Profiling Chromatin Accessibility at Single-Cell Resolution
Bibliographic record
Abstract
How distinct transcriptional programs are enacted to generate cellular heterogeneity and plasticity, and enable complex fate decisions are important open questions. One key regulator is the cell's epigenome state that drives distinct transcriptional programs by regulating chromatin accessibility. Genome-wide chromatin accessibility measurements can impart insights into regulatory sequences (in)accessible to DNA-binding proteins at a single-cell resolution. This review outlines molecular methods and bioinformatic tools for capturing cell-to-cell chromatin variation using single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) in a scalable fashion. It also covers joint profiling of chromatin with transcriptome/proteome measurements, computational strategies to integrate multi-omic measurements, and predictive bioinformatic tools to infer chromatin accessibility from single-cell transcriptomic datasets. Methodological refinements that increase power for cell discovery through robust chromatin coverage and integrate measurements from multiple modalities will further expand our understanding of gene regulation during homeostasis and disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".