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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.

Zoe A Clarke1,2, Tallulah S Andrews2,3,4, Jawairia Atif3,4

  • 1Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

Nature Protocols
|May 25, 2021
PubMed
Summary
This summary is machine-generated.

This tutorial guides researchers on interpreting single-cell transcriptomics data to build annotated cell maps. It details a three-step workflow for identifying cell types and states from complex biological datasets.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell transcriptomics enables high-throughput profiling of cellular heterogeneity.
  • Established protocols exist for generating single-cell transcriptomic data from various tissues.
  • Interpreting this data is crucial for discovering novel cell types, states, and dynamics.

Purpose of the Study:

  • To provide a tutorial on interpreting single-cell transcriptomic data.
  • To guide the creation of annotated cell maps.
  • To address common challenges in data interpretation.

Main Methods:

  • A three-step workflow is recommended: automatic cell annotation, manual cell annotation, and verification.
  • The tutorial covers guiding principles and specific software tools for each step.
  • An R notebook is provided to facilitate the workflow execution.

Main Results:

  • The tutorial offers strategies for overcoming frequently encountered challenges in data interpretation.
  • It provides recommendations for software and resources.
  • The R notebook allows for practical application of the recommended workflow.

Conclusions:

  • Effective interpretation of single-cell transcriptomic data is key to generating comprehensive cell atlases.
  • The presented workflow and resources aid researchers in identifying cell types, states, and dynamics.
  • This guide supports the advancement of single-cell data analysis and biological discovery.