Flow Cytometry Data Analysis in R : A Complete Tutorial 2024

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: github.com/pri...
Download public datasets: flowrepository.org
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Supplementary Resources and Support
For those eager to learn more, we’ve curated a list of additional reading materials and online resources. Plus, join our community forum for ongoing support and to engage with fellow learners.
Other videos:
• 2024 Ultimate Guide: S...
• (1) Flow Cytometry Dat...
Important Links:
dillonhammill....
jchellmuth.com...
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Stay tuned for our next video, where we’ll delve deeper into advanced data analysis techniques in R, further enhancing your skills in bioinformatics.
We wrap up this tutorial by summarizing the key points and encouraging you to practice these new skills in your research. Remember, proficiency in data analysis is a journey of continuous learning and application.
Don’t miss out on the rest of our series! Subscribe to our channel, engage with our content, and share your progress or questions in the comments. Together, let’s explore the exciting world of flow cytometry data analysis!
Keywords: Flow Cytometry Data Analysis, R Programming Tutorial, Bioinformatics Skills,and Data Visualization in R
#FlowCytometry #DataAnalysis #Bioinformatics #RProgramming #FACSTutorial

[@norbergarcia497]

Looking forward to your next video on CytoExploreR! Thank you for taking the time to explain the code in detail :)

[@BioinfoCopilot]

Sure will try to update soon!

[@cristianjaviermena6955]

AWESOME! thank you very much!

[@BioinfoCopilot]

My pleasure 😇

[@jawad7776]

Please upload another session of fcs files data analysis by R

[@BioinfoCopilot]

Sure. I will upload soon. Thanks

[@jawad7776]

Sir Still waiting for your 2nd video of flow data analysis by R😢

[@BioinfoCopilot]

Vacation time now. Will update next year. Thanks 🙏

[@damanrique]

Dear fellow: with autoplot and several other functions to plot I have tried, I get always this error: "Error in guides$setup(scales, aesthetics, default = params$guide_default %||% : attempt to apply non-function" I have not been able to find a solution. Any ideas? Regards

[@BioinfoCopilot]

This is due to package dependencies being not updated while installing the main packages. You can read this thread: github.com/thackl/gggenomes/issues/181

[@saurabhbaheti3032]

thanks for the tutorial. I am facing an issue with flowAI. flow_auto_qc(fcsfile) Error: cannot allocate vector of size 36292.7 Gb . Do you know how can i resolve this?

[@BioinfoCopilot]

First check memory limit in R >memory.limit() [1] some values > memory.limit(size=xyz) Check how much RAM you have. I think this is a very big file. You have to do it in HPC or so.

[@phillipssekamatte6761]

How would you compesate using actual single stain controls?

[@BioinfoCopilot]

Using Spillover matrix rdrr.io/bioc/flowStats/man/spillover-flowSet.html

[@phillipssekamatte6761]

Thanks you so much

[@rps2930]

Can you share sources from where we can download flow cytometry data freely.

[@BioinfoCopilot]

Here you can download public datasets: flowrepository.org

[@rps2930]

@@BioinfoCopilot thank you Sir for your reply. Is it a good idea to show this kind of analysis in the poster in a conference?

[@BioinfoCopilot]

@rps2930 Yes definitely 👍

[@andreasElmann]

This worked for me setwd("directory") file_list