Annotating cell types in human single-cell RNA-seq data with CellO

Matthew N Bernstein; Colin N Dewey

doi:10.1016/j.xpro.2021.100705

Annotating cell types in human single-cell RNA-seq data with CellO

STAR Protoc. 2021 Aug 17;2(3):100705. doi: 10.1016/j.xpro.2021.100705. eCollection 2021 Sep 17.

Authors

Matthew N Bernstein¹, Colin N Dewey^{2

3}

Affiliations

¹ Morgridge Institute for Research, Madison, WI 53715, USA.
² Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI 53792, USA.
³ Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI 53706, USA.

Abstract

Cell type annotation is important in the analysis of single-cell RNA-seq data. CellO is a machine-learning-based tool for annotating cells using the Cell Ontology, a rich hierarchy of known cell types. We provide a protocol for using the CellO Python package to annotate human cells. We demonstrate how to use CellO in conjunction with Scanpy, a Python library for performing single-cell analysis, annotate a lung tissue data set, interpret its hierarchically structured cell type annotations, and create publication-ready figures. For complete details on the use and execution of this protocol, please refer to Bernstein et al. (2021).

Keywords: Bioinformatics; RNAseq.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biological Ontologies
Computational Biology / methods
Data Curation / methods*
Exome Sequencing / methods
Humans
Machine Learning
RNA-Seq / methods*
Sequence Analysis, RNA / methods*
Single-Cell Analysis / methods
Software
Transcriptome / genetics