Implementation and Validation of an Automated Flow Cytometry Analysis Pipeline for Human Immune Profiling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automated reagent preparation, sample processing, and data acquisition have increased the rate at which flow cytometry data can be generated. Furthermore, advances in technology and flow cytometry instrumentation continually increase the complexity and dimensionality of this data. Together, this leads to increased pressure on manual data analysis, which has inherent limitations including subjectivity of the analyst and the length of time needed for data processing. These issues can create bottlenecks in the data processing workflow and potentially compromise data quality. To address these issues, as well as the challenges associated with manual gating in a high-volume human immune profiling laboratory, we sought to implement an automated analysis pipeline. In this report, we discuss considerations for selecting an automated analysis method, the process of implementing an automated pipeline, and detail our successful incorporation of an automated gating strategy with flowDensity into our analysis workflow. This validated pipeline augments our laboratory's ability to provide rapid high-throughput immune profiling for patients participating in cancer immunotherapy clinical trials. © International Society for Advancement of Cytometry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it