Robust classification of single-cell transcriptome data by nonnegative matrix factorization

@article{Shao2017RobustCO,
  title={Robust classification of single-cell transcriptome data by nonnegative matrix factorization},
  author={Chunxuan Shao and Thomas H{\"o}fer},
  journal={Bioinformatics},
  year={2017},
  volume={33 2},
  pages={235-242}
}
MOTIVATION Single-cell transcriptome data provide unprecedented resolution to study heterogeneity in cell populations and present a challenge for unsupervised classification. Popular methods, like principal component analysis (PCA), often suffer from the high level of noise in the data. RESULTS Here we adapt Nonnegative Matrix Factorization (NMF) to study the problem of identifying subpopulations in single-cell transcriptome data. In contrast to the conventional gene-centered view of NMF… CONTINUE READING
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