The anatomy of single cell mass cytometry data
Bibliographic record
Abstract
Mass cytometry enables the measurement of up to 50 features on single cell. This has catalyzed a shift toward multidimensional data analysis methods, rather than the manual gating strategies as traditionally for in flow cytometry data. This shift means that data scientists are involved in the analysis process to an increasing degree. As the data is analyzed in a more unbiased fashion, where noisy or uninformative observations are not easily excluded, a deeper knowledge of the origin, noise, and modalities of the data is therefore needed to embark on useful data analysis. In this primer, we introduce the idiosyncrasies of mass cytometry data with a focus on the technical properties of how data generated with the CyTOF® system, and the characteristics of protein expression in the cells of the hematopoietic continuum, specifically targeted toward data scientists. We also provide a comprehensive online repository of scripts, tutorials, and example data. © 2018 International Society for Advancement of Cytometry.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".