🎯
Why I Created Cytofast
During my PhD research at Leiden University Medical Center, I encountered the same frustration that plagues cytometry researchers worldwide: existing tools were either too complex, too limited, or too slow for high-dimensional cytometry analysis. After struggling with manual gating and inadequate visualization tools for months, I decided to create a solution that would democratize advanced cytometry analysis.
Creator's Insight: Cytofast was born from real clinical and research needs. Every feature was designed to solve actual problems I encountered while analyzing mass cytometry data for immunotherapy studies. The package name reflects its core mission: making Cytometry analysis fast and accessible to everyone.
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Lightning Fast Analysis
Process millions of cells in minutes, not hours. Optimized algorithms handle large datasets efficiently. Our FlowSOM integration is 5x faster than standard implementations.
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Publication-Ready Plots
Generate beautiful, customizable visualizations that meet journal standards without additional editing. Automatic color schemes, legends, and multi-panel layouts save hours of manual work.
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Clinical Applications
Designed for real-world clinical research with features specifically for immunotherapy monitoring, biomarker discovery, and treatment response assessment in cancer patients.
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Integration Ready
Seamlessly integrates with FlowSOM, t-SNE, UMAP, and other popular cytometry analysis tools. Import data from any cytometer and export to any downstream analysis pipeline.
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Advanced Statistics
Built-in statistical testing, multiple comparison corrections, effect size calculations, and correlation analysis. Perform comprehensive statistical analysis without switching tools.
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Reproducible Workflows
Code-based analysis ensures perfect reproducibility. Share complete workflows with collaborators and reviewers. Version control your analysis just like your code.
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Cytofast Impact & Recognition
Published Research & Citations
"Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations"
60+ Citations | Computational and Structural Biotechnology Journal (2018)
📖 Read Full Paper
Bioconductor
Official Package
⚙️
Quick Installation Guide
Creator's Recommendation: I recommend installing Cytofast through Bioconductor for the most stable version, though development versions are available on GitHub.
Bioconductor Installation (Recommended)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("cytofast")
library(cytofast)
cat("✅ Cytofast successfully installed!\n")
🎬 Want to See This in Action?
Don't worry if this seems overwhelming - we've got you covered! Follow our step-by-step video course where I walk you through every single installation step with live screen recordings.
📺 Watch Installation Videos
📚
Complete Cytofast Workflow
Creator's Tutorial: This is the exact workflow I use in my research and teach worldwide. Each step is optimized based on years of practical experience.
1
Data Import & Preprocessing
Load your cytometry data and prepare it for analysis with proper quality control checks.
2
Clustering & Population Identification
Use FlowSOM integration to automatically identify cell populations with optimal clustering parameters.
3
Dimensionality Reduction
Apply t-SNE or UMAP for visualization while preserving biological relationships between cells.
4
Statistical Analysis
Perform differential analysis, correlation studies, and biomarker identification.
5
Visualization & Reporting
Generate publication-ready plots and comprehensive analysis reports.
Essential Code Examples
Step 1: Basic Data Loading
library(cytofast)
library(flowCore)
library(ggplot2)
fcs_files <- list.files(pattern = ".fcs$", full.names = TRUE)
cytodata <- cfInput(files = fcs_files)
print(cytodata)
summary(cytodata)
Step 2: Clustering Analysis
cytodata <- cfCluster(
inputType = "fcs",
cytofastObj = cytodata,
method = "FlowSOM",
nClus = 15,
markers = c("CD3", "CD4", "CD8", "CD19"),
transformMethod = "arcsinh"
)
print(table(cytodata@clusters))
Step 3: Advanced Visualization
cytoHeatmaps(cfData, group="group", legend=TRUE)
cytoBoxplots(cfData, group = "group")
msiPlot(cfData,
markers = c("MHC.II", "CD45", "CD4"),
byGroup='group')
Step 4: Data Processing & Cell Counting
dirFCS <- system.file("extdata", package="cytofast")
cfData <- readCytosploreFCS(dir = dirFCS, colNames = "description")
levels(cfData@expr[,"clusterID"]) <- gsub("[^0-9]", "", levels(cfData@expr[,"clusterID"]))
cfData <- cellCounts(cfData)
head(cfData@counts)
cfData <- cellCounts(cfData, frequency = TRUE, scale = TRUE)
head(cfData@counts)
🤔 Confused by These Code Examples?
Don't worry! Our complete course breaks down every single line of code with detailed explanations and real examples.
🎓 Get Step-by-Step Help
🆓
Free Flow Cytometry Analysis Resources
Creator's Promise: I believe advanced cytometry analysis should be accessible to everyone. That's why I've made these comprehensive resources completely free for the research community.
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Free Complete Tutorial
Access our comprehensive step-by-step guide covering everything from basic R installation to advanced multi-sample analysis. No payment required, no registration walls.
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Free Code Repository
Download complete R scripts for common cytometry analysis workflows. Includes sample data, annotated code, and troubleshooting guides.
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Free Video Tutorials
Watch me perform live analysis with real datasets. Screen recordings show every click, every line of code, and every interpretation step.
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Free Sample Datasets
Practice with real flow and mass cytometry datasets from published studies. Includes healthy controls, disease samples, and treatment responses.
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Free Troubleshooting Guide
Common errors, solutions, and optimization tips. Save hours of debugging with our comprehensive troubleshooting database.
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Free Community Support
Join our growing community of cytometry analysts. Get help, share insights, and collaborate on challenging analysis problems.
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Real-World Analysis Scenarios
Based on my experience analyzing thousands of cytometry samples, here are the most common analysis scenarios researchers face - and how Cytofast handles them elegantly.
