Flow Cytometry Data Analysis & Visualization in R using CytoExploreR: Complete Guide

Dive into the world of scientific data analysis with our detailed tutorial on Flow Cytometry Data Analysis in R. This article complements the first video on flow cytometry analyses designed to equip you with cutting-edge skills in analyzing flow cytometry data using the powerful R programming language. Perfect for both beginners and experienced researchers, this tutorial is your gateway to mastering flow cytometry analysis in 2024.

High-throughput FACS analyses with R Evaluating flow cytometry data using R might appear intimidating initially, but I strongly encourage its adoption for individuals conducting medium to high-throughput FACS-based experiments. Even when examining a limited number of markers, conventional flow analysis software such as FlowJo struggles when dealing with extensive sample datasets. It operates slowly, is susceptible to crashes, and exporting large plots can be cumbersome. In contrast, R-based flow cytometry analysis excels in addressing these challenges effectively. Various R packages are available for the analysis of flow cytometry data, offering versatile solutions for researchers.

Understanding Flow Cytometry

Flow cytometry is a crucial technique used in cell biology, immunology, and other research areas for analyzing the physical and chemical characteristics of cells or particles. Our video begins by introducing you to the basics of flow cytometry, its significance in modern science, and its diverse applications.

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Why R for Flow Cytometry?

We delve into the reasons why R is the preferred tool for flow cytometry data analysis. Its powerful statistical and data visualization capabilities make it an invaluable asset for researchers looking to gain deeper insights from their data. R’s flexibility and extensive library support streamline the flow cytometry analysis process, making it accessible even to those new to programming.

Our tutorial guides you through the initial setup process, including installing R and the necessary packages. We provide step-by-step instructions to ensure you have a smooth start, setting the foundation for efficient data analysis.

In this video, I will guide you through the intricacies of analyzing high-throughput FACS data using R. Whether you're a beginner or an experienced researcher, this tutorial is designed to enhance your data analysis skills in the realm of flow cytometry.

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Key Tutorial Segments

• Data Import and Management: Learn to import flow cytometry data into R and manage it effectively.
• Cleaning and Preprocessing Data: We cover essential steps in preparing your data for analysis, ensuring accuracy and reliability in your results.
• Exploratory Data Analysis (EDA): Discover techniques to explore and understand your dataset, a crucial step before diving into more complex analyses.
• Data Visualization: Our video demonstrates how to create insightful and visually appealing data visualizations, an essential skill in presenting your findings.

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To reinforce learning, we provide practical examples and exercises. Work on a sample dataset to apply the skills you’ve learned, gaining hands-on experience in flow cytometry data analysis.

Github Repo: https://github.com/pritampanda15/Prot...

Download public datasets: http://flowrepository.org

CytoExploreR is comprehensive collection of interactive exploratory flow cytometry analysis tools designed under a unified framework.

CytoExploreR has been specifically designed to integrate all existing flow cytometry analysis techniques (e.g. manual gating, automated gating and dimension reduction) in a format that makes these tools freely accessible to users with no coding experience.

If you are new to CytoExploreR visit https://dillonhammill.github.io/CytoE... to get started.

Github: https://github.com/pritampanda15/Prot...

#FACS #FlowCytometry #CytoExplorer #DataAnalysis #RStats #Bioinformatics #Cytometry #RProgramming #SingleCellAnalysis #DataVisualization

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