Model type
Probabilistic single-cell model; this record is the paper-specific evaluated configuration.
Pipeline
scVI + scANVI
The scVI plus scANVI workflow is a probabilistic single-cell comparator in the scaLR study.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/scaLR performance comparison with different pipelines (paragraph 6); Results/Comparison of scaLR extracted top 3,500 features as input for different pipelines (paragraph 1)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
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (paragraph 3)At a glance
Biological inputs
Single-cell expression profiles and available cell labels
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Introduction (paragraph 1); Introduction (paragraph 4)Outputs
Latent cell representations and cell annotations
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/scaLR performance comparison with different pipelines (paragraph 6); Results/Performance and analysis cost comparison (paragraph 1)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 |
|---|---|---|
| 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. |
How it works
How the evaluated method works
Variational autoencoders learn low-dimensional cell representations; the scANVI stage uses annotation information in the combined workflow.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (paragraph 3)Underlying method and version boundaries
scvi-tools contains probabilistic models for single-cell analyses. scVI, scANVI and study-specific downstream heads are distinct procedures even when distributed through the same software package.
Sources
scverse/scvi-tools README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies scVI + scANVI: PBMC cell-type classification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b3-016Strengths and limitations
Strengths and considerations
- Provides an established probabilistic integration/annotation reference.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Introduction (paragraph 1); Introduction (paragraph 2)
Limitations and conditions
- This is a two-model workflow, not scVI alone; label availability and preprocessing must match the evaluated protocol.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results/Comparison of top-K differential gene expression features between full and test set samples (paragraph 2); Methods/scaLR performance comparison with different pipelines (paragraph 6)
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-1be5c4b7c52a41Specifications
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 | Probabilistic single-cell model; this record is the paper-specific evaluated configuration.Sourcesscverse/scvi-tools README.md · README.md model description |
| Architecture / procedure | Variational autoencoders learn low-dimensional cell representations; the scANVI stage uses annotation information in the combined workflow.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (paragraph 3) |
| Biological inputs | Single-cell expression profiles and available cell labelsSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Introduction (paragraph 1); Introduction (paragraph 4) |
| Outputs | Latent cell representations and cell annotationsSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/scaLR performance comparison with different pipelines (paragraph 6); Results/Performance and analysis cost comparison (paragraph 1) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery; scverse/scvi-tools README.md · Methods/Single-cell RNA-Seq data selection and download; Methods/The scaLR platform; Methods/The scaLR platform/Data processing; Methods/The scaLR platform/Feature extraction; Methods/The scaLR platform/Training; Methods/The scaLR platform/Evaluation and downstream analysis; Methods/scaLR performance comparison with different pipelines; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | Two network layers,30 latent dimensions, scVI 100 epochs and scANVI 25 epochs (Table 2 footnote b).SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Table2 footnote b |
| Training data / fitting | Fit the VAE and annotation model on the PBMCs-BS expression data: scVI 100 epochs and scANVI 25 epochs.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Table2 footnote b |
| Context limits | Table 2 uses all gene features and a 30-dimensional latent space; Table 3 separately uses the top 3,500 features.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Tables2–3; feature and latent-dimension settings |
| Access | Official upstream implementation and usage documentation: https://github.com/scverse/scvi-tools/blob/73b28e44223621470e582a81a102c107bb22678b/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesscverse/scvi-tools README.md · README.md; installation, model download and usage instructions |
| Code licence | BSD 3-Clause (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).Sourcesscverse/scvi-tools LICENSE · LICENSE; complete licence text |
| 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 sourcesSourcesscverse/scvi-tools 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (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 |
| Diagram steps ["Single-cell expression profiles and available cell labels","scVI + scANVI","Latent cell representations and cell annotations"] Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (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 |
| Diagram title Evaluated procedure (conceptual) Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (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 |
| Model type Probabilistic single-cell model; this record is the paper-specific evaluated configuration. Individual claims | scverse/scvi-tools README.md README.md model description Version: 73b28e44223621470e582a81a102c107bb22678b | 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 Variational autoencoders learn low-dimensional cell representations; the scANVI stage uses annotation information in the combined workflow. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (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 |
| 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 | scverse/scvi-tools README.md README.md; checkpoint/access documentation and licence scope Version: 73b28e44223621470e582a81a102c107bb22678b | 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 Single-cell expression profiles and available cell labels Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Introduction (paragraph 1); Introduction (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 Latent cell representations and cell annotations Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/scaLR performance comparison with different pipelines (paragraph 6); Results/Performance and analysis cost comparison (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 |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | scverse/scvi-tools README.md Methods/Single-cell RNA-Seq data selection and download; Methods/The scaLR platform; Methods/The scaLR platform/Data processing; Methods/The scaLR platform/Feature extraction; Methods/The scaLR platform/Training; Methods/The scaLR platform/Evaluation and downstream analysis; Methods/scaLR performance comparison with different pipelines; 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: 73b28e44223621470e582a81a102c107bb22678b | 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/Single-cell RNA-Seq data selection and download; Methods/The scaLR platform; Methods/The scaLR platform/Data processing; Methods/The scaLR platform/Feature extraction; Methods/The scaLR platform/Training; Methods/The scaLR platform/Evaluation and downstream analysis; Methods/scaLR performance comparison with different pipelines; 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
3 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
- scverse/scvi-tools README.md · Original source · 73b28e44223621470e582a81a102c107bb22678b
- scverse/scvi-tools LICENSE · Original source · 73b28e44223621470e582a81a102c107bb22678b
Technical metadata and extraction receipts
Stable ID: reported-model-1be5c4b7c52a41
- areas
- cells-tissues
- entity level
- method
- version
- Not reported
- reported name
- scVI + scANVI
- 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: This record identifies a composed analysis workflow with separately identifiable upstream models, representations or tools and a downstream prediction/scoring procedure. Results belong to that complete composition rather than to an upstream model alone.; source ids: scalr-2025; evidence-reported-base-scvi-readme-md; source locator: Methods/scaLR performance comparison with different pipelines (paragraph 6); Methods/scaLR performance comparison with different pipelines (paragraph 3) | README.md model description | Methods/scaLR performance comparison with different pipelines (paragraph 6); Results/Comparison of scaLR extracted top 3,500 features as input for different pipelines (paragraph 1); ambiguities: This is the paper-specific pipeline identity; unspecified component checkpoints or implementation versions are not inferred from its name.