Robust segregation of donor and recipient cells from single-cell RNA-sequencing of transplant samples
Bibliographic record
Abstract
Background: Single-cell RNA-sequencing (scRNA-seq) technology has revealed novel cell populations in organs, uncovered regulatory relationships between genes, and allowed for tracking of cell lineage trajectory during development. It demonstrates promise as a method to better understand transplant biology; however, fundamental bioinformatic tools for its use in the context of transplantation have not been developed. One major need has been a robust method to identify cells as being either donor or recipient genotype origin, and ideally without the need to separately sequence the donor and recipient. Methods: and real-world scRNA-seq transplant data, we benchmarked our method against existing demultiplexing methods to profile their limitations in terms of sequencing depth, donor and recipient cell imbalance, and single nucleotide variant input selection. Results: data, scTx could more accurately separate donor from recipient cells and at much lower genotype ratios than existing methods. This was further validated using solid-organ scRNA-seq data where scTx could more reliably identify when a second genotype was present and at lower numbers of cells from a second genotype. Conclusion: scTx introduces the capability to accurately segregate donor and recipient gene expression at the single-cell level from scRNA-seq data without the need to separately genotype the donor and recipient. This will facilitate the use of scRNA-seq in the context of transplantation.
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 | 0.000 |
| 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 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".