EP6: CELLTYPIST & CELLHINT - Towards Automated Annotation And Integration Of Single-Cell Data

"The increase in single-cell datasets leads to challenges in annotating cell types and further comparing them across the community such as the Human Cell Atlas (HCA). A unified way to name, harmonize, and integrate cell types holds promise for the consolidation of hard-earned legacy knowledge among researchers in the field. In this talk, you will be introduced the two machine learning-based tools, CellTypist (https://github.com/Teichlab/celltypist) and CellHint (https://github.com/Teichlab/cellhint) that were developed to annotate and unify single-cell datasets in an automated manner. CellTypist and CellHint build on interpretable machine learning frameworks, create shareable models, and aim to address one of the most fundamental questions in cell biology: what cell types are contained in human organs?"

Learning objectives:
1. Understanding the concepts of cell type annotation, harmonization, and integration.
2. Understanding the approaches CellTypist and CellHint take to address the above problem(s).
3. Understanding the biological signals that can be detected and enriched by CellTypist and CellHint.

#SCSE #Celltyping #cellannotation #cellhint #celltypist #singlecellrnaseq #singlecell