Visualization of Cell Composition and Maturation in the Bone Marrow Using 10‐Color Flow Cytometry and Radar Plots
Bibliographic record
Abstract
BACKGROUND: The enormous potential of complex data files generated by 10-color flow cytometry (FC) is hindered by the requirement for exhaustive manual gating and the complexity of multidimensional data visualization. We propose a model using radar plots (RPs), to improve FC data visualization by capturing multidimensionality and integration of FC findings. METHOD: We analysed 12 normal/reactive bone marrow (N/R BM) samples and 12 BM samples from patients with myelodysplasia (MDS) with 10-color FC. All identifiable cell clusters were individually marked, grouped, and visualized on radar plots. RPs were optimized to de-clutter the cell clusters and map BM cell composition and maturation. RESULTS: A total of 27 immature and mature cell clusters were identified and visualized on 8 multidimensional radar plots. The RPs displayed flow cytometry findings of normal BM in an integrated fashion to maximize overall insight into the data set. The constructed map of bone marrow cell composition was reproducible in all normal BM samples analyzed. Analysis of the pilot cohort of patient samples confirmed the presence of MDS-related changes. These changes are readily identifiable on RPs. CONCLUSION: We demonstrated that the cell clusters of normal BM can be mapped on multidimensional radar plots, which provide an inclusive insight into BM cell composition and maturation. These reproducible RPs present a comprehensive and comprehensible visual display of differentiation and maturation of haematopoietic cells in normal BM, and can be used as a reference map to assess abnormal haematopoiesis in MDS. © 2017 International Clinical Cytometry Society.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".