Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays
Bibliographic record
Abstract
MOTIVATION: Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted. RESULTS: To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis). AVAILABILITY: The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: rbrinkman@bccrc.ca.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".