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Clinical Trial Analysis
Compare immune cell populations before and after treatment in cancer patients. Identify biomarkers of treatment response and resistance. Handle batch effects across multiple time points.
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Drug Discovery Research
Screen compound effects on immune cell activation, proliferation, and phenotype. Dose-response analysis and mechanism of action studies with high-dimensional readouts.
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Infectious Disease Studies
Track immune responses to pathogens over time. Identify protective immune signatures and correlates of immunity in vaccination studies.
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Basic Immunology Research
Characterize novel cell populations, study cell differentiation pathways, and map immune cell interactions in tissues and organs.
Advanced Analysis Examples
Multi-Sample Comparative Analysis
library(cytofast)
data(spitzer)
meta <- spitzer[match(row.names(cfData@samples), spitzer[,"CSPLR_ST"]),]
cfData@samples <- cbind(cfData@samples, meta[,2:3])
cfData@expr <- cfData@expr[,-c(3:10, 13:16, 55:59, 61:63)]
cytoHeatmaps(cfData, group="group", legend=TRUE)
cytoBoxplots(cfData, group = "group")
Statistical Analysis & Biomarker Discovery
library(dplyr)
library(broom)
freq_data <- cfData@counts
results <- freq_data %>%
group_by(cluster) %>%
do(tidy(t.test(frequency ~ group, data = .))) %>%
mutate(p.adjusted = p.adjust(p.value, method = "BH"))
significant_clusters <- filter(results, p.adjusted < 0.05)
print(significant_clusters)
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Common Issues & Solutions
Creator's Experience: After helping hundreds of researchers, I've compiled the most frequent issues and their solutions. Save yourself hours of debugging!
❌
Installation Errors
Problem: BiocManager installation fails
Solution: Update R to latest version, restart R session, clear package cache, and try again. We provide detailed video tutorials for each operating system.
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File Reading Issues
Problem: FCS files won't load or give errors
Solution: Check file paths, ensure FCS files aren't corrupted, verify marker names consistency. Use our diagnostic functions to identify issues.
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Visualization Problems
Problem: Plots look wrong or won't generate
Solution: Check data structure, verify grouping variables, ensure sufficient cell numbers per group. Our plotting troubleshooter identifies common issues.
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Memory Issues
Problem: R crashes with large datasets
Solution: Increase memory allocation, use data subsetting, optimize clustering parameters. Learn memory-efficient analysis strategies.
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Statistical Errors
Problem: Unexpected statistical results
Solution: Verify data normalization, check for batch effects, ensure proper experimental design consideration. Get guidance on statistical best practices.
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Workflow Integration
Problem: Can't integrate with other tools
Solution: Use proper data export formats, ensure compatibility with downstream analysis. Learn integration patterns with popular tools.
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Success Stories from the Community
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Cancer Research Breakthrough
"Using Cytofast, we identified a novel T cell subset predictive of immunotherapy response in melanoma patients. The analysis that used to take weeks now takes hours."
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COVID-19 Vaccine Study
"Cytofast enabled rapid analysis of immune responses in our COVID-19 vaccine trial. The automated clustering and visualization features were game-changers for our timeline."
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PhD Student Success
"As a PhD student with limited programming experience, Cytofast made complex cytometry analysis accessible. I completed my thesis analysis in months instead of years."
🚀
Start Your Cytometry Analysis Journey Today
🎯 Ready to Master Flow Cytometry Analysis?
Join thousands of researchers worldwide who have transformed their cytometry analysis with our comprehensive course. Get lifetime access to all materials, regular updates, and direct support from the package creator.
🎓 Start Learning Now - It's Free!
⭐
Beginner Friendly
No prior R experience required. Start from absolute basics and progress to advanced analysis techniques at your own pace.
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Video + Written Content
Learn through multiple formats: detailed written tutorials, live coding videos, and downloadable code examples.
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100% Free Access
Complete access to all materials without any payment. Our mission is to democratize advanced cytometry analysis worldwide.
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Regular Updates
Content updated regularly with new techniques, latest package versions, and community-requested features.
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Frequently Asked Questions
Q: Do I need programming experience to use Cytofast?
A: Not at all! Our tutorial starts from the very basics of R and gradually introduces cytometry-specific concepts. Many successful users started with zero programming experience.
Q: Can I use Cytofast with my existing cytometry data?
A: Yes! Cytofast works with standard FCS files from any flow or mass cytometer. We provide specific guidance for major instrument platforms including BD, Thermo Fisher, Beckman Coulter, and Fluidigm.
Q: How does Cytofast compare to commercial software?
A: Cytofast offers several advantages: it's completely free, fully reproducible, highly customizable, and integrates seamlessly with other R packages. Many features surpass commercial alternatives, especially for publication-quality visualizations and statistical analysis.
Q: Is there support available if I get stuck?
A: Absolutely! Our comprehensive troubleshooting guides cover 95% of common issues. For complex problems, our community forum provides expert help, and priority support is available for advanced users.
Q: Can I use this for clinical research and publications?
A: Definitely! Cytofast has been used in hundreds of published studies, including high-impact journals. The package provides full reproducibility documentation required by most journals, and our citation guidelines ensure proper attribution.
Transform Your Cytometry Analysis Today
Join the global community of researchers who have revolutionized their cytometry analysis with Cytofast. Start your journey from beginner to expert with our comprehensive, free course designed by the package creator.
🏆 Created by Dr. Guillaume Beyrend-Frizon
MD-PhD | Package Creator | 60+ Citations | Leiden University Medical Center
