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

Liu, J.; Song, Y.; Lei, J.

doi:10.1101/678557

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bioRxiv · 10.1101/678557

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

Liu, J.·Song, Y.·Lei, J.

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Abstract

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.

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Liu, J., Song, Y., Lei, J.. 2019-06-21. Single-cell entropy to quantify the cellular transcriptome from single-cell RNA-seq data. https://doi.org/10.1101/678557

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