Datasets
Mouse brain accessibility datasets from multiple platforms and genome-reference versions.
Task
Cross-platform scATAC cell-type annotation
Cross-platform cell annotation transfers labels between scATAC-seq reference and query datasets.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results tableOverview
Metrics
Accuracy and F1 for cell-type annotation, stratified by source-to-target transfer task.
Allowed inputs
Chromatin-accessibility representations in source/reference and target/query domains.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results tableEvaluation procedure diagram
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results tablelimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Results
Results are available, but no reviewed comparison panel is linked in this release.
All evaluations
2 evaluations · 2 results. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Pipeline: scLLMDA | Task: Cross-platform scATAC cell-type annotation Dataset: MosA1 reference → WholeBrainA query | 0.652 F1 unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourcescLLMDA: Cross-platform scATAC cell-type annotation Cross-platform reference-query cell-type annotation. Aggregation: Not reported Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Table 2, scLLMDA row, Ref: MosA1 / Q: WholeBrainA F1 column |
| Configuration: MINGLE | Task: Cross-platform scATAC cell-type annotation Dataset: MosA1 reference → WholeBrainA query | 0.626 F1 unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMINGLE: Cross-platform scATAC cell-type annotation Cross-platform reference-query comparator. Aggregation: Not reported Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Table 2, MINGLE row, Ref: MosA1 / Q: WholeBrainA F1 column |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Methods and evaluation design
Procedure, tasks and evaluated configurations
How it works
Evaluation methodology
Mouse brain accessibility datasets from multiple platforms and genome-reference versions. Source/reference and target/query domains are evaluated under platform and tissue distribution shifts. Accuracy and F1 for cell-type annotation, stratified by source-to-target transfer task. scNym, scJoint, Cellcano, SANGO, annATAC, AtacAnnoR and MINGLE. The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results tableRecorded evaluations
Each evaluation records what was tested and under which conditions.
Run instructions
No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.
A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.
Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Limitations and conditions
- Unlabelled target cells participate in adaptation. Comparators differ in feature modalities and access to reference information, which must remain attached to their reported results.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table
Profile review details
Relevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged.
Stable record: reported-task-d82b6284f3f431Specifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Datasets | Mouse brain accessibility datasets from multiple platforms and genome-reference versions.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Splits | Source/reference and target/query domains are evaluated under platform and tissue distribution shifts.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Metrics | Accuracy and F1 for cell-type annotation, stratified by source-to-target transfer task.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Baselines | scNym, scJoint, Cellcano, SANGO, annATAC, AtacAnnoR and MINGLE.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Leakage controls | This is transductive domain adaptation: both source and target cells enter the graph-training stage, while the stated classification loss uses labelled source nodes. Target-domain access is part of the protocol and should not be represented as an untouched-query inductive test.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: overall loss, source classification loss, domain-adversarial loss and Parameter settings; cached paragraphs 60–69 |
| Uncertainty | The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim. · Not reported in inspected sourcesSourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Entity type | Paper-specific computational evaluation protocol.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Organisms | Mouse brain.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Assays | Single-cell ATAC-seq across platforms and genome-reference versions.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Allowed inputs | Chromatin-accessibility representations in source/reference and target/query domains.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
| Adaptation | Cross-platform annotation transfer compared with specialized annotation methods.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Papers and result coverage
Last literature check: 2026-09-17. Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
| Paper or primary resource | Version | Reference |
|---|---|---|
| Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation | version of record | Read source DOI: 10.1371/journal.pcbi.1014226 |
Historical gaps recorded on 2026-09-17
The catalogue now holds 2 result rows for this benchmark. A note below about pending extraction describes the state on 2026-09-17 and may since have been answered by a later batch. The result rows and their sources are the current record.
- Reference-to-query direction is part of protocol identity; reversed pairs are not replicates.
- Inputs and adaptation differ across baselines; scJoint uses scATAC at both stages and Cellcano follows its original target-size-dependent rounds.
- No uncertainty in tables; F1 and accuracy printed separately, including slash-joined Table 3 cells.
Search and extraction details
primary comparison table screened
Searches
- "PMC13132462"
Evidence locations
- Tables 2–3
- Methods: Benchmark methods
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
17 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets Mouse brain accessibility datasets from multiple platforms and genome-reference versions. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits Source/reference and target/query domains are evaluated under platform and tissue distribution shifts. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Cross-platform annotation transfer compared with specialized annotation methods. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Accuracy and F1 for cell-type annotation, stratified by source-to-target transfer task. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines scNym, scJoint, Cellcano, SANGO, annATAC, AtacAnnoR and MINGLE. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls This is transductive domain adaptation: both source and target cells enter the graph-training stage, while the stated classification loss uses labelled source nodes. Target-domain access is part of the protocol and should not be represented as an untouched-query inductive test. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: overall loss, source classification loss, domain-adversarial loss and Parameter settings; cached paragraphs 60–69 Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table Version: version of record | unreported automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
2 source records and release history
- Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Original source · version of record
- Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-d82b6284f3f431
- areas
- cells-tissues
- tasks
- Cross-platform scATAC cell-type annotation
- entity level
- task
- version
- Not reported
- task
- Cross-platform scATAC cell-type annotation
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_table_screened; primary sources: expansion-p3-scatac-llmda-2026; inspected locators: Tables 2–3; Methods: Benchmark methods; searched queries: "PMC13132462"; gaps: Reference-to-query direction is part of protocol identity; reversed pairs are not replicates.; Inputs and adaptation differ across baselines; scJoint uses scATAC at both stages and Cellcano follows its original target-size-dependent rounds.; No uncertainty in tables; F1 and accuracy printed separately, including slash-joined Table 3 cells.; claim scope: Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
- historical missing metadata
- protocol version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- benchmark
- entity classification
- review date: 2026-09-17; rationale: This source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: scatac-llmda-2026; source locator: Methods: Benchmark datasets; Benchmark methods; Problem definition; cached text lines 9–17; task metric definitions and corresponding results table; ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.