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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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      "fileid": "https://r2.ropensci.org/7dd7a214b9ae4212095f336c34543b7128f312631f482c63955788fe01e8483f",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/bioc/actions/runs/36238408949"
    }
  ]
}