A Pipeline for automated analysis of flow cytometry data: Preliminary results on lymphoma sub-type diagnosis
Bibliographic record
Abstract
Flow cytometry (FCM) is widely used in health research and is a technique to measure cell properties such as phenotype, cytokine expression, etc., for up to millions of cells from a sample. FCM data analysis is a highly tedious, subjective and manually time-consuming (to the level of impracticality for some data) process that is based on intuition rather than standardized statistical inference. This study proposes a pipeline for automatic analysis of FCM data. The proposed pipeline identifies biomarkers that correlate with physiological/pathological conditions and classifies the samples to specific pathological/physiological entities. The pipeline utilizes a model-based clustering approach to identify cell populations that share similar biological functions. Support vector machine (SVM) and random forest (RF) classifiers were then used to classify the samples and identify biomarkers associated with disease status. The performance of the proposed data analysis pipeline has been evaluated on lymphoma patients. Preliminary results show more than 90% accuracy in differentiating between some sub-types of lymphoma. The proposed pipeline also finds biologically meaningful biomarkers that differ between lymphoma subtypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".