Multi-level cellular and functional annotation of single-cell transcriptomes
Bibliographic record
Abstract
Abstract Single-cell RNA-sequencing (scRNA-seq) offers unprecedented insight into heterogenous biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell analytic toolbox that offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Novel methods that are introduced to facilitate scRNA-seq annotation include: (i ) co-dependency index (CDI)-based differential expression; (ii ) cluster resolution optimization using a marker-specificity criterion; (iii ) marker-based cell-type annotation with Miko scoring; and (iv ) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Our unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and we provide illustrative examples of cellular and transcriptomic annotation of developmental and immunological scRNA-seq atlases. Overall, scPipeline provides a flexible computational framework for in-depth scRNA-seq analysis.
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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 | 0.001 |
| 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".