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Related Concept Videos

RNA-seq03:21

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Related Experiment Video

Updated: Nov 29, 2025

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Become Competent in Generating RNA-Seq Heat Maps in One Day for Novices Without Prior R Experience.

Kejin Hu1

  • 1Department of Biochemistry and Molecular Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA. kejinhu@uab.edu.

Methods in Molecular Biology (Clifton, N.J.)
|November 23, 2020
PubMed
Summary

This tutorial teaches biologists to create heat maps from RNA sequencing (RNA-seq) data using R, even with no prior programming experience. Master heat map generation for gene expression analysis in just one day.

Keywords:
Heat mapsHuman embryonic stem cellsRNA-seqRStudioTutorialWNT pathwayspheatmap

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Heat map visualization is crucial for RNA sequencing (RNA-seq) data analysis.
  • Many biological scientists lack the R programming skills or bioinformatician support to generate heat maps independently.

Purpose of the Study:

  • To provide a beginner-friendly tutorial for creating heat maps from RNA-seq data using the pheatmap package in R.
  • To empower biological scientists to perform heat map visualization without prior R experience.

Main Methods:

  • Installation of R, RStudio, and the pheatmap package.
  • Step-by-step guidance on basic R commands and data manipulation for RNA-seq datasets.
  • Demonstration of pheatmap function arguments for customized heat map generation.

Main Results:

  • Successful generation of heat maps for 16 differentially expressed genes in the WNT pathway.
  • Provision of over 20 template scripts for creating and customizing heat maps.
  • Enabling novices to produce publication-ready heat maps within a day.

Conclusions:

  • Biological scientists can master heat map generation for RNA-seq data analysis in one day with this tutorial.
  • The pheatmap package offers a user-friendly approach to visualizing gene expression patterns.
  • Democratizing data visualization tools for biological research.