sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution
Bibliographic record
Abstract
Chromosome copy number variations (CNVs) are a near-universal feature of cancer; however, their individual effects on cellular function are often incompletely understood. Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) might be leveraged to reveal the function of intra-clonal CNVs; however, it cannot directly link cellular gene expression to CNVs. Here, we report a high-throughput scRNA-seq analysis pipeline that provides paired CNV profiles and transcriptomes for single cells, enabling exploration of the effects of CNVs on cellular programs. RTAM1 and -2 normalization methods are described, and are shown to improve transcriptome alignment between cells, increasing the sensitivity of scRNA-seq for CNV detection. We also report single-cell inferred chromosomal copy number variation (sciCNV), a tool for inferring single-cell CNVs from scRNA-seq at 19-46 Mb resolution. Comparison of sciCNV with existing RNA-based CNV methods reveals useful advances in sensitivity and specificity. Using sciCNV, we demonstrate that scRNA-seq can be used to examine the cellular effects of cancer CNVs. As an example, sciCNV is used to identify subclonal multiple myeloma (MM) cells with +8q22-24. Studies of the gene expression of intra-clonal MM cells with and without the CNV demonstrate that +8q22-24 upregulates MYC and MYC-target genes, messenger RNA processing and protein synthesis, which is consistent with established models. In conclusion, we provide new tools for scRNA-seq that enable paired profiling of the CNVs and transcriptomes of single cells, facilitating rapid and accurate deconstruction of the effects of cancer CNVs on cellular programming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".