Modeling Type 1 Diabetes progression from single-cell transcriptomic measurements in human islets
Bibliographic record
Abstract
Abstract Type 1 diabetes (T1D) is a chronic condition in which the insulin-producing beta cells are destroyed by immune cells. Research in the past few decades characterized the immune cells involved in disease pathogenesis and has led to the development of immunotherapies that can delay the onset of T1D by two years. Despite this progress, early detection of autoimmunity in individuals who will develop T1D remains a challenge. Here, we evaluated the potential of combining single-cell genomics and machine learning strategies as a prime approach to tackle this challenge. We used gradient-boosting-based machine learning algorithms and modeled changes in transcriptional profiles of single cells from pancreatic tissues in T1D and nondiabetic organ donors collected by the Human Pancreas Analysis Program. We assessed whether mathematical modelling could predict the likelihood of T1D development in nondiabetic autoantibody-positive organ donors. While the majority of autoantibody-positive organ donors were predicted to be nondiabetic by our model, select donors with unique gene signatures were classified with the T1D group. Remarkably, our strategy also revealed a shared gene signature in distinct T1D associated models based on different cell types including alpha cells, beta cells and acinar cells, suggesting a common effect of the disease on transcriptional outputs of these cells. Together, our strategy presents the first report on the utility of machine learning algorithms in early detection of molecular changes in T1D.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".