Best practices for instrument settings and raw data analysis in plant flow cytometry
Bibliographic record
Abstract
Flow cytometry (FCM) is now the most widely used method to determine ploidy levels and genome size of plants. To get reliable estimates and allow reproducibility of measurements, the methodology should be standardized and follow the best practices in the field. In this article, we discuss instrument calibration and quality control and various instrument and acquisition settings (parameters, flow rate, number of events, scales, use of discriminators, peak positions). These settings must be decided before measurements because they determine the amount and quality of the data and thus influence all downstream analyses. We describe the two main approaches to raw data analysis (gating and histogram modeling), and we discuss their advantages and disadvantages. Finally, we provide a summary of best practice recommendations for data acquisition and raw data analysis in plant FCM.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.014 |
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".