diff --git a/.github/workflows/quarto_netlify.yml b/.github/workflows/quarto_netlify.yml index e8bf4be2..1d95acc1 100644 --- a/.github/workflows/quarto_netlify.yml +++ b/.github/workflows/quarto_netlify.yml @@ -107,6 +107,13 @@ jobs: dir: '_site' alias: "${{ env.BRANCH_NAME }}" message: 'Deploy preview ${{ github.ref }}' + + - name: Upload artifact + if: failure() + uses: actions/upload-artifact@v4 + id: upload-artifact + with: + name: _site - name: Comment on PR (success) uses: thollander/actions-comment-pull-request@v2 @@ -122,6 +129,8 @@ jobs: with: message: | [![Deploy: failure](https://img.shields.io/badge/Deploy-failure-critical)](${{ steps.deploy_preview.outputs.logs }}) + + Artifacts are available for download [here](${{ steps.upload-artifact.outputs.artifact_url }}) comment_tag: deploy_status - name: Comment on PR (actions failure) @@ -130,5 +139,6 @@ jobs: with: message: | [![Deploy: failure](https://img.shields.io/badge/Deploy-failure-critical)](https://github.com/${{github.repository}}/actions/runs/${{github.run_id}}/jobs/${{github.job}}) + + Artifacts are available for download [here](${{ steps.upload-artifact.outputs.artifact_url }}) comment_tag: deploy_status - \ No newline at end of file diff --git a/_publish.yml b/_publish.yml index 01f35491..a5c612a3 100644 --- a/_publish.yml +++ b/_publish.yml @@ -1,4 +1,4 @@ - source: project netlify: - id: 397b6416-708f-4133-afe9-9a07ed2e03bf - url: 'https://openproblems.bio' + url: 'https://openproblems.netlify.app' diff --git a/results/cyto_batch_integration/data/dataset_info.json b/results/cyto_batch_integration/data/dataset_info.json new file mode 100644 index 00000000..1b4f6782 --- /dev/null +++ b/results/cyto_batch_integration/data/dataset_info.json @@ -0,0 +1,12 @@ +[ + { + "dataset_id": "leomazzi_cyto_spleen", + "dataset_name": "Leomazzi Spleen Cytometry", + "dataset_summary": "Flow cytometry data of spleens of 8 mice. For each mouse, aliquotes of the same original sample were divided into 2 batches and measured with 2 different instrument settings to allow the creation of sample-paired replicates for benchmarking purposes.", + "dataset_description": "Flow cytometry data of spleens from 4 WT (IKK2 fl/fl CD11c-cre +/+) and 4 KO (IKK2 fl/fl CD11c-cre Tg/+) B6 mice, measured with a 22-color panel and 2 different instrument settings. Data has been preprocessed (compensated with a batch-specific compensation matrix, logicle transformed, cleaned with PeacoQC and pregated on live single CD45+ cells).", + "data_reference": null, + "data_url": "https://saeyslab.sites.vib.be/en", + "date_created": "23-05-2025", + "file_size": 489781536 + } +] diff --git a/results/cyto_batch_integration/data/method_info.json b/results/cyto_batch_integration/data/method_info.json new file mode 100644 index 00000000..e8343b10 --- /dev/null +++ b/results/cyto_batch_integration/data/method_info.json @@ -0,0 +1,194 @@ +[ + { + "task_id": "control_methods", + "method_id": "shuffle_integration", + "method_name": "Shuffle integration", + "method_summary": "Integrations are randomly permuted", + "method_description": "Integrations are randomly permuted", + "is_baseline": true, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/control_methods/shuffle_integration:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/control_methods/shuffle_integration", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "control_methods", + "method_id": "shuffle_integration_by_batch", + "method_name": "Shuffle integration by batch", + "method_summary": "Integrations are randomly permuted within each batch", + "method_description": "Integrations are randomly permuted within each batch", + "is_baseline": true, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/control_methods/shuffle_integration_by_batch:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/control_methods/shuffle_integration_by_batch", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "control_methods", + "method_id": "shuffle_integration_by_cell_type", + "method_name": "Shuffle integration by cell type", + "method_summary": "Integrations are randomly permuted within each cell type", + "method_description": "Integrations are randomly permuted within each cell type", + "is_baseline": true, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/control_methods/shuffle_integration_by_cell_type:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/control_methods/shuffle_integration_by_cell_type", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "methods", + "method_id": "harmonypy", + "method_name": "Harmonypy", + "method_summary": "Harmonypy is a port of the harmony R package", + "method_description": "Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. \nIt is especially useful for large single-cell datasets such as single-cell RNA-seq.