FlowCAP: comparison of automated and manual gating of standardized lyoplate flow cytometry data (P3374)
Bibliographic record
Abstract
Abstract Standardization of immunological assays, including flow cytometry, in terms of reagents, sample handling, instrument setup, and data analysis, is essential for successful cross-study and cross-center analysis in order to mitigate the effects of technical variability in assay results. The Human Immunology Project and the Federation of Clinical Immunology Societies (FOCIS) have partnered to develop five standardized, lyophilized, eight-color staining reagent panels (termed lyoplates) for this purpose. In collaboration with the FlowCAP consortium, standardized samples (Cytotrol control cells) were distributed to nine participating centers and analyzed by flow cytometry using the lyoplate reagents and SOP’s to minimize experimental variability. Data from two of these panels (T-cell and B-cell) were entered into the FlowCAP-III challenge, where participants analyzed the data using automated gating methods for comparison against cell population statistics for major T and B-cell subsets as defined by a consensus manual gating scheme. This evaluation showed that several automated gating algorithms could successfully recapitulate centralized manual gating statistics for T-cell and B-cell subsets with little statistical bias, and with within-center and between-center variability as low or lower than centralized manual gating. These results demonstrate that automated gating algorithms are ready for use in performing reproducible analyses and comparisons of immunological data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".