Quantification of transcript isoforms at the single-cell level using SCALPEL
Bibliographic record
Abstract
Single-cell RNA sequencing (scRNA-seq) facilitates the study of transcriptome diversity in individual cells. Yet, many existing methods lack sensitivity and accuracy. Here we introduce SCALPEL, a Nextflow-based tool to quantify and characterize transcript isoforms from standard 3’ scRNA-seq data. Using synthetic data, SCALPEL demonstrates higher sensitivity and specificity compared to other tools. In real datasets, SCALPEL predictions have a high agreement with other tools and can be experimentally validated. The use of SCALPEL on real datasets reveals novel cell populations undetectable using single-cell gene expression data, confirms known 3’ UTR length changes during cell differentiation, and identifies cell-type specific miRNA signatures regulating isoform expression. Additionally, we show that SCALPEL improves isoform quantification using paired long- and short-read scRNA-seq data. Overall, SCALPEL expands the current scRNA-seq toolkit to explore post-transcriptional gene regulation across species, tissues, and technologies, advancing our understanding of gene regulatory mechanisms at the single-cell level. Single-cell RNA-seq facilitates the study of transcriptome diversity in individual cells. Here, authors introduce a tool for isoform quantification at the single-cell level using 3’ scRNA-seq data, contributing to the study of post-transcriptional gene regulation in individual cells.
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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 | 0.000 |
| 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 | 0.001 | 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".