Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
Label projection on Pancreas (by batch), Accuracy. Scored with Accuracy on Pancreas (by batch). Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type).
Overview
Label projection on Pancreas (by batch), Accuracy. Scored with Accuracy on Pancreas (by batch). Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type).
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluation design
Benchmarks bring together tasks and protocols. A task describes the biological question; a protocol defines a particular test.
Benchmarks
These source-backed links do not make different protocols or scores interchangeable.
Recorded evaluations
Each evaluation records what was tested and under which conditions.
- K-neighbors classifier (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- Logistic regression (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- Majority Vote on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- Random Labels on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- scANVI (All genes) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- scANVI (Seurat v3 2000 HVG) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- scArches+scANVI (All genes) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- scArches+scANVI (Seurat v3 2000 HVG) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
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.
Published comparisons
Explore the results reported under one evaluation protocol. Each figure keeps its source, dataset and metric together; it is not a ranking across studies. The pooled view gathers every source table that reports the same metric and names what it does not hold constant.
Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
accuracy (fraction) · Higher values are better for this metric.
Every method Open Problems label projection reports on Label projection on Pancreas (by batch), Accuracy, scored with Accuracy on Pancreas (by batch).
Evaluation protocol · Pancreas (by batch) (Open Problems label projection split)
- Majority Vote · Configuration · Author-reported evaluation0.34947254530700567
- Multilayer perceptron (log CP10k) · Configuration · Author-reported evaluation0.9640248850419258
- Logistic regression (log CP10k) · Configuration · Author-reported evaluation0.9634839058696241
- True Labels · Method · Author-reported evaluation1
- K-neighbors classifier (log CP10k) · Configuration · Author-reported evaluation0.874492832025967
- Random Labels · Method · Author-reported evaluation0.21341628347308628
- XGBoost (log CP10k) · Configuration · Author-reported evaluation0.93913984311604
- K-neighbors classifier (log scran) · Configuration · Author-reported evaluation0.8290505815526102
- Logistic regression (log scran) · Configuration · Author-reported evaluation0.9418447389775494
- XGBoost (log scran) · Configuration · Author-reported evaluation0.9342710305653232
- Multilayer perceptron (log scran) · Configuration · Author-reported evaluation0.9537462807681905
- Seurat reference mapping (SCTransform) · Configuration · Author-reported evaluation0.9588855829050582
- scANVI (Seurat v3 2000 HVG) · Configuration · Author-reported evaluation0.9596970516635109
- scArches+scANVI (Seurat v3 2000 HVG) · Configuration · Author-reported evaluation0.9540167703543414
- scArches+scANVI (All genes) · Configuration · Author-reported evaluation0.9507708953205302
- scANVI (All genes) · Configuration · Author-reported evaluation0.9575331349743035
Source order is preserved. Plotted marks show point estimates; uncertainty, where reported, is retained in the printed values and table. Differences do not establish statistical significance.
openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), metric(accuracy)Values, uncertainty and evidence
| Tested entity | Printed value | Uncertainty | Evidence |
|---|---|---|---|
| Majority Vote · Configuration | 0.34947254530700567 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(majority_vote), paramset(none), metric(accuracy) |
| Multilayer perceptron (log CP10k) · Configuration | 0.9640248850419258 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) |
| Logistic regression (log CP10k) · Configuration | 0.9634839058696241 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log CP10k), metric(accuracy) |
| True Labels · Method | 1 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(true_labels), paramset(none), metric(accuracy) |
| K-neighbors classifier (log CP10k) · Configuration | 0.874492832025967 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log CP10k), metric(accuracy) |
| Random Labels · Method | 0.21341628347308628 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(random_labels), paramset(none), metric(accuracy) |
| XGBoost (log CP10k) · Configuration | 0.93913984311604 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log CP10k), metric(accuracy) |
| K-neighbors classifier (log scran) · Configuration | 0.8290505815526102 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log scran), metric(accuracy) |
| Logistic regression (log scran) · Configuration | 0.9418447389775494 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log scran), metric(accuracy) |
| XGBoost (log scran) · Configuration | 0.9342710305653232 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log scran), metric(accuracy) |
| Multilayer perceptron (log scran) · Configuration | 0.9537462807681905 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log scran), metric(accuracy) |
| Seurat reference mapping (SCTransform) · Configuration | 0.9588855829050582 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) |
| scANVI (Seurat v3 2000 HVG) · Configuration | 0.9596970516635109 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scanvi), paramset(Seurat v3 2000 HVG), metric(accuracy) |
| scArches+scANVI (Seurat v3 2000 HVG) · Configuration | 0.9540167703543414 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scarches_scanvi), paramset(Seurat v3 2000 HVG), metric(accuracy) |
| scArches+scANVI (All genes) · Configuration | 0.9507708953205302 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scarches_scanvi), paramset(All genes), metric(accuracy) |
| scANVI (All genes) · Configuration | 0.9575331349743035 fraction | Not reported | Author-reported evaluation · source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scanvi), paramset(All genes), metric(accuracy) |
Scope and limitations
- Results published by the Open Problems project, source checked but not independently reproduced.
- This covers the label projection task at v1.0.0 only, not the whole Open Problems suite.
- true_labels and random_labels are controls that bound the scale, not competing methods.
- Preprocessing is part of the run, so the same method appears once per parameter set.
Source transcription and grouping reviewed by automated source review on 2026-09-18. These experiments were not independently reproduced by rewire.
Tested entities and results
Release 2026-09-17-134cd1815de8 · 16 evaluations · 16 metric rows. Different protocols are not a single leaderboard. Where several source tables report the same metric, the published comparisons above offer a pooled view that names what it does not hold constant.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| K-neighbors classifier (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: K-neighbors classifier (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.874492832025967 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: K-neighbors classifier (log scran)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8290505815526102 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log scran), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Logistic regression (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9634839058696241 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Logistic regression (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Logistic regression (log scran)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9418447389775494 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(logistic_regression), paramset(log scran), metric(accuracy) Source checking is not independent reproduction. |
| Majority Vote on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Majority VoteTask: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.34947254530700567 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(majority_vote), paramset(none), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Multilayer perceptron (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9640248850419258 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Multilayer perceptron (log scran)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9537462807681905 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(multilayer_perceptron), paramset(log scran), metric(accuracy) Source checking is not independent reproduction. |
| Random Labels on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Method: Random LabelsTask: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.21341628347308628 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(random_labels), paramset(none), metric(accuracy) Source checking is not independent reproduction. |
| scANVI (All genes) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: scANVI (All genes)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9575331349743035 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scanvi), paramset(All genes), metric(accuracy) Source checking is not independent reproduction. |
| scANVI (Seurat v3 2000 HVG) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: scANVI (Seurat v3 2000 HVG)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9596970516635109 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scanvi), paramset(Seurat v3 2000 HVG), metric(accuracy) Source checking is not independent reproduction. |
| scArches+scANVI (All genes) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: scArches+scANVI (All genes)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9507708953205302 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scarches_scanvi), paramset(All genes), metric(accuracy) Source checking is not independent reproduction. |
| scArches+scANVI (Seurat v3 2000 HVG) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: scArches+scANVI (Seurat v3 2000 HVG)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9540167703543414 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(scarches_scanvi), paramset(Seurat v3 2000 HVG), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9588855829050582 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| True Labels on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Method: True LabelsTask: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 1 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(true_labels), paramset(none), metric(accuracy) Source checking is not independent reproduction. |
| XGBoost (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: XGBoost (log CP10k)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.93913984311604 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log CP10k), metric(accuracy) Source checking is not independent reproduction. |
| XGBoost (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Configuration: XGBoost (log scran)Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), AccuracyDataset subset: Pancreas (by batch) (Open Problems label projection split) Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9342710305653232 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(xgboost), paramset(log scran), metric(accuracy) Source checking is not independent reproduction. |
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.
1 evidence row matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Relationship: part of discovery-benchmark-open-problems Individual claims | openproblems-label primary benchmark evidence results, dataset(pancreas_batch), metric(accuracy) Version: v1.0.0 | source checked automated source review · 2026-09-18 Audit detailsPrimary-source transcription with no human sign-off and no independent reproduction. Field: Claim: open-problems-association-pancreas-batch-accuracy Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
Sources and history
Release 2026-09-17-134cd1815de8 · Record review: source checked
1 source records and release history
Download this releaseTechnical metadata and extraction receipts
Stable ID: open-problems-task-pancreas-batch-accuracy
- areas
- cells-tissues
- tasks
- Label projection on Pancreas (by batch), Accuracy
- metric
- Accuracy
- metric direction
- higher
- dataset
- Pancreas (by batch)
- protocol
- Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type).
- source locator
- results, dataset(pancreas_batch), metric(accuracy)
- comparison panels
- id: open-problems-panel-pancreas-batch-accuracy; title: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy; protocol id: open-problems-task-pancreas-batch-accuracy; dataset id: open-problems-dataset-pancreas-by-batch; metric: accuracy; unit: fraction; direction: higher; result ids: open-problems-result-majority-vote-pancreas-batch-accuracy-accuracy; open-problems-result-multilayer-perceptron-log-cp10k-pancreas-batch-accuracy-accuracy; open-problems-result-logistic-regression-log-cp10k-pancreas-batch-accuracy-accuracy; open-problems-result-true-labels-pancreas-batch-accuracy-accuracy; open-problems-result-k-neighbors-classifier-log-cp10k-pancreas-batch-accuracy-accuracy; open-problems-result-random-labels-pancreas-batch-accuracy-accuracy; open-problems-result-xgboost-log-cp10k-pancreas-batch-accuracy-accuracy; open-problems-result-k-neighbors-classifier-log-scran-pancreas-batch-accuracy-accuracy; open-problems-result-logistic-regression-log-scran-pancreas-batch-accuracy-accuracy; open-problems-result-xgboost-log-scran-pancreas-batch-accuracy-accuracy; open-problems-result-multilayer-perceptron-log-scran-pancreas-batch-accuracy-accuracy; open-problems-result-seurat-reference-mapping-sctransform-pancreas-batch-accuracy-accuracy; open-problems-result-scanvi-seurat-v3-2000-hvg-pancreas-batch-accuracy-accuracy; open-problems-result-scarches-plus-scanvi-seurat-v3-2000-hvg-pancreas-batch-accuracy-accuracy; open-problems-result-scarches-plus-scanvi-all-genes-pancreas-batch-accuracy-accuracy; open-problems-result-scanvi-all-genes-pancreas-batch-accuracy-accuracy; source ids: expansion-p3-open-problems; source locator: results, dataset(pancreas_batch), metric(accuracy); context: Every method Open Problems label projection reports on Label projection on Pancreas (by batch), Accuracy, scored with Accuracy on Pancreas (by batch).; caveats: Results published by the Open Problems project, source checked but not independently reproduced.; This covers the label projection task at v1.0.0 only, not the whole Open Problems suite.; true_labels and random_labels are controls that bound the scale, not competing methods.; Preprocessing is part of the run, so the same method appears once per parameter set.; review: method: automated_source_review; date: 2026-09-18
Related records
- part of: Open Problems
- subject: Open Problems label projection PANCREAS-BATCH-ACCURACY: part of discovery-benchmark-open-problems
- benchmark: K-neighbors classifier (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Logistic regression (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Logistic regression (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Majority Vote on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Multilayer perceptron (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Multilayer perceptron (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Random Labels on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: scANVI (All genes) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: scANVI (Seurat v3 2000 HVG) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: scArches+scANVI (All genes) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: scArches+scANVI (Seurat v3 2000 HVG) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: True Labels on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: XGBoost (log CP10k) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- benchmark: XGBoost (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy