Quality control

Summary

Task ✗✗ ✗✗✗
batch integration embed 837
batch integration feature 546
batch integration graph 438
cell cell communication ligand target 109
cell cell communication source target 109
denoising 84 1 1
dimensionality reduction 553 26 6 1
label projection 149
matching modalities 66
task perturbation prediction 161 2
task spatial decomposition 61 2 4 2
task spatially variable genes 105 17 1 1

Detailed

Tip

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Task Category Name Value Condition Severity
denoising Scaling Worst score knn_smoothing poisson -10.2983151 worst_score >= -1 ✗✗✗
task spatially variable genes Raw results Method ‘boostgp’ %missing 0.7800000 pct_missing <= .1 ✗✗✗
dimensionality reduction Raw results Dataset ‘zebrafish_labs’ %missing 0.6000000 pct_missing <= .1 ✗✗✗
task spatial decomposition Scaling Worst score cell2location r2 -3.3126000 worst_score >= -1 ✗✗✗
task spatial decomposition Scaling Worst score seurat r2 -3.1724000 worst_score >= -1 ✗✗✗
task perturbation prediction Dataset info Pct ‘task_id’ missing 1.0000000 percent_missing(dataset_info, field) ✗✗
task spatial decomposition Dataset info Pct ‘task_id’ missing 1.0000000 percent_missing(dataset_info, field) ✗✗
task perturbation prediction Method info Pct ‘paper_reference’ missing 0.5000000 percent_missing(method_info, field) ✗✗
task spatial decomposition Method info Pct ‘paper_reference’ missing 0.8181818 percent_missing(method_info, field) ✗✗
task spatial decomposition Scaling Worst score nnls r2 -2.8586000 worst_score >= -1 ✗✗
task spatial decomposition Scaling Worst score rctd r2 -2.5379000 worst_score >= -1 ✗✗
task spatially variable genes Raw results Dataset ‘zenodo_spatial/merfish/mouse_cortex’ %missing 0.2500000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘continuity’ %missing 0.2500000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘lcmc’ %missing 0.2500000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qglobal’ %missing 0.2500000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qlocal’ %missing 0.2500000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qnn’ %missing 0.2500000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qnn_auc’ %missing 0.2500000 pct_missing <= .1 ✗✗
denoising Scaling Worst score alra_sqrt poisson -2.3012026 worst_score >= -1 ✗✗
task spatially variable genes Raw results Dataset ‘zenodo_spatial/seqfish/mouse_organogenesis_seqfish’ %missing 0.1875000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/slideseqv2/mouse_cortex’ %missing 0.1875000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/stereoseq/drosophila_embryo_e6_3’ %missing 0.1875000 pct_missing <= .1
task spatial decomposition Scaling Worst score nmfreg r2 -1.8296000 worst_score >= -1
task spatially variable genes Raw results Method ‘spark’ %missing 0.1600000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_pca_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_pca_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘diffusion_map’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘neuralee_default’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘neuralee_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pca_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pca_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_default’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_sqrt’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_distances_log_cp10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_distances_log_cp10k_hvg’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_neighbors_log_cp10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_neighbors_log_cp10k_hvg’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘random_features’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘spectral_features’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘true_features’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘tsne_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘tsne_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_pca_logCP10k’ %missing 0.1500000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_pca_logCP10k_1kHVG’ %missing 0.1500000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘tenx_visium/visium/human_brain_cancer’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘tenx_visium/visium/human_normal_prostate’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/slideseqv2/mouse_cerebellum’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/slideseqv2/mouse_hippocampus_puck’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/slideseqv2/mouse_olfactory_bulb_puck’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/slideseqv2/mouse_somatosensory_cortex_puck’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/starmap/mouse_brain_2d_zstep10_0’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/starmap/mouse_brain_2d_zstep15_0’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/stereoseq/drosophila_embryo_e10’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/stereoseq/drosophila_embryo_e5_6’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/stereoseq/drosophila_embryo_e7’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Dataset ‘zenodo_spatial/stereoseq/drosophila_embryo_e9_1’ %missing 0.1250000 pct_missing <= .1
task spatially variable genes Raw results Method ‘somde’ %missing 0.1200000 pct_missing <= .1
task spatial decomposition Scaling Worst score destvi r2 -1.1763000 worst_score >= -1