\n", + "is_baseline": false, + "references_doi": "10.1038/s41592-019-0619-0", + "references_bibtex": null, + "code_url": "https://github.com/slowkow/harmonypy", + "documentation_url": "https://portals.broadinstitute.org/harmony", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/methods/harmonypy:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/methods/harmonypy", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "methods", + "method_id": "limma_remove_batch_effect", + "method_name": "Limma removeBatchEffect", + "method_summary": "Uses a linear model and matrix decomposition to remove batch effects from a dataset", + "method_description": "Limma removeBatchEffect is a method that uses a linear model and matrix\ndecomposition to remove batch effects from a dataset. It first fits a linear\nmodel to the data, then decomposes the model matrix into a set of orthogonal\ncomponents. The batch effect is then removed by subtracting the component\ncorresponding to the batch effect from the data.\n", + "is_baseline": false, + "references_doi": "10.1093/nar/gkv007", + "references_bibtex": null, + "code_url": "https://github.com/bioc/limma", + "documentation_url": "https://bioinf.wehi.edu.au/limma", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/methods/limma_remove_batch_effect:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/methods/limma_remove_batch_effect", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "control_methods", + "method_id": "no_integration", + "method_name": "No Integration", + "method_summary": "Control method returning the unintegrated data without performing batch correction.", + "method_description": "The component works by reading and writing back the 'unintegrated' data without performing any operation. \n", + "is_baseline": true, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/control_methods/no_integration:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/control_methods/no_integration", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "control_methods", + "method_id": "perfect_integration_horizontal", + "method_name": "Perfect Integration Horizontal", + "method_summary": "Positive control method for horizontal metrics which reprsents perfect batch integration.", + "method_description": "The method actually just return the validation data but just changing the batch\nand sample ID to those that are in the unintegrated_censored.\nBecause the marker expression is the exactly same as the validation data, there won't\nbe any batch effect present when computing horizontal metrics.\nBatch effect will be present when computing vertical metrics as the validation data\ncontain samples from different batches, unintegrated.\n", + "is_baseline": true, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/control_methods/perfect_integration_horizontal:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/control_methods/perfect_integration_horizontal", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "control_methods", + "method_id": "perfect_integration_vertical", + "method_name": "Perfect Integration Vertical", + "method_summary": "Positive control method for vertical metrics reflecting a scenario in which all samples belong to the same batch", + "method_description": "This control method return all samples from batch 1. \nBecause the samples all came from one batch, we do not expect to see any technical\nvariation caused by batch effects, but we still expect a sample-level effect due to the \nunderlying differences in biology of the samples.\nThe vertical metrics should return a good score.\nHowever, poor scores are expected for horizontal metrics because some samples (those\nfrom unintegrated data) will be compared against the validation data, which still \ncontains variation due to batch effect.\n", + "is_baseline": true, + "references_doi": null, + "references_bibtex": null, + "code_url": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "documentation_url": null, + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/control_methods/perfect_integration_vertical:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/control_methods/perfect_integration_vertical", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "methods", + "method_id": "combat", + "method_name": "Combat", + "method_summary": "ComBat batch correction for single-cell data, implemented in the scanpy package", + "method_description": "Corrects for batch effects by fitting linear models, gains statistical power via an EB framework where information is borrowed across genes. \nThis uses the implementation combat.py\n", + "is_baseline": false, + "references_doi": "10.1093/biostatistics/kxj037", + "references_bibtex": null, + "code_url": "https://github.com/brentp/combat.py", + "documentation_url": "https://scanpy.readthedocs.io/en/latest/api/generated/scanpy.pp.combat.html", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/methods/combat:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/methods/combat", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "methods", + "method_id": "cycombine_nocontrols", + "method_name": "cyCombine (no-controls)", + "method_summary": "cyCombine perform batch correction by using self-organizing maps and ComBat.", + "method_description": "cyCombine perform batch integration by first using self-organizing maps (SOM) to \ngroup similar cells, then applies a ComBat-based method to correct batch effects within \neach group of similar cells. \n\nHere, we run cyCombine without control samples (replicates in cyCombine terminology).