Instruments and measurements · What you can do here
Analyse flow cytometry data
Parse FCS files, apply spillover compensation matrices, transform fluorescence scales, execute sequential gating from controls, and calculate population counts and parent frequencies.
What this involves
Flow cytometry data analysis requires systematic handling of raw listmode FCS files to convert detector events into biological statistics. The platform parses file metadata and event data using fcsparser and FlowIO on a dedicated environment, removing the need for local software installations. The raw data are inspected directly to identify signal saturation or baseline drift before applying the file's recorded spillover matrix ($SPILLOVER or SPILL) to compensate for spectral overlap.
Correct gating requires a rigorous, ordered progression: identifying intact cells and clearing debris on forward and side scatter (FSC/SSC), excluding doublets using forward scatter area versus height (FSC-A against FSC-H), isolating viable cells, and then defining marker-positive subsets. Marker thresholds are established against fluorescence minus one (FMO) or unstained control tubes rather than placed visually on stained samples.
Downstream functional assays are evaluated with quantitative models. Proliferation measured by CFSE is quantified by fitting halving peak intensities to determine division and proliferation indices against undivided controls. Cell cycle DNA content distributions (G1, S, and G2 phases) are resolved using Dean–Jett–Fox style modelling on singlet populations, with EdU or BrdU pulse labelling used to quantify S-phase duration over time.
How it goes
- 1FCS parsing and compensationFCS files are imported using fcsparser or FlowIO to extract event data and channel metadata, followed by applying the internal $SPILLOVER or SPILL matrix to correct for fluorophore emission overlap before gating.
- 2Fluorescence transformationCompensated fluorescence channels are transformed using logicle scaling or an arcsinh transformation with a cofactor of approximately 150 to resolve negative and low-intensity events on visual plots.
- 3Sequential gating hierarchyEvents are filtered in sequence: debris exclusion on FSC/SSC, doublet discrimination on FSC-A versus FSC-H, live/dead discrimination, and specific marker boundaries calibrated against FMO or unstained controls.
- 4Proliferation and cell cycle modellingFor specialized assays, CFSE generational peaks are fitted as halving fluorescent intensities, or DNA histograms are fitted using Dean–Jett–Fox models to isolate G1, S, and G2 populations.
- 5Exporting counts and frequenciesEvent counts are recorded at every gating stage, and frequencies are computed relative to parent populations, accompanied by parameter uncertainty estimates and comparisons of alternative gating boundaries.
Start it here
Ask in plain language. The agent works on a Linux machine of its own — it installs and runs what the job needs, keeps every file, and reads the results back with the numbers explained. Nothing to set up, and a long run continues after you close the page.
What to watch for
- Never set positive marker gates by visual estimation on experimental samples; always calibrate boundaries using FMO or unstained controls.
- Do not gate uncompensated events; apply the acquisition $SPILLOVER or SPILL matrix before defining biological sub-populations.
- Avoid reporting frequencies relative to total acquired events rather than the immediate parent gate unless specifically required.
- Ensure doublets are eliminated using FSC-A versus FSC-H before attempting cell cycle analysis or Dean–Jett–Fox distribution fitting.
- Do not treat technical replicates as independent measurements; average them into a single biological n and report the error model.
What it runs
nf-core pipelines
Questions
Do I need to install anything to analyse flow cytometry data?
No. The research agent does this work on a Linux machine of its own — it installs what the job needs, keeps the files, and reads the results back to you. Long runs continue if you close the page.
Which tools read and process the FCS files?
The platform runs Python scripts using packages including fcsparser and FlowIO on its own machine, returning tables and plots without requiring local software or licenses.
How are display scales transformed after compensation?
Data are visualised using either an arcsinh transformation with a cofactor of approximately 150 or logicle scaling to accurately display negative and low-intensity values.
How should proliferation experiments like CFSE be quantified?
Generations are fitted as fluorescent peaks that halve in intensity with each division, allowing the calculation of division and proliferation indices alongside undivided control samples.
How are DNA content cell cycle phases determined?
Singlet events are fitted with Dean–Jett–Fox style models to estimate the proportions of cells in G1, S, and G2 phases, and EdU or BrdU pulse-labelling data are used to derive S-phase duration over time.
Written from the method the research agent follows for this work and the documentation of the tools listed above. Statements citing a figure that does not appear there are removed before publication. Corollary is a non-commercial research demonstration; results are not for clinical use.