Flow, Spectral Flow and Mass Cytometry Analysis
We provide comprehensive bioinformatics support for the analysis and interpretation of flow, spectral flow and mass cytometry data, helping you extract meaningful biological insights from high‑dimensional single‑cell datasets. Our team can intervene at any step of the analysis pipeline, from raw instrument output to data already preprocessed in software such as FlowJo, Kaluza or Cytobank. We have extensively analysed Cytek Aurora and CyTOF data.
What we offer
- Experimental design and data management
- Advice on panel design, replication and sample size
- Support for data organization, metadata annotation and documentation of analyses
- Quality control and preprocessing
- Import of raw files with attention to truncation, unmixing and compensation issues.
- Transformation (for example arcsinh or logicle)
- Manual and automated quality control to remove low‑quality events and samples using state‑of‑the‑art cleaning algorithms (for example flowAI, PeacoQC, flowClean)
- Assessment and correction of batch effects and integration of multiple datasets (for example multi‑center or longitudinal studies) using control samples or study design information (for example normalization methods like CytoNorm or regression‑based adjustment)
- Dimensionality reduction and visualization
- Interpretable 2D projections using UMAP, t‑SNE, PCA or diffusion maps to explore cellular heterogeneity
- Publication‑quality figures with customizable coloring by markers, samples or experimental conditions
- Unsupervised clustering, gating and population annotation
- Unsupervised clustering tailored to your dataset and question (for example FlowSOM or PhenoGraph)
- Support for both data‑driven clustering and classical gating (automated or semi‑automated), and guided merging/annotation of clusters into biologically interpretable populations
- Differential and advanced statistical analyses
- Differential abundance of cell populations across conditions, time points or patient groups
- Differential state analysis of functional markers within defined populations
- Regression‑based frameworks (for example generalized linear or mixed models) that can incorporate covariates such as clinical variables
- Trajectory and pseudotime analysis: inference of cellular trajectories and pseudotime for developmental or activation processes (for example using diffusion maps combined with trajectory methods such as Slingshot)
- Reproducible pipelines and reporting : construction of documented, version‑controlled R‑based workflows using established Bioconductor ecosystems (for example flowCore, CATALYST, FlowSOM, diffcyt, slingshot, CytoNorm, CytoPipeline), and delivery of concise reports or interactive tools adapted to your project’s needs
How we work
- We work collaboratively with your research team
- We perform the analysis in close consultation with you, ensuring biological relevance at every step
- We provide guidance and hands-on support so you can learn to analyze your own data and gain long-term autonomy
- We develop tailored analysis strategies and visualization tools to meet your specific research needs