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  "Description": "Single-cell RNA-seq technologies enable high throughput\ngene expression measurement of individual cells, and allow the\ndiscovery of heterogeneity within cell populations. Measurement\nof cell-to-cell gene expression similarity is critical for the\nidentification, visualization and analysis of cell populations.\nHowever, single-cell data introduce challenges to conventional\nmeasures of gene expression similarity because of the high\nlevel of noise, outliers and dropouts. We develop a novel\nsimilarity-learning framework, SIMLR (Single-cell\nInterpretation via Multi-kernel LeaRning), which learns an\nappropriate distance metric from the data for dimension\nreduction, clustering and visualization.",
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      "commit": "dd6fe994a67929d10433f2b53737d6dc1a3155ce",
      "fileid": "https://r2.ropensci.org/3c920d386eaf640333714a3afe8d6670d51255d73f63793b69d86ac4a4478b69",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/bioc/actions/runs/40314395725"
    }
  ]
}