Computational Tracking of Cell Origins Using CellSexID from Single-Cell Transcriptomes
Bibliographic record
Abstract
Abstract Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplantation research. Existing methods such as fluorescent labeling and genetic barcoding are technically demanding, costly, and often impractical for dynamic or heterogeneous tissues. Here, we introduce CellSexID, a computational framework that leverages sex as a surrogate marker for cell origin inference. Using a machine learning model trained on single-cell transcriptomic data, CellSexID accurately predicts the sex of individual cells, 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 both public datasets and experimental flow sorting, confirming the biological relevance of predicted populations. We further demonstrate CellSexID’s applicability beyond chimeric models, including organ transplantation and multiplexed sample demultiplexing. As a scalable and cost-effective alternative to physical labeling, CellSexID facilitates precise cell tracking and supports diverse biomedical applications involving mixed cellular origins.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".