Robust chromatin state annotation
Bibliographic record
Abstract
Abstract Background Segmentation and genome annotations (SAGA) methods such as ChromHMM and Segway are widely to annotate chromatin states in the genome. These algorithms take as input a collection of genomics datasets, partition the genome, and assign a label to each segment such that positions with the same label have similar patterns in the input data. SAGA methods output an human-interpretable summary of the genome by labeling every genomic position with its annotated activity such as Enhancer, Transcribed, etc. Chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of SAGA state assignments. Results Towards the goal of producing robust chromatin state annotations, we performed a comprehensive evaluation of the reproducibility of SAGA methods. We show that SAGA annotations exhibit a large degree of disagreement, even when run with the same method on replicated data sets. This finding suggests that there is significant risk to using SAGA chromatin state annotations. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to SAGA annotations. This r-value is assigned to each genomic bin of a SAGA annotation and represents the probability that the label of this bin will be reproduced in a replicated experiment. This process is analogous to irreproducible discovery rate (IDR) analysis that is commonly used for ChIP-seq peak calling and related tasks. Thus SAGAconf allows a researcher to select only the reliable parts of a SAGA annotation for use in downstream analyses. SAGAconf r-values provide accurate confidence estimates of SAGA annotations, allowing researchers to filter out unreliable elements and remove doubt in those that stand up to this scrutiny.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".