Disentangling single-cell omics representation with a power spectral density-based feature extraction

HIGHLIGHTS

• who: Seid Miad Zandavi and colleagues from the School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia have published the paper: Disentangling single-cell omics representation with a power spectral density-based feature extraction, in the Journal: (JOURNAL) of November/21,/2021
• what: Feature extraction seeks an optimal transformation of the input data into a latent feature vector with the primary goal of extracting important information from input data, controlling for confounding effects, adjusting overdispersion, and removing redundancy to enhance the separation of distinct cellular phenotypes .
• how: The . . .
  

   

  

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