Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis
of-flight; ELISA, enzyme-linked immunosorbent assay; SPADE, spanning tree progression of density normalized events; CLARA, clustering for large applications; Citrus, cluster identification, characterization and regression; mTORC1, mechanistic target of rapamycin complex 1; viSNE, visualization of t-distributed stochastic neighbor embedding; One-SENSE, one-dimensional soliexpression by nonlinear stochastic embedding; ACCENSE, automatic classification of cellular expression by nonlinear
... embedding; SCUBA, single-cell clustering using bifurcation analysis; HIF-1α, hypoxia-inducible factor; GBM, glioblastoma; DREMI, conditional-density resampled estimate of mutual information; DREVI, conditional-density rescaled visualization; GFP, green fluorescent protein; Abstract New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions.