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
spatial decomposition 77 1 1

Detailed

Tip

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Task Category Name Value Condition Severity
denoising Scaling Worst score knn_smoothing poisson -10.298315 worst_score >= -1 ✗✗✗
dimensionality reduction Raw results Dataset ‘zebrafish_labs’ %missing 0.600000 pct_missing <= .1 ✗✗✗
spatial decomposition Scaling Worst score seuratv3 r2 -4.847695 worst_score >= -1 ✗✗✗
spatial decomposition Scaling Worst score tangram r2 -2.638332 worst_score >= -1 ✗✗
dimensionality reduction Raw results Metric ‘continuity’ %missing 0.250000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘lcmc’ %missing 0.250000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qglobal’ %missing 0.250000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qlocal’ %missing 0.250000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qnn’ %missing 0.250000 pct_missing <= .1 ✗✗
dimensionality reduction Raw results Metric ‘qnn_auc’ %missing 0.250000 pct_missing <= .1 ✗✗
denoising Scaling Worst score alra_sqrt poisson -2.301203 worst_score >= -1 ✗✗
dimensionality reduction Raw results Method ‘densmap_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_pca_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘densmap_pca_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘diffusion_map’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘neuralee_default’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘neuralee_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pca_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pca_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_default’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘phate_sqrt’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_distances_log_cp10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_distances_log_cp10k_hvg’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_neighbors_log_cp10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘pymde_neighbors_log_cp10k_hvg’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘random_features’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘spectral_features’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘true_features’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘tsne_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘tsne_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_pca_logCP10k’ %missing 0.150000 pct_missing <= .1
dimensionality reduction Raw results Method ‘umap_pca_logCP10k_1kHVG’ %missing 0.150000 pct_missing <= .1