Identifying differential cell populations in flow cytometry data accounting for marker frequency
Bibliographic record
Abstract
We present a statistical test that compares a novel abundance score, SpecEnr, to discover biologically meaningful and interpretable differential cell population that predict a given phenotype or disease from flow cytometry data. Existing methods for differential cell population identification compare a limited set of prespecified cell populations, find differential cell populations as a byproduct of another procedure, or compare overlapping cell populations in a search space of all possible cell populations. Though thorough, analyzing all possible cell populations can be difficult as many cell populations share cells with one another. For example, an increase in one cell population may induce an increase in several other cell populations that share its cells. Our method solves this issue by taking into account these dependencies. By comparing independent score, SpecEnr, we find differential cell populations that are the source of these changes and we show how these results can be easily interpreted via a lattice-based visualization tool.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".