\n", + "is_baseline": false, + "references_doi": "10.1038/s41467-022-29383-5", + "references_bibtex": null, + "code_url": "https://github.com/biosurf/cyCombine", + "documentation_url": "https://biosurf.org/cyCombine.html", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/methods/cycombine_nocontrols:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/methods/cycombine_nocontrols", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "methods", + "method_id": "gaussnorm", + "method_name": "GaussNorm", + "method_summary": "Batch effect correction using a per‐channel basis normalization method (gaussNorm)", + "method_description": "This method batch-normalizes a set of cytometry data samples by identifying and aligning the high density regions (landmarks or peaks) for each channel.\nThe data of each channel is shifted in such a way that the identified high density regions are moved to fixed locations called base landmarks.\nNormalization is achieved in three phases:\n1. identifying high-density regions (landmarks) for each flowFrame in the flowSet for a single channel\n2. computing the best matching between the landmarks and a set of fixed reference landmarks for each channel called base landmarks\n3. manipulating the data of each channel in such a way that each landmark is moved to its matching base landmark. Please note that this normalization is on a channel-by-channel basis\n\nNOTE: The default implementation uses `max.lms=2`, although for some channels it is not possible to compute 2 landmarks, resulting in an error.\nIn order to fully automate the batch normalization process, this implementation checks whether it is possible to compute 2 landmarks, and if not, it sets `max.lms=1` for that channel.\n", + "is_baseline": false, + "references_doi": "10.1002/cyto.a.20823", + "references_bibtex": null, + "code_url": "https://github.com/RGLab/flowStats", + "documentation_url": "https://rdrr.io/bioc/flowStats/src/R/gaussNorm.R", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/methods/gaussnorm:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/methods/gaussnorm", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + }, + { + "task_id": "methods", + "method_id": "cytonorm_controls", + "method_name": "CytoNorm with controls", + "method_summary": "CytoNorm Batch normalization algorithm which uses shared controls across batches.", + "method_description": "CytoNorm corrects batch effects by using reference control samples (aliquots of one sample, \ntechnical replicates) included with each batch. \nIt clusters cells, then trains a model on the control samples to learn how marker \nexpression distributions differ across batches for each population.\nIt then uses splines to align these distributions to a common reference (either the mean\nof batches or to a single batch).\nIn this CytoNorm version, batches are aligned to the mean of the batches.\nClustering was performed by FlowSOM, using the default parameters provided by CytoNorm.\n", + "is_baseline": false, + "references_doi": "10.1002/cyto.a.23904", + "references_bibtex": null, + "code_url": "https://github.com/saeyslab/CytoNorm", + "documentation_url": "https://github.com/saeyslab/CytoNorm", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/methods/cytonorm_controls:build_main", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/methods/cytonorm_controls", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f" + } +] diff --git a/results/cyto_batch_integration/data/metric_execution_info.json b/results/cyto_batch_integration/data/metric_execution_info.json new file mode 100644 index 00000000..967136fc --- /dev/null +++ b/results/cyto_batch_integration/data/metric_execution_info.json @@ -0,0 +1,674 @@ +[ + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "combat", + "metric_component_name": "average_batch_r2", + "resources": { + "submit": "2025-05-23 12:46:51", + "exit_code": 0, + "duration_sec": 184, + "cpu_pct": 174.1, + "peak_memory_mb": 5735, + "disk_read_mb": 3278, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "combat", + "metric_component_name": "emd", + "resources": { + "submit": "2025-05-23 12:46:51", + "exit_code": 0, + "duration_sec": 912, + "cpu_pct": 97.7, + "peak_memory_mb": 