ddClone: Joint statistical inference of clonal populations from single cell and bulk tumour sequencing data
Bibliographic record
Abstract
A statistical framework leveraging data obtained from both single cell and bulk sequencing strategies. The ddClone (Salehi et al.) approach is predicated on the notion that single cell sequencing data will inform and improve clustering of allele fractions derived from bulk sequencing data in a joint statistical model. ddClone combines a Bayesian non-parametric prior informed by single cell data with a likelihood model based on bulk sequencing data to infer clonal population architecture. Intuitively, the prior encourages genomic loci with co-occurring mutations in single cells to cluster together. Using a cell-locus binary matrix from single cell sequencing, ddClone computes a distance matrix between mutations using the Jaccard distance with exponential decay. This matrix is then used as a prior for inference over mutation clusters and their prevalences from deeply sequenced bulk data in a distance-dependent Chinese restaurant process (Frazier and Blei 2012) framework. The output of the model is the most probable set of mutational clusters present and the prevalence of each mutation in the population. The code is based on the ddCRP model, as introduced and implemented in (Frazier and Blei 2012).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".