Sydney Bioinformatics Webinar: Kim -- scReClassify: Post-hoc Cell Type Classification of Single Cell RNA-seq Data

Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global
transcriptome profiling on the single cell level.  Cell type identification from
scRNA-seq data is a critical task in a variety of research such as developmental
biology, cell reprogramming, and cancers.  Typically, cell type identification relies on
human inspection using a combination of prior biological knowledge (e.g.  marker genes
and morphology) and computational techniques (e.g.  PCA and clustering).  Due to the
incompleteness of our current knowledge and the subjectivity involved in this process, a
small amount of cells may be subject to mislabelling.  

A semi-supervised learning framework, scReClassify, for ‘post hoc’ cell type
identification from scRNA-seq datasets is developed.  Starting from an initial cell type
annotation with potentially mislabelled cells, scReClassify first performs dimension
reduction using PCA and next applies a semi-supervised learning method to learn and
subsequently reclassify cells that are likely mislabelled initially to the most probable
cell types.  By using both simulated and real-world experimental datasets that profiled
various tissues and biological systems, scReClassify is shown to be able to accurately
identify and reclassify misclassified cells to their correct cell types.  scReClassify
can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune
cell type annotations generated by any cell type classification procedure.  It is
implemented as an R package and is freely available from
https://github.com/SydneyBioX/scReClassify