FlowCAP: critical assessment of flow cytometry population identification methods (65.2)
Bibliographic record
Abstract
Abstract Traditional methods for flow cytometry (FCM) data processing have relied on manual gating of cell events to define cell populations for statistical analysis. However, this approach has become increasingly problematic with the advances in instrumentation and reagents that allow for evaluation of larger numbers of cell properties. Recently several groups have developed computational methods for automatically identifying cell populations in multidimensional FCM data obviating the need for manual gating. In order to compare the performance of these methods, the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) competition was established to make available a common set of FCM data together with manual gating results for comparative analysis. The first FlowCAP competition included 5 different data sets with data from 12-30 samples containing 5000-100,000 cell events stained with 3-10 fluorochrome markers. We received 36 analysis result submissions from 14 research groups. Both model fitting and density-based clustering methods were found to perform well in comparison with manual gating by domain experts as the gold standard, using statistical tests to measure and rank algorithm performance. In addition, combining results using a computational “ensemble” method was found to outperform all individual methods. These results suggest that, in the near future, automated computational methods may become an integral part of routine FCM data analysis.
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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 | 0.062 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".