  Single-cell analysis is currently one of the most high-resolution techniques
to study biology. The large complex datasets that have been generated have
spurred numerous developments in computational biology, in particular the use
of advanced statistics and machine learning. This review attempts to explain
the deeper theoretical concepts that underpin current state-of-the-art analysis
methods. Single-cell analysis is covered from cell, through instruments, to
current and upcoming models. A minimum of mathematics and statistics has been
used, but the reader is assumed to either have basic knowledge of single-cell
analysis workflows, or have a solid knowledge of statistics. The aim of this
review is to spread concepts which are not yet in common use, especially from
topology and generative processes, and how new statistical models can be
developed to capture more of biology. This opens epistemological questions
regarding our ontology and models, and some pointers will be given to how
natural language processing (NLP) may help overcome our cognitive limitations
for understanding single-cell data.
