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Fast single-cell analysis with non-negative dimensional reductions

Details

There are reasons to not use PCA.

  • PCA fits to missing signal,

  • considers only highly variable features,

  • is almost useless without further graph-based analysis,

  • requires centering and scaling of your data,

  • and is robust only within experiments.

Instead, you should use Non-negative Matrix Factorization (NMF).

  • NMF imputes missing signal,

  • learns models using all features,

  • does everything PCA does and provides useful information itself,

  • requires only variance stabilization,

  • and is robust across experiments.

Singlet is all about extremely fast NMF for single-cell dimensional reduction and integration.

See the vignettes to get started.

Author

Zach DeBruine