flowCL: ontology-based cell population labelling in flow cytometry
Bibliographic record
Abstract
MOTIVATION: Finding one or more cell populations of interest, such as those correlating to a specific disease, is critical when analysing flow cytometry data. However, labelling of cell populations is not well defined, making it difficult to integrate the output of algorithms to external knowledge sources. RESULTS: We developed flowCL, a software package that performs semantic labelling of cell populations based on their surface markers and applied it to labelling of the Federation of Clinical Immunology Societies Human Immunology Project Consortium lyoplate populations as a use case. CONCLUSION: By providing automated labelling of cell populations based on their immunophenotype, flowCL allows for unambiguous and reproducible identification of standardized cell types. AVAILABILITY AND IMPLEMENTATION: Code, R script and documentation are available under the Artistic 2.0 license through Bioconductor (http://www.bioconductor.org/packages/devel/bioc/html/flowCL.html). CONTACT: rbrinkman@bccrc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".