Best practices for user consultation in flow cytometry shared resource laboratories
Bibliographic record
Abstract
This "Best Practices in User Consultation" article is the result of a 2022 International Society for the Advancement of Cytometry (ISAC) membership survey that collected valuable insights from the shared research laboratory (SRL) community and of a group discussion at the CYTO 2022 workshop of the same name. One key takeaway is the importance of initiating a consultation at the outset of a flow cytometry project, particularly for trainees. This approach enables the improvement and standardization of every step, from planning experiments to interpreting data. This proactive approach effectively mitigates experimental bias and avoids superfluous trial and error, thereby conserving valuable time and resources. In addition to guidelines, the optimal approaches for user consultation specify communication channels, methods, and critical information, thereby establishing a structure for productive correspondence between SRL and users. This framework functions as an exemplar for establishing robust and autonomous collaborative relationships. User consultation adds value by providing researchers with the necessary information to conduct reproducible flow cytometry experiments that adhere to scientific rigor. By following the steps, instructions, and strategies outlined in these best practices, an SRL can readily tailor them to its own setting, establishing a personalized workflow and formalizing user consultation services. This article provides a pragmatic guide for improving the caliber and efficacy of flow cytometry research and aggregates the flow cytometry SRL community's collective knowledge regarding user consultation.
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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 | 0.374 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.014 | 0.045 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".