Clustering cite-seq data with a canonical correlation-based deep learning method

HIGHLIGHTS

  • who: August and colleagues from the Shandong University, China have published the paper: Clustering CITE-seq data with a canonical correlation-based deep learning method, in the Journal: (JOURNAL)
  • what: The authors propose single-cell CITE-seq Cluster (scCTClust) to conduct clustering for CITE-seq data. Instead of finding proper linear projections, the authors seek appropriate parameters θ(r) and θ(p) for omics-specific encoders f(r) and f(p). Specifically, the authors aim to investigate the performance of scCTClust against competing methods under different cluster numbers, protein data dimensions, and differential expression feature probabilities.
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