Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data
Bibliographic record
Abstract
Aberrant alterations in genome copy number, chromatin accessibility, and transcriptional programs all play pivotal roles in cancer. The Numbat algorithm has been widely adopted to perform copy number variation (CNV) inference from single-cell RNA sequencing (scRNA-seq) data. Here, we introduce Numbat-multiome that extends the capabilities of Numbat to perform CNV inference from both scRNA-seq and accessible chromatin profiles (through single-cell Assay of Transposase [Tn5]-Accessible Chromatin sequencing [scATAC-seq] data), either separately or in an integrated manner. Our approach unifies data originating from different modalities through a binning strategy that relies on a common genomic coordinate system across modalities. We demonstrate the tool's robust performance in four running modes (RNA gene, RNA bin, ATAC bin, and Combined bin) using benchmark cohorts of tumors with dynamic changes in expression patterns and copy number heterogeneity, including early-stage multiple myeloma and Richter's syndrome arising from chronic lymphocytic leukemia, validated against whole-genome sequencing. Numbat-multiome achieves high precision and recall (median F1>0.9) across different CNV event types, with consistent performance across sample types and event lengths. The tool's ability to track clonal evolution in serial samples and identify rare subclones allows for integration of epigenomic profiles at the subclonal level, providing new insights into the stepwise genetic and epigenetic changes underlying cancer phenotypic shifts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".