5837, + "disk_read_mb": 9834, + "disk_write_mb": 6 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "combat", + "metric_component_name": "flowsom_mapping_similarity", + "resources": { + "submit": "2025-05-23 12:46:51", + "exit_code": 0, + "duration_sec": 795, + "cpu_pct": 101, + "peak_memory_mb": 10343, + "disk_read_mb": 2253, + "disk_write_mb": 696 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "combat", + "metric_component_name": "n_inconsistent_peaks", + "resources": { + "submit": "2025-05-23 12:46:51", + "exit_code": 0, + "duration_sec": 1292, + "cpu_pct": 736.9, + "peak_memory_mb": 5530, + "disk_read_mb": 3278, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cycombine_nocontrols", + "metric_component_name": "average_batch_r2", + "resources": { + "submit": "2025-05-23 12:56:51", + "exit_code": 0, + "duration_sec": 182, + "cpu_pct": 175.7, + "peak_memory_mb": 5735, + "disk_read_mb": 3278, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cycombine_nocontrols", + "metric_component_name": "emd", + "resources": { + "submit": "2025-05-23 12:56:51", + "exit_code": 0, + "duration_sec": 912, + "cpu_pct": 98, + "peak_memory_mb": 5940, + "disk_read_mb": 9834, + "disk_write_mb": 6 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cycombine_nocontrols", + "metric_component_name": "flowsom_mapping_similarity", + "resources": { + "submit": "2025-05-23 12:56:51", + "exit_code": 0, + "duration_sec": 773, + "cpu_pct": 100.3, + "peak_memory_mb": 8704, + "disk_read_mb": 2253, + "disk_write_mb": 696 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cycombine_nocontrols", + "metric_component_name": "n_inconsistent_peaks", + "resources": { + "submit": "2025-05-23 12:56:51", + "exit_code": 0, + "duration_sec": 1298, + "cpu_pct": 733.2, + "peak_memory_mb": 5428, + "disk_read_mb": 3278, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cytonorm_controls", + "metric_component_name": "average_batch_r2", + "resources": { + "submit": "2025-05-23 13:03:11", + "exit_code": 0, + "duration_sec": 182, + "cpu_pct": 260.5, + "peak_memory_mb": 7066, + "disk_read_mb": 2664, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cytonorm_controls", + "metric_component_name": "emd", + "resources": { + "submit": "2025-05-23 13:03:11", + "exit_code": 0, + "duration_sec": 912, + "cpu_pct": 100.3, + "peak_memory_mb": 7271, + "disk_read_mb": 7992, + "disk_write_mb": 6 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cytonorm_controls", + "metric_component_name": "flowsom_mapping_similarity", + "resources": { + "submit": "2025-05-23 13:03:11", + "exit_code": 0, + "duration_sec": 767, + "cpu_pct": 100.3, + "peak_memory_mb": 9012, + "disk_read_mb": 2048, + "disk_write_mb": 696 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "cytonorm_controls", + "metric_component_name": "n_inconsistent_peaks", + "resources": { + "submit": "2025-05-23 13:03:11", + "exit_code": 0, + "duration_sec": 1476, + "cpu_pct": 3346.1, + "peak_memory_mb": 9524, + "disk_read_mb": 2664, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "gaussnorm", + "metric_component_name": "average_batch_r2", + "resources": { + "submit": "2025-05-23 12:57:31", + "exit_code": 0, + "duration_sec": 196, + "cpu_pct": 390.3, + "peak_memory_mb": 9728, + "disk_read_mb": 2868, + "disk_write_mb": 2 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "gaussnorm", + "metric_component_name": "emd", + 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+ "dataset_id": "leomazzi_cyto_spleen", + "method_id": "shuffle_integration_by_cell_type", + "metric_component_name": "n_inconsistent_peaks", + "resources": { + "submit": "2025-05-23 12:51:21", + "exit_code": 0, + "duration_sec": 1284, + "cpu_pct": 740.3, + "peak_memory_mb": 4506, + "disk_read_mb": 2458, + "disk_write_mb": 2 + } + } +] diff --git a/results/cyto_batch_integration/data/metric_info.json b/results/cyto_batch_integration/data/metric_info.json new file mode 100644 index 00000000..3b2e119b --- /dev/null +++ b/results/cyto_batch_integration/data/metric_info.json @@ -0,0 +1,167 @@ +[ + { + "task_id": "metrics", + "component_name": "emd", + "metric_id": "emd_mean_ct_horiz", + "metric_name": "EMD Mean CT Horizontal", + "metric_summary": "Mean Earth Mover Distance calculated horizontally across donors for each cell type and marker.", + "metric_description": "Earth Mover Distance (EMD), also known as the Wasserstein metric, measures the difference \nbetween two probability distributions. \n\nHere, EMD is used to compare marker expression distributions between paired samples from the same donor \nquantified across two different batches. \nFor each paired sample, cell type, and marker, the marker expression values are first converted into \nprobability distributions. \nThis is done by binning the expression values into a range from -100 to 100 with a bin width of 0.1.