Statistical modeling, estimation, and remediation of sample index hopping in multiplexed droplet-based single-cell RNA-seq data
Bibliographic record
Abstract
Abstract We introduce a probabilistic model for estimation of sample index-hopping rate in multiplexed droplet-based single-cell RNA sequencing data and for inference of the true sample of origin of the hopped reads. Across the datasets we analyzed, we estimate the sample index hopping probability to range between 0.003–0.009, a small number that counter-intuitively gives rise to a large fraction of ‘phantom molecules’ – as high as 85% in a given sample. We demonstrate that our model-based approach can correct for this artifact by accurately purging the majority of phantom molecules from the data. Code and reproducible analysis notebooks are available at https://github.com/csglab/phantom_purge . Structure Section 1 provides a concise summary of the paper. Section 2 provides a brief historical and technical overview of the phenomenon of sample index hopping and an explanation of related concepts. The three sections that follow describe the statistical modeling approach and correspond to the following three goals. (1) Building a generative model that probabilistically describes the phenomenon of sample index hopping of multiplexed sample reads (Section 3). (2) Estimating the index hopping rate from empirical experimental data (Section 4). (3) Correcting for the effects of sample index hopping through a principled probabilistic procedure that reassigns reads to their true sample of origin and discards predicted phantom molecules by optimally minimizing the false positive rate (Section 5). Next, Section 6 details the results of the analyses performed on empirical and experimental validation datasets. The Supplementary Notes consists of three sections: (1) Mathematical Derivations, (2) Overview of Computational Workflow, (3) Method’s Limitations.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".