Combinatorial approach to enumerate immunophenotypes in flow cytometry panels (TECH1P.872)
Bibliographic record
Abstract
Abstract The identification of cell populations from flow cytometry has rarely been mapped in a multi-dimensional space. Instead, the focus is typically limited to a small number of pre-specified cell types already described in the literature. We propose to re-conceptualize the flow cytometry panel as a discovery tool to disentangle complex flow data into the simplest, most clinically relevant cell types. We present a combinatorial method to enumerate all possible immunophenotypes within a flow panel from a clinical trial study. We applied this approach to 11,000 raw flow cytometry files from a multi-panel flow experiment in a ragweed allergy clinical trial (ImmPort:SDY1), available from the publicly accessible, NIAID-funded Immunology Database and Analysis portal (ImmPort; immport.niaid.nih.gov). Files were processed through an automated pipeline that applied ImmPort’s FLOCK autogating tool to segregate cell populations based on the expression of individual surface markers. This yielded roughly 59,000 unique cell populations. Unsupervised hierarchical clustering revealed cell populations that were highly correlated with treatment conditions, based on the Pearson similarity measure. From these results we identified a subset of monocytes with positive expression for CD23 that were correlated with clinical outcome. We believe this approach can help identify the minimal set of markers needed to characterize functionally distinct cell populations associated with clinical outcome.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".