Computational tracking of cell origins using CellSexID from single-cell transcriptomes
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
Cell tracking in chimeric models is essential yet challenging in developmental biology, regenerative medicine, and transplantation research. Current methods like fluorescent labeling and genetic barcoding are technically demanding, costly, and often impractical for dynamic tissues. We present CellSexID, a computational framework that uses sex as a surrogate marker for cell-origin inference. By training machine-learning models on single-cell transcriptomic data, CellSexID accurately predicts individual cell sex, enabling in silico distinction between donor and recipient cells in sex-mismatched settings. The model identifies minimal sex-linked gene sets through ensemble feature selection and has been validated using public datasets and experimental flow sorting, confirming biological relevance. We demonstrate CellSexID's applicability beyond chimeric models, including organ transplantation and sample demultiplexing. As a practical alternative to physical labeling, CellSexID facilitates precise cell tracking and supports diverse biomedical applications where mixed cellular populations need to be distinguished.
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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 | 0.001 | 0.000 |
| 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 it