Flow cytometry data standards
Bibliographic record
Abstract
BACKGROUND: Flow cytometry is a widely used analytical technique for examining microscopic particles, such as cells. The Flow Cytometry Standard (FCS) was developed in 1984 for storing flow data and it is supported by all instrument and third party software vendors. However, FCS does not capture the full scope of flow cytometry (FCM)-related data and metadata, and data standards have recently been developed to address this shortcoming. FINDINGS: The Data Standards Task Force (DSTF) of the International Society for the Advancement of Cytometry (ISAC) has developed several data standards to complement the raw data encoded in FCS files. Efforts started with the Minimum Information about a Flow Cytometry Experiment, a minimal data reporting standard of details necessary to include when publishing FCM experiments to facilitate third party understanding. MIFlowCyt is now being recommended to authors by publishers as part of manuscript submission, and manuscripts are being checked by reviewers and editors for compliance. Gating-ML was then introduced to capture gating descriptions - an essential part of FCM data analysis describing the selection of cell populations of interest. The Classification Results File Format was developed to accommodate results of the gating process, mostly within the context of automated clustering. Additionally, the Archival Cytometry Standard bundles data with all the other components describing experiments. Here, we introduce these recent standards and provide the very first example of how they can be used to report FCM data including analysis and results in a standardized, computationally exchangeable form. CONCLUSIONS: Reporting standards and open file formats are essential for scientific collaboration and independent validation. The recently developed FCM data standards are now being incorporated into third party software tools and data repositories, which will ultimately facilitate understanding and data reuse.
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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 | 0.054 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.117 |
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".