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

Matthew N Bernstein, Colin N Dewey
Author Information
  1. Matthew N Bernstein: Morgridge Institute for Research, Madison, WI 53715, USA.
  2. Colin N Dewey: Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI 53792, 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

References

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MeSH Term

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

Word Cloud

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