\nThe `wasserstein_distance` function from SciPy is then used to calculate the EMD between the two \nprobability distributions belonging to the same cell type, marker, and a given paired samples.\nThis is then repeated for every cell type, marker, and paired sample.\nFinally, the average of all these EMD values is computed and reported as the metric score.\n\nA high score indicates large overall differences in the distributions of marker expressions \nbetween the paired samples, suggesting poor batch integration.\nA low score means the small differences in marker expression distributions between batches, \nindicating good batch integration.\n", + "references_doi": "10.1023/A:1026543900054", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/emd", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/emd:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "emd", + "metric_id": "emd_max_ct_horiz", + "metric_name": "EMD Max CT", + "metric_summary": "Max Earth Mover Distance calculated horizontally across donors for each cell type and marker.", + "metric_description": "Earth Mover Distance (EMD), also known as the Wasserstein metric, measures the difference \nbetween two probability distributions. \n\nHere, EMD is used to compare marker expression distributions between paired samples from the same donor \nquantified across two different batches. \nFor each paired sample, cell type, and marker, the marker expression values are first converted into \nprobability distributions. \nThis is done by binning the expression values into a range from -100 to 100 with a bin width of 0.1.\nThe `wasserstein_distance` function from SciPy is then used to calculate the EMD between the two \nprobability distributions belonging to the same cell type, marker, and a given paired samples.\nThis is then repeated for every cell type, marker, and paired sample.\nFinally, the maximum of all these EMD values is computed and reported as the metric score.\n\nEMD Max CT score reflects the largest difference in marker expression distributions across all cell types, \nmarkers, and paired samples.\nA high score indicates that at least one marker, cell type, or sample pair has a large difference in \ndistribution after batch integration.\nA low score means that even the most poorly corrected marker expression is well integrated across batches. \n", + "references_doi": "10.1023/A:1026543900054", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/emd", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/emd:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "emd", + "metric_id": "emd_mean_global_horiz", + "metric_name": "EMD Mean Global Horizontal", + "metric_summary": "Mean Earth Mover Distance calculated horizontally across donors for each marker.", + "metric_description": "Earth Mover Distance (EMD), also known as the Wasserstein metric, measures the difference \nbetween two probability distributions. \n\nHere, EMD is used to compare marker expression distributions between paired samples from the same donor \nquantified across two different batches. \nFor each paired sample and marker, the marker expression values are first converted into \nprobability distributions. \nThis is done by binning the expression values into a range from -100 to 100 with a bin width of 0.1.\nThe `wasserstein_distance` function from SciPy is then used to calculate the EMD between the two \nprobability distributions belonging to the same cell type, marker, and a given paired samples.\nThis is then repeated for every marker and paired sample.\nFinally, the average of all these EMD values is computed and reported as the metric score.\n\nThe key difference between this and `emd_mean_ct_horiz` is that the EMD values are\ncomputed agnostic of cell types.\n\nA high score indicates that at least one marker and cell type in a given sample pair has a \nlarge difference in distribution after batch integration.\nA low score means that the most poorly corrected marker expression is well integrated across batches. \n", + "references_doi": "10.1023/A:1026543900054", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/emd", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/emd:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "emd", + "metric_id": "emd_max_global_horiz", + "metric_name": "EMD Max Global Horizontal", + "metric_summary": "Max Earth Mover Distance calculated horizontally across donors for each marker.", + "metric_description": "Earth Mover Distance (EMD), also known as the Wasserstein metric, measures the difference \nbetween two probability distributions. \n\nHere, EMD is used to compare marker expression distributions between paired samples from the same donor \nquantified across two different batches. \nFor each paired sample and marker, the marker expression values are first converted into \nprobability distributions. \nThis is done by binning the expression values into a range from -100 to 100 with a bin width of 0.1.