PolarFCS: A Multi-Parametric Data Visualisation Aid for Flow Cytometry Assessment
Bibliographic record
Abstract
Currently available Flow-Cytometry Software (FCS) analysis platforms are computationally efficient and user-friendly, but may lack the functionality of single-plot, multi-parametric data visualisation. Methods to overcome this include gating techniques and/or dimensionality reduction. However, these strategies make Flow-Cytometry (FC) data analysis more time- and labour-intensive; profound errors can also result from incorrect FCS use. We have developed PolarFCS, a software tool capable of single-plot, multi-parametric data visualisation. Unlike traditional clinical FC plots, which typically operate directly on a data-set to produce single-parameter FC histograms or two-parameter orthogonal scatter plots, PolarFCS operates on the flow-parameter calculated centre of mass of each event in the data-set, and presents these as a dot-plot. We compare PolarFCS to our traditional clinical FCS workflow, using a selection of clinical plasma cell FC data. Multiple flow plots and gating strategies are required in the traditional software to isolate neoplastic populations. In PolarFCS, however, positional re-arrangement and scaling of the poles can be used to quickly isolate a population of interest. We also compare both approaches in a case of Minimal Residual Disease (MRD) assessment, and again, the versatility of the polar adjustment and parameter scaling allowable with PolarFCS is demonstrated. PolarFCS employs strategies that allow more accurate, standardised and detailed FC data analysis compared to traditional FCS platforms. Visualisation of multiple parameters in a single plot is an effective and invaluable feature that many other platforms currently do not offer. Availability: PolarFCS can be downloaded at https://github.com/etiennemahe/PolarFCS
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".