Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Task
PBMC cell-type classification
PBMC cell classification assesses predictive performance and resource use under a common computing environment.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24At a glance
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 | PBMC bacterial-sepsis data are used for the principal all-gene comparison.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Splits | Input cells are divided into training, validation and testing subsets, with dataset sizes in Table 1. The Methods do not specify donor-disjoint grouping for the PBMC comparison.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Table 1; Methods: Data processing and Training |
| Metrics | Accuracy and, in broader experiments, precision, recall and F1, alongside memory/runtime.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Baselines | scVI/scANVI, SingleCellNet, CellTypist, ACTINN and devCellPy.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Leakage controls | Feature-selection models use training data and validation data; the final classifier is selected with validation performance. The inspected PBMC comparison and data-processing sections do not specify donor-disjoint or batch-disjoint partitions, so a cell split cannot be treated as a held-out-donor experiment. · Not reported in inspected sourcesSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods: Data processing, Feature extraction, Training and tool comparisons; cached paragraphs 63–82 |
| Uncertainty | The PBMC cell-type comparison reports classification metrics, but its results, figure captions and comparison methods do not define repeated-seed dispersion or a donor/sample-level confidence-interval procedure for those metrics. · Not reported in inspected sourcesSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · PBMC comparison results; associated figure captions; Methods: comparison of scaLR with other pipelines |
| Entity type | Paper-specific computational evaluation protocol.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Organisms | Human PBMCs.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Assays | Single-cell bacterial-sepsis expression with cell-type annotations.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Allowed inputs | Single-cell gene-expression profiles.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 |
| Adaptation | Supervised neural-network cell-type classification; validation data select the training checkpoint.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods: Feature extraction and Training |
How it works
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24Evaluation methodology
PBMC bacterial-sepsis data are used for the principal all-gene comparison. Accuracy and, in broader experiments, precision, recall and F1, alongside memory/runtime. scVI/scANVI, SingleCellNet, CellTypist, ACTINN and devCellPy.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24Recorded evaluations
Each evaluation records what was tested and under which conditions.
Tested entities and results
Release 2026-09-17-d277315f7d76 · 2 evaluations · 2 metric rows. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| scaLR: PBMC cell-type classification All features and samples from PBMCs-BS. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.942 Cell-type accuracy Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Table 2, scaLR row, Cell type Accuracy column Source checking is not independent reproduction. |
| scVI + scANVI: PBMC cell-type classification All features and samples from PBMCs-BS; comparison pipeline combines scVI and scANVI. Independent external evaluation · Evaluation metadata: needs review | ||
| 0.939 Cell-type accuracy Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Table 2, Svi-tools (scVI & scANVI) row, Cell type Accuracy column Source checking is not independent reproduction. |
Papers and result coverage
Last literature check: 2026-09-17. Primary-source discovery and table/protocol screening; source checked is not independently reproduced. Raw acquisitions not automatically numerical publication approval.
| Paper or primary resource | Version | Reference |
|---|---|---|
| scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery | version of record | Read source DOI: 10.1093/bib/bbaf243 |
What is still missing
- Complete raw table acquired. Celltype accuracy is distinct from cellstate and time/memory. Time formatting and hardware need review before efficiency comparisons; no new timing claim accepted. Structured extraction pending.
Search and extraction details
source found structured extraction pending
Searches
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery primary paper benchmark results
Evidence locations
- Table2 PBMC celltype versuscellstate blocks
Strengths and limitations
Limitations and conditions
- The paper contains separate cell-state and cell-type outcomes and larger cross-cohort evaluations. These should not be merged into one PBMC protocol.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24
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-b46b7b839bff93Evidence 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 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 ["Allowed inputs: Single-cell gene-expression profiles.","Datasets: PBMC bacterial-sepsis data are used for the principal all-gene comparison.","Metrics: Accuracy and, in broader experiments, precision, recall and F1, alongside memory/runtime."] Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 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 PBMC bacterial-sepsis data are used for the principal all-gene comparison. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 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 Input cells are divided into training, validation and testing subsets, with dataset sizes in Table 1. The Methods do not specify donor-disjoint grouping for the PBMC comparison. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Table 1; Methods: Data processing and Training 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 Supervised neural-network cell-type classification; validation data select the training checkpoint. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods: Feature extraction and Training 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, in broader experiments, precision, recall and F1, alongside memory/runtime. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 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 scVI/scANVI, SingleCellNet, CellTypist, ACTINN and devCellPy. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24 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 Feature-selection models use training data and validation data; the final classifier is selected with validation performance. The inspected PBMC comparison and data-processing sections do not specify donor-disjoint or batch-disjoint partitions, so a cell split cannot be treated as a held-out-donor experiment. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods: Data processing, Feature extraction, Training and tool comparisons; cached paragraphs 63–82 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 |
| Uncertainty The PBMC cell-type comparison reports classification metrics, but its results, figure captions and comparison methods do not define repeated-seed dispersion or a donor/sample-level confidence-interval procedure for those metrics. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery PBMC comparison results; associated figure captions; Methods: comparison of scaLR with other pipelines 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
Release 2026-09-17-d277315f7d76 · Record review: needs review
2 source records and release history
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Original source · version of record
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-b46b7b839bff93
- areas
- cells-tissues
- tasks
- PBMC cell-type classification
- entity level
- task
- version
- Not reported
- task
- PBMC cell-type classification
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: source_found_structured_extraction_pending; primary sources: evidence-expansion-p2-scalr-2025-829afab6a4e3; inspected locators: Table2 PBMC celltype versuscellstate blocks; searched queries: scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery primary paper benchmark results; gaps: Complete raw table acquired. Celltype accuracy is distinct from cellstate and time/memory. Time formatting and hardware need review before efficiency comparisons; no new timing claim accepted. Structured extraction pending.; claim scope: Primary-source discovery and table/protocol screening; source checked is not independently reproduced. Raw acquisitions not automatically numerical publication approval.
- 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: scalr-2025; source locator: Results: Performance evaluation using all genes; robustness evaluations; cached text lines 13–14, 22–24; 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.
Related records
- dataset: PBMCs-BS
- benchmark: scaLR: PBMC cell-type classification
- benchmark: scVI + scANVI: PBMC cell-type classification