\nThe `wasserstein_distance` function from SciPy is then used to calculate the EMD between the two \nprobability distributions belonging to the same cell type, marker, and a given paired samples.\nThis is then repeated for every cell type, marker, and paired sample.\nFinally, the maximum of all these EMD values is computed and reported as the metric score.\n\nThe key difference between this and `emd_max_ct_horiz` is that the EMD values are\ncomputed agnostic of cell types.\n\nA high score indicates that at least one marker in a given sample pair has a large difference in \ndistribution after batch integration.\nA low score means that the most poorly corrected marker expression is well integrated across batches. \n", + "references_doi": "10.1023/A:1026543900054", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/emd", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/emd:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "emd", + "metric_id": "emd_mean_global_vert", + "metric_name": "EMD Mean Global Vertical", + "metric_summary": "Mean Earth Mover Distance across batch corrected samples and markers.", + "metric_description": "Earth Mover Distance (EMD), also known as the Wasserstein metric, measures the difference \nbetween two probability distributions. \n\nHere, EMD is used to compare marker expression distributions between all integrated samples.\nFor each pair of samples and marker, the marker expression values are first converted into \nprobability distributions. \nThis is done by binning the expression values into a range from -100 to 100 with a bin width of 0.1.\nThe `wasserstein_distance` function from SciPy is then used to calculate the EMD between the two \nprobability distributions belonging to the same cell type, marker, and a given paired samples.\nThis is then repeated for every cell type, marker, and paired sample.\nFinally, the average of all these EMD values is computed and reported as the metric score.\n\nA high score indicates overall, there is a large difference in distribution of marker expression after batch integration.\nA low score means that overall, the samples are well integrated.\n", + "references_doi": "10.1023/A:1026543900054", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/emd", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/emd:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "emd", + "metric_id": "emd_max_global_vert", + "metric_name": "EMD Max Global Vertical", + "metric_summary": "Max Earth Mover Distance across batch corrected samples and markers.", + "metric_description": "Earth Mover Distance (EMD), also known as the Wasserstein metric, measures the difference \nbetween two probability distributions. \n\nHere, EMD is used to compare marker expression distributions between all integrated samples.\nFor each pair of samples and marker, the marker expression values are first converted into \nprobability distributions. \nThis is done by binning the expression values into a range from -100 to 100 with a bin width of 0.1.\nThe `wasserstein_distance` function from SciPy is then used to calculate the EMD between the two \nprobability distributions belonging to the same cell type, marker, and a given paired samples.\nThis is then repeated for every cell type, marker, and paired sample.\nFinally, the maximum of all these EMD values is computed and reported as the metric score.\n\nA high score indicates there is a pair of samples and marker which show large difference in distribution after batch integration.\nA low score means that, the worst integrated pair of samples and marker are well integrated.\n", + "references_doi": "10.1023/A:1026543900054", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/emd", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/emd:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "n_inconsistent_peaks", + "metric_id": "n_inconsistent_peaks", + "metric_name": "Number of inconsistent peaks Global", + "metric_summary": "Comparison of the number of marker‑expression peaks between validation and batch‑normalized data.", + "metric_description": "The metric compares the number of marker expression peaks between the validation and batch-normalized data. \nThe number of peaks is calculated using the `scipy.signal.find_peaks` function. \nThe metric is calculated as the absolute difference between the number of peaks in the validation and batch-normalized data.\nThe marker expression profiles are first smoothed using kernel density estimation (KDE) (`scipy.stats.gaussian_kde`),\nand then peaks are then identified using the `scipy.signal.find_peaks` function.\nFor peak calling, the `prominence` parameter is set to 0.1 and the `height` parameter is set to 0.05*max_density.