Seurat reference mapping (SCTransform)
Seurat reference mapping is a cell type label transfer method provided by the Seurat package. Gene expression counts are first normalised by SCTransform before computing PCA. Then it finds mutual nearest neighbours, known as transfer anchors, between the labelled and unlabelled part of the data in PCA space, and computes each cell’s distance to each of the anchor pairs. Finally, it uses the labelled anchors to predict cell types for unlabelled cells based on these distances.
Overview
Seurat reference mapping is a cell type label transfer method provided by the Seurat package. Gene expression counts are first normalised by SCTransform before computing PCA. Then it finds mutual nearest neighbours, known as transfer anchors, between the labelled and unlabelled part of the data in PCA space, and computes each cell’s distance to each of the anchor pairs. Finally, it uses the labelled anchors to predict cell types for unlabelled cells based on these distances.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
Release 2026-09-17-134cd1815de8 · 24 evaluations · 24 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 |
|---|---|---|
| Seurat reference mapping (SCTransform) on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), AccuracyDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8478130617136009 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 scoreDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8432431418068973 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 scoreDataset subset: CeNGEN (split by batch) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.4796671443590633 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), AccuracyDataset subset: CeNGEN (random split) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8321474453997431 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 scoreDataset subset: CeNGEN (random split) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.830794751253811 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 scoreDataset subset: CeNGEN (random split) (Open Problems label projection split) 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.7342634929447799 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(cengen_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) 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. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 scoreDataset 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 | ||
| 0.9582796607282135 f1 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(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 scoreDataset 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 | ||
| 0.8517420844591291 f1-macro 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(f1_macro) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), AccuracyDataset subset: Pancreas (random split) (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 randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.982370820668693 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 scoreDataset subset: Pancreas (random split) (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 randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9816543811179429 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 scoreDataset subset: Pancreas (random split) (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 randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8948274458832942 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), AccuracyDataset subset: Pancreas (random split with label noise) (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 randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9756548536209553 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 scoreDataset subset: Pancreas (random split with label noise) (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 randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.975085587274387 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 scoreDataset subset: Pancreas (random split with label noise) (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 randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8955432565937734 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), AccuracyDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.9119358074222668 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 scoreDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.909781894158941 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 scoreDataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split) All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8526389520351096 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), AccuracyDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.3442973919813157 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 scoreDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.41030222359279184 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 scoreDataset subset: Zebrafish (by laboratory) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.28347167382711347 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), AccuracyDataset subset: Zebrafish (random split) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8596287703016241 accuracy Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(seurat_reference_mapping), paramset(SCTransform), metric(accuracy) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 scoreDataset subset: Zebrafish (random split) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.8577182706160404 f1 Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1) Source checking is not independent reproduction. |
| Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Configuration: Seurat reference mapping (SCTransform)Task: Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 scoreDataset subset: Zebrafish (random split) (Open Problems label projection split) 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Author-reported evaluation · Evaluation metadata: source checked | ||
| 0.7417937504863691 f1-macro Unit: fraction · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedopenproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(seurat_reference_mapping), paramset(SCTransform), metric(f1_macro) Source checking is not independent reproduction. |
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Related records
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- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy
- model: Seurat reference mapping (SCTransform) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy
- model: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy
- model: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score
- model: Seurat reference mapping (SCTransform) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score