Single-cell entropy to quantify the cellular transcriptome from single-cell RNA-seq data

We present the use of single-cell entropy (scEntropy) to measure the order of the cellular transcriptome profile from single-cell RNA-seq data, which leads to a method of unsupervised cell type classification through scEntropy followed by the Gaussian mixture model (scEGMM). scEntropy is straightforward in defining an intrinsic transcriptional state of a cell. scEGMM is a coherent method of cell type classification that includes no parameters and no clustering; however, it is comparable to existing machine learning-based methods in benchmarking studies and facilitates biological interpretation.

• Publication year

  2019

• Venue

  bioRxiv

• Publication date

  2019-06-21

• Fields of study

  Biology, Chemistry, Computer Science

• Identifiers
  DOI 10.1142/S1793048020500010arXiv 2002.06391
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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