  We present a novel method for automated identification of putative cell types
from single-cell RNA-seq (scRNA-seq) data. By iteratively applying a machine
learning approach to an initial clustering of gene expression profiles of a
given set of cells, we simultaneously identify distinct cell groups and a
weighted list of feature genes for each group. The feature genes, which are
differentially expressed in the particular cell group, jointly discriminate the
given cell group from other cells. Each such group of cells corresponds to a
putative cell type or state, characterised by the feature genes as markers. To
benchmark this approach, we use expert-annotated scRNA-seq datasets from a
range of experiments, as well as comparing to existing cell annotation methods,
which are all based on a pre-existing reference. We show that our method
automatically identifies the 'ground truth' cell assignments with high
accuracy. Moreover, our method, Single Cell Clustering Assessment Framework
(SCCAF) predicts new putative biologically meaningful cell-states in published
data on haematopoiesis and the human cortex. SCCAF is available as an
open-source software package on GitHub (https://github.com/SCCAF/sccaf) and as
a Python package index and has also been implemented as a Galaxy tool in the
Human Cell Atlas.