\n", + "references_doi": "10.1038/s41592-019-0686-2", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/n_inconsistent_peaks", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/n_inconsistent_peaks:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "n_inconsistent_peaks", + "metric_id": "n_inconsistent_peaks_ct", + "metric_name": "Number of inconsistent peaks (Cell Type)", + "metric_summary": "Comparison of the number of cell‑type marker‑expression peaks between validation and batch‑normalized data.", + "metric_description": "The metric compares the number of cell type specific marker expression peaks between the validation and batch-normalized data. \nThe number of peaks is calculated using the `scipy.signal.find_peaks` function. \nThe metric is calculated as the absolute difference between the number of peaks in the validation and batch-normalized data.\nThe (cell type) marker expression profiles are first smoothed using kernel density estimation (KDE) (`scipy.stats.gaussian_kde`),\nand then peaks are then identified using the `scipy.signal.find_peaks` function.\nFor peak calling, the `prominence` parameter is set to 0.1 and the `height` parameter is set to 0.05*max_density.\n", + "references_doi": "10.1038/s41592-019-0686-2", + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/n_inconsistent_peaks", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/n_inconsistent_peaks:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "average_batch_r2", + "metric_id": "average_batch_r2_global", + "metric_name": "Average Batch R-squared Global", + "metric_summary": "The average batch R-squared quantifies, on average, how strongly the batch variable B explains the variance in the data.", + "metric_description": "First, a simple linear model `sklearn.linear_model.LinearRegression` is fitted for each paired sample and marker to determine the fraction of variance (R^2) explained by the batch covariate B. |\nThe average batch R_squared is then computed as the average of the $R^2$ values across all paired samples, markers. |\nAs a result, $\\overline{R^2_B}_{global}$ quantifies how much of the total variability in the data is driven by batch effects. Consequently, lower values are desirable. |\n\n$\\overline{R^2_B}_{global} = \\frac{1}{N*M}\\sum_{\\substack{(x_{\\mathrm{int}},\\,x_{\\mathrm{val}})\\\\ \\text{paired samples}}}^{N} \\sum_{i=1}^{M} \\,R^2\\!\\bigl(\\mathrm{marker}_i \\mid B\\bigr)$\n\nWhere:\n- $N$ is the number of paired samples, where x_{\\mathrm{int}} is the replicate that has been batch-corrected and x_{\\mathrm{val}} is replicate used for validation. Paired samples belong to different batches.\n- $M$ is the number of markers\n- $B$ is the batch covariate\n\nA higher value of $\\overline{R^2_B}_{global}$ indicates that the batch variable explains more of the variance in the data, which indicates a higher level of batch effects. |\n", + "references_doi": null, + "references_bibtex": "@book{draper1998applied,\ntitle={Applied regression analysis},\nauthor={Draper, Norman R and Smith, Harry},\npublisher={John Wiley \\& Sons}\n}\n", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/average_batch_r2", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/average_batch_r2:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "average_batch_r2", + "metric_id": "average_batch_r2_ct", + "metric_name": "Average Batch R-squared Cell Type", + "metric_summary": "The average batch R-squared Cell Type quantifies, on average, how strongly the batch variable B explains the variance in the data (by taking into account cell type effect).", + "metric_description": "First, a simple linear model `sklearn.linear_model.LinearRegression` is fitted for each paired sample, marker and cell type to determine the fraction of variance (R^2) explained by the batch covariate B. |\nThe average batch R_squared is then computed as the average of the $R^2$ values across all paired samples, markers and cell types. |\nAs a result, $\\overline{R^2_B}_{cell\\ type}$ quantifies how much of the total variability in the data is driven by batch effects. Consequently, lower values are desirable. |\n\n$\\overline{R^2_B}_{cell\\ type} = \\frac{1}{N*C*M}\\sum_{\\substack{(x_{\\mathrm{int}},\\,x_{\\mathrm{val}})\\\\ \\text{paired samples}}}^{N} \\sum_{j=1}^{C} \\sum_{i=1}^{M}\\,R^2\\!\\bigl(\\mathrm{marker}_i \\mid B\\bigr)$\n\nWhere:\n- $N$ is the number of paired samples, where x_{\\mathrm{int}} is the replicate that has been batch-corrected and x_{\\mathrm{val}} is replicate used for validation. Paired samples belong to different batches.