Model type
Graph-based predictive method; this record is the paper-specific evaluated configuration.
Pipeline
scLLMDA
scLLMDA annotates scATAC-seq cells using peak-sequence embeddings and graph-based domain adaptation.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Discussion (paragraph 1); Introduction (paragraph 4)How it worksEvaluated procedure (conceptual)
Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1)At a glance
Biological inputs
scATAC-seq accessibility values, peak DNA sequences and labelled reference cells
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Feature extraction from genomic sequences (paragraph 5); Materials and methods/Feature extraction from genomic sequences (paragraph 4)Outputs
Cell-type annotations in a target scATAC-seq dataset
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Problem definition (paragraph 1); Results/Effectiveness of GDA module (paragraph 3)limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| scLLMDA: Cross-platform scATAC cell-type annotation Pipeline: scLLMDATask: Cross-platform scATAC cell-type annotationDataset: MosA1 reference → WholeBrainA query Cross-platform reference-query cell-type annotation. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.6525 F1 Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Table 2, scLLMDA row, Ref: MosA1 / Q: WholeBrainA F1 column Source checking is not independent reproduction. |
How it works
How the evaluated method works
A pretrained DNA model encodes peak sequences; accessibility values form cell representations. Source and target similarity graphs enter a graph neural network that aligns domains while retaining local neighbourhoods.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1)What was evaluated
The linked evaluation record identifies scLLMDA: Cross-platform scATAC cell-type annotation. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b3-005Strengths and limitations
Strengths and considerations
- Uses both sequence features and cell-neighbourhood context for transfer within the ATAC modality.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention. (paragraph 6); Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention. (paragraph 1)
Limitations and conditions
- Access to target-cell structure is part of the adaptation protocol; it is not equivalent to a purely inductive sequence-only test.
Sources
Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Parameter settings (paragraph 1); Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention. (paragraph 1)
Profile review details
Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.
Stable record: reported-model-a0db32ae53e5edSpecifications
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 |
|---|---|
| Model type | Graph-based predictive method; this record is the paper-specific evaluated configuration.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) |
| Architecture / procedure | A pretrained DNA model encodes peak sequences; accessibility values form cell representations. Source and target similarity graphs enter a graph neural network that aligns domains while retaining local neighbourhoods.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) |
| Biological inputs | scATAC-seq accessibility values, peak DNA sequences and labelled reference cellsSourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Feature extraction from genomic sequences (paragraph 5); Materials and methods/Feature extraction from genomic sequences (paragraph 4) |
| Outputs | Cell-type annotations in a target scATAC-seq datasetSourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Problem definition (paragraph 1); Results/Effectiveness of GDA module (paragraph 3) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation; sheng-guan-2001/scLLMDA README.md · Materials and methods/Benchmark datasets; Materials and methods/Benchmark methods; Materials and methods/Problem definition; Materials and methods/Feature extraction from genomic sequences; Materials and methods/Cell type annotation via graph domain adaptation/Graph construction.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the global consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | scLLMDA is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Cross-dataset cell annotation uses mouse snATAC-seq/ATAC-seq datasets GSE126724 and GSE111586 plus10xMouseBrain; assemblies differ (GRCm38/mm10 versus mm9) and remain dataset-specific.SourcesCell type annotation for scATAC-seq via DNA large language model and graph domain adaptation · Materials and methods/Benchmark datasets (paragraph 1); Results/Cross-platform cell type annotation (paragraph 1) |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation; sheng-guan-2001/scLLMDA README.md · Materials and methods/Benchmark datasets; Materials and methods/Benchmark methods; Materials and methods/Problem definition; Materials and methods/Feature extraction from genomic sequences; Materials and methods/Cell type annotation via graph domain adaptation/Graph construction.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the global consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention.; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official study implementation and usage documentation: https://github.com/sheng-guan-2001/scLLMDA/blob/5e24025710bb068312d50a5749ef6bb838ef5a32/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcessheng-guan-2001/scLLMDA README.md · README.md; installation, model download and usage instructions |
| Code licence | No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sourcesSourcessheng-guan-2001/scLLMDA README.md · README.md and repository-root licence-file search |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcessheng-guan-2001/scLLMDA README.md · README.md; checkpoint/access documentation and licence scope |
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.
21 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["scATAC-seq accessibility values, peak DNA sequences and labelled reference cells","scLLMDA","Cell-type annotations in a target scATAC-seq dataset"] Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Graph-based predictive method; this record is the paper-specific evaluated configuration. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure A pretrained DNA model encodes peak sequences; accessibility values form cell representations. Source and target similarity graphs enter a graph neural network that aligns domains while retaining local neighbourhoods. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | sheng-guan-2001/scLLMDA README.md README.md; checkpoint/access documentation and licence scope Version: 5e24025710bb068312d50a5749ef6bb838ef5a32 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs scATAC-seq accessibility values, peak DNA sequences and labelled reference cells Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Feature extraction from genomic sequences (paragraph 5); Materials and methods/Feature extraction from genomic sequences (paragraph 4) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Cell-type annotations in a target scATAC-seq dataset Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Problem definition (paragraph 1); Results/Effectiveness of GDA module (paragraph 3) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | sheng-guan-2001/scLLMDA README.md Materials and methods/Benchmark datasets; Materials and methods/Benchmark methods; Materials and methods/Problem definition; Materials and methods/Feature extraction from genomic sequences; Materials and methods/Cell type annotation via graph domain adaptation/Graph construction.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the global consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 5e24025710bb068312d50a5749ef6bb838ef5a32 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation Materials and methods/Benchmark datasets; Materials and methods/Benchmark methods; Materials and methods/Problem definition; Materials and methods/Feature extraction from genomic sequences; Materials and methods/Cell type annotation via graph domain adaptation/Graph construction.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Capture the global consistency relationship of each graph.; Materials and methods/Cell type annotation via graph domain adaptation/Feature fusion via attention.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: version of record | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · 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
- sheng-guan-2001/scLLMDA README.md · Original source · 5e24025710bb068312d50a5749ef6bb838ef5a32
Technical metadata and extraction receipts
Stable ID: reported-model-a0db32ae53e5ed
- areas
- cells-tissues
- entity level
- method
- version
- Not reported
- reported name
- scLLMDA
- historical missing metadata
- version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: 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
- model
- entity classification
- review date: 2026-09-17; rationale: The source identifies three stages: DNABERT-2 sequence feature extraction, cell embedding learning, and graph construction with domain adaptation. Preserve the exact source-scoped composition and its results; no additional checkpoint or family equivalence is inferred.; source ids: scatac-llmda-2026; source locator: Materials and methods/Cell type annotation via graph domain adaptation/Capture the local consistency relationship of each graph. (paragraph 4); Materials and methods/Loss function (paragraph 1) | Discussion (paragraph 1); Introduction (paragraph 4) | Materials and methods: Feature extraction from genomic sequences; Cell type annotation via graph domain adaptation; Results: Computational efficiency and scalability; ambiguities: This is the paper-specific pipeline identity. Missing component versions or checkpoint hashes remain unknown; a shared upstream name does not establish equivalent pipelines.