Variance Inference

  • R. Lopez, J. Regier, M. B. Cole, M. I. Jordan, and N. Yosef, “Deep generative modeling for single-cell transcriptomics,” Nature Methods, vol. 15, no. 12, Art. no. 12, Dec. 2018, doi: 10.1038/s41592-018-0229-2.
  • V. Svensson, A. Gayoso, N. Yosef, and L. Pachter, “Interpretable factor models of single-cell RNA-seq via variational autoencoders,” Bioinformatics, vol. 36, no. 11, pp. 3418–3421, Jun. 2020, doi: 10.1093/bioinformatics/btaa169.
  • G. Eraslan, L. M. Simon, M. Mircea, N. S. Mueller, and F. J. Theis, “Single-cell RNA-seq denoising using a deep count autoencoder,” Nature Communications, vol. 10, no. 1, Art. no. 1, Jan. 2019, doi: 10.1038/s41467-018-07931-2.

Integration

  • T. Stuart et al., “Comprehensive Integration of Single-Cell Data,” Cell, vol. 177, no. 7, pp. 1888-1902.e21, Jun. 2019, doi: 10.1016/j.cell.2019.05.031.
  • I. Korsunsky et al., “Fast, sensitive and accurate integration of single-cell data with Harmony,” Nature Methods, vol. 16, no. 12, Art. no. 12, Dec. 2019, doi: 10.1038/s41592-019-0619-0.
  • J. Liu, C. Gao, J. Sodicoff, V. Kozareva, E. Z. Macosko, and J. D. Welch, “Jointly defining cell types from multiple single-cell datasets using LIGER,” Nature Protocols, vol. 15, no. 11, Art. no. 11, Nov. 2020, doi: 10.1038/s41596-020-0391-8.

Normalization

  • C. Hafemeister and R. Satija, “Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression,” Genome Biology, vol. 20, no. 1, p. 296, Dec. 2019, doi: 10.1186/s13059-019-1874-1.
  • A. T. L. Lun, K. Bach, and J. C. Marioni, “Pooling across cells to normalize single-cell RNA sequencing data with many zero counts,” Genome Biol, vol. 17, no. 1, Art. no. 1, Dec. 2016, doi: 10.1186/s13059-016-0947-7.
  • W. Tang et al., “bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data,” Bioinformatics, vol. 36, no. 4, pp. 1174–1181, Feb. 2020, doi: 10.1093/bioinformatics/btz726.
  • A. Lun, “Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data,” bioRxiv, p. 404962, Aug. 2018, doi: 10.1101/404962.
  • P.-Y. Tung et al., “Batch effects and the effective design of single-cell gene expression studies,” Scientific Reports, vol. 7, no. 1, Art. no. 1, Jan. 2017, doi: 10.1038/srep39921.
  • D. Kuang and J. Kim, “Resistant Fit Regression Normalization for Single-cell RNA-seq Data,” in 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Oct. 2020, pp. 236–240, doi: 10.1109/BIBE50027.2020.00046.
  • R. Bacher et al., “SCnorm: robust normalization of single-cell RNA-seq data,” Nature Methods, vol. 14, no. 6, Art. no. 6, Jun. 2017, doi: 10.1038/nmeth.4263.
  • J. Wang et al., “Data denoising with transfer learning in single-cell transcriptomics,” Nature Methods, vol. 16, no. 9, Art. no. 9, Sep. 2019, doi: 10.1038/s41592-019-0537-1.