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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Oct 22, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Matthew N Bernstein¹, Colin N Dewey^2,3

¹Morgridge Institute for Research, Madison, WI 53715, USA.

|August 30, 2021

Summary

This summary is machine-generated.

CellO is a machine-learning tool that annotates human cells from single-cell RNA sequencing data. This protocol details its use with Scanpy for analyzing lung tissue datasets and generating figures.

Keywords:

Bioinformatics RNAseq

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Last Updated: Oct 22, 2025

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Published on: January 10, 2019

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Accurate cell type annotation is crucial for interpreting single-cell RNA sequencing (scRNA-seq) data.
Existing methods may lack the hierarchical structure needed for comprehensive cell classification.

Purpose of the Study:

To provide a protocol for using the CellO Python package for cell type annotation.
To demonstrate the integration of CellO with Scanpy for scRNA-seq analysis.

Main Methods:

Utilized the CellO machine-learning tool and Python package.
Integrated CellO with the Scanpy library for scRNA-seq data analysis.
Applied the protocol to a human lung tissue dataset.

Main Results:

Successfully annotated human cells using CellO and Scanpy.
Interpreted hierarchically structured cell type annotations.
Generated publication-ready figures for data visualization.

Conclusions:

The CellO Python package offers a robust method for cell type annotation in scRNA-seq data.
The protocol facilitates the use of CellO with Scanpy for detailed analysis of complex datasets.
This approach aids in the biological interpretation of single-cell data through hierarchical classification.