— an unsupervised model for the integrative analysis of single-cell multiomics data

HIGHLIGHTS

• who: . and colleagues from the Tsinghua University, China have published the research: u2014 An unsupervised model for the integrative analysis of single-cell multiomics data, in the Journal: (JOURNAL)
• what: The authors propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data, where transcriptomic and epigenomic (chromatin accessibility or histone modifications) data were obtained from the same cell. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. By . . .
  

   

  

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