8,569 cells, 17,499 genes, 14 cell populations (13 after excluding populations with fewer than 10 cells across the dataset), inDrop protocol, human pancreas (Baron et al., ref. [30] in the source). This is the Table 2 dataset-size figure, not independently confirmed as the exact scored denominator used for any individual classifier's median F1-score or unlabeled-percentage computation (per-fold or per-classifier exclusions, if any, are not detailed in the retrieved main text). Quoted verbatim, Table 2 footnotes: "a Used for intra-dataset evaluation" and "b Used for inter-dataset evaluation" -- this dataset carries both footnotes in Table 2, i.e. it is used in both evaluation designs in the source; only its intra-dataset use is in scope for the protocol linked here.
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4 evaluations · 7 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: scmapcell — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 0.984 median-f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceabdelaal baron human scmapcell Not reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, Results, "All classifiers perform well in intra-dataset experiments": "for the Baron Human dataset, the median F1-score for SVM rejection, scmapcell, scPred, and SVM is 0.991, 0.984, 0.981, and 0.980, respectively (Fig. 1a)." Fig. 1a, scmapcell row. |
| Configuration: scmapcell — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 4.2% pct-unlabeled percent · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceabdelaal baron human scmapcell Not reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, same location: "SVM rejection, scmapcell, and scPred assigned 1.5%, 4.2%, and 10.8% of the cells, respectively, as unlabeled while SVM (without rejection) classified 100% of the cells with a median F1-score of 0.98 (Fig. 1b)." Fig. 1b, scmapcell row. |
| Configuration: scPred — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 0.981 median-f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNot reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, Results, "All classifiers perform well in intra-dataset experiments": "for the Baron Human dataset, the median F1-score for SVM rejection, scmapcell, scPred, and SVM is 0.991, 0.984, 0.981, and 0.980, respectively (Fig. 1a)." Fig. 1a, scPred row. |
| Configuration: scPred — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 10.8% pct-unlabeled percent · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNot reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, same location: "SVM rejection, scmapcell, and scPred assigned 1.5%, 4.2%, and 10.8% of the cells, respectively, as unlabeled while SVM (without rejection) classified 100% of the cells with a median F1-score of 0.98 (Fig. 1b)." Fig. 1b, scPred row. |
| Configuration: SVM (no rejection) — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 0.98 median-f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceNot reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, Results, "All classifiers perform well in intra-dataset experiments": "for the Baron Human dataset, the median F1-score for SVM rejection, scmapcell, scPred, and SVM is 0.991, 0.984, 0.981, and 0.980, respectively (Fig. 1a)." Fig. 1a, SVM row. (Also restated as 0.98 alongside the unlabeled-percentage sentence, Fig. 1b.) |
| Configuration: SVMrejection — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 0.991 median-f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceabdelaal baron human svm rejection Not reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, Results, "All classifiers perform well in intra-dataset experiments": "for the Baron Human dataset, the median F1-score for SVM rejection, scmapcell, scPred, and SVM is 0.991, 0.984, 0.981, and 0.980, respectively (Fig. 1a)." Fig. 1a, SVM rejection row. |
| Configuration: SVMrejection — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 1.5% pct-unlabeled percent · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceabdelaal baron human svm rejection Not reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, same location: "SVM rejection, scmapcell, and scPred assigned 1.5%, 4.2%, and 10.8% of the cells, respectively, as unlabeled while SVM (without rejection) classified 100% of the cells with a median F1-score of 0.98 (Fig. 1b)." Fig. 1b, SVM rejection row. |
Source checking is not independent reproduction. Release 2026-10-06-161b59a1d02c.
A dataset supplies biological observations. The evaluation protocol defines how those observations are split, used and scored.
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
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.
10 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.assay_protocol inDrop Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.population.cell_populations_after_filter_lt_10_cells 13 Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.population.cell_populations_total 14 Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.population.cells 8569 Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.population.genes 17499 Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.population.population_scope Table 2 dataset-size figure; NOT independently confirmed as the exact scored denominator for any individual classifier's result. Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Table 2, row "Baron (Human)" Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.split Intra-dataset: stratified 5-fold cross-validation within this single dataset; training and testing folds identical across classifiers (Methods, "Intra-dataset classification", quoted: "we evaluated the performance by applying a 5-fold cross-validation across each dataset after filtering genes, cells, and small cell populations. The folds were divided in a stratified manner in order to keep equal proportions of each cell population in each fold. The training and testing folds were exactly the same for all classifiers."). This is NOT a cross-study or cross-platform split. Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description 8,569 cells, 17,499 genes, 14 cell populations (13 after excluding populations with fewer than 10 cells across the dataset), inDrop protocol, human pancreas (Baron et al., ref. [30] in the source). This is the Table 2 dataset-size figure, not independently confirmed as the exact scored denominator used for any individual classifier's median F1-score or unlabeled-percentage computation (per-fold or per-classifier exclusions, if any, are not detailed in the retrieved main text). Quoted verbatim, Table 2 footnotes: "a Used for intra-dataset evaluation" and "b Used for inter-dataset evaluation" -- this dataset carries both footnotes in Table 2, i.e. it is used in both evaluation designs in the source; only its intra-dataset use is in scope for the protocol linked here. Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) Context-only references | A comparison of automatic cell identification methods for single-cell RNA sequencing data Table 2, row "Baron (Human)" Version: 10.1186/s13059-019-1795-z; published article XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
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Stable ID: ucc-research-data-abdelaal-baron-human