Web: http://arxiv.org/abs/2209.06716

Sept. 15, 2022, 1:12 a.m. | Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G.H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawr

stat.ML updates on arXiv.org arxiv.org

Single-cell RNA-seq datasets are growing in size and complexity, enabling the
study of cellular composition changes in various biological/clinical contexts.
Scalable dimensionality reduction techniques are in need to disentangle
biological variation in them, while accounting for technical and biological
confounders. In this work, we extend a popular approach for probabilistic
non-linear dimensionality reduction, the Gaussian process latent variable
model, to scale to massive single-cell datasets while explicitly accounting for
technical and biological confounders. The key idea is to use an …

arxiv data effects modelling scalable technical

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