Copy-scAT: Deconvoluting single-cell chromatin accessibility of genetic subclones in cancer
Bibliographic record
Abstract
Single-cell epigenomic assays have tremendous potential to illuminate mechanisms of transcriptional control in functionally diverse cancer cell populations. However, application of these techniques to clinical tumor specimens has been hampered by the current inability to distinguish malignant from nonmalignant cells, which potently confounds data analysis and interpretation. Here, we describe Copy-scAT, an R package that uses single-cell epigenomic data to infer copy number variants (CNVs) that define cancer cells. Copy-scAT enables studies of subclonal chromatin dynamics in complex tumors like glioblastoma. By deploying Copy-scAT, we uncovered potent influences of genetics on chromatin accessibility profiles in individual subclones. Consequently, some genetic subclones were predisposed to acquire stem-like or more differentiated molecular phenotypes, reminiscent of developmental paradigms. Copy-scAT is ideal for studies of the relationships between genetics and epigenetics in malignancies with high levels of intratumoral heterogeneity and to investigate how cancer cells interface with their microenvironment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".