  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.