\n- $C$ is the number of cell types\n- $M$ is the number of markers\n- $B$ is the batch covariate\n\nThe $\\overline{Rˆ2_B}_{global}$ is a variation of the latter metric, where the average is computed across paired samples and markers only, without taking into account the cell types. |\n\nA higher value of $\\overline{R^2_B}_{global}$ or $\\overline{R^2_B}_{cell\\ type}$ indicates that the batch variable explains more of the variance in the data, which indicates a higher level of batch effects. |\n\nA good performance on $\\overline{R^2_B}_{global}$ but not on $\\overline{R^2_B}_{cell\\ type}$ might indicate that the batch effect correction is discarding cell type specific batch effects. |\n", + "references_doi": null, + "references_bibtex": "@book{draper1998applied,\ntitle={Applied regression analysis},\nauthor={Draper, Norman R and Smith, Harry},\npublisher={John Wiley \\& Sons}\n}\n", + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/average_batch_r2", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/average_batch_r2:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": false + }, + { + "task_id": "metrics", + "component_name": "flowsom_mapping_similarity", + "metric_id": "flowsom_mean_mapping_similarity", + "metric_name": "FlowSOM Mean Mapping Similarity", + "metric_summary": "Assess the similarity between FlowSOM trees of integrated and validation samples.", + "metric_description": "The metric is based on the FlowSOM algorithm, a popular method which uses self-organizing maps for the viasualization/interpretation/clustering of cytometry data. \nThe FlowSOM algorithm creates a tree structure that represents the relationships between different cell populations in the data.\n\nFor each paired sample (where 'int' is the batch-integrated sample and 'val' is the validation sample)\n1. A FlowSOM tree is created using validation data.\n2. Data from the integrated sample is mapped onto the FlowSOM tree created in step 1.\n3. A similarity measure is computed by comparing cell type proportions of 'val' and 'int' in each metacluster.\n\nIdeally, the proportions of cell types in the metaclusters of the integrated sample should be very similar to those in the validation sample,\nas we assume that only technical variability is present between these two samples.\n\nThe FlowSOM mapping similarity measure can be expressed as follows:\n$\\text{FlowSOM mapping similarity} = 100 - \\text{FlowSOM mapping dissimilarity}$\n\nThe $\\text{FlowSOM mapping dissimilarity}$ is:\n\n$\\text{FlowSOM mapping dissimilarity} = \\sum_{m=1}^{M}w_{m}\\sum_{c=1}^{C}\\abs{P^{val}_{m,c} - P^{int}_{m,c}}$\n\nWhere:\n- $M$ is the number of metaclusters\n- $C$ is the number of cell types\n- $w_{m}$ is the weight of metacluster $m$ (the number of cells in metacluster $m$, for both validation and integrated samples, divided by the total number of cells)\n- $P^{val}_{m,c}$ is the percentage of cell type $c$ in metacluster $m$ of the validation sample\n- $P^{int}_{m,c}$ is the percentage of cell type $c$ in metacluster $m$ of the integrated sample\n\nThe average FlowSOM mapping similarity among all paired samples is computed and used as the final metric value.\nIt is an horizontal metric.\n", + "references_doi": ["10.18129/B9.bioc.FlowSOM", "10.1002/cyto.a.22625"], + "references_bibtex": null, + "implementation_url": "https://github.com/openproblems-bio/task_cyto_batch_integration/blob/37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f/src/metrics/flowsom_mapping_similarity", + "image": "https://ghcr.io/openproblems-bio/task_cyto_batch_integration/metrics/flowsom_mapping_similarity:build_main", + "code_version": "build_main", + "commit_sha": "37dcf0c34b0aa64c16d7d82bc631ff6684e37c5f", + "maximize": true + } +] diff --git a/results/cyto_batch_integration/data/quality_control.json b/results/cyto_batch_integration/data/quality_control.json new file mode 100644 index 00000000..c24f2ab7 --- /dev/null +++ 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equal to #methods × #metrics × #datasets.\n Task id: task_cyto_batch_integration\n Number of results: 12\n Number of methods: 12\n Number of metrics: 11\n Number of datasets: 1\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'emd_mean_ct_horiz' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: emd_mean_ct_horiz\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'emd_max_ct_horiz' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: emd_max_ct_horiz\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'emd_mean_global_horiz' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: emd_mean_global_horiz\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'emd_max_global_horiz' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: emd_max_global_horiz\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'emd_mean_global_vert' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: emd_mean_global_vert\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'emd_max_global_vert' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: emd_max_global_vert\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'n_inconsistent_peaks' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: n_inