Configuration

K-neighbors classifier (log scran)

K-neighbors classifier uses the "k-nearest neighbours" approach, which is a popular machine learning algorithm for classification and regression tasks. The assumption underlying KNN in this context is that cells with similar gene expression profiles tend to belong to the same cell type. For each unlabelled cell, this method computes the $k$ labelled cells (in this case, 5) with the smallest distance in PCA space, and assigns that cell the most common cell type among its $k$ nearest neighbors.

24 evaluations · 24 results

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Evaluations and results

24 evaluations · 24 results. Different protocols are not a single leaderboard.

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.787 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.805 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.351 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.838 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.837 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.75 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.829 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.832 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.577 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.964 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.964 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.772 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.941 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.94 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.722 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.869 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.864 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.828 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.191 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.202 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.2 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.802 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(k_neighbors_classifier), paramset(log scran), metric(accuracy)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.803 f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(k_neighbors_classifier), paramset(log scran), metric(f1)
Configuration: K-neighbors classifier (log scran)	Task: Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.649 f1-macro fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source K-neighbors classifier (log scran) on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score 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). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(k_neighbors_classifier), paramset(log scran), metric(f1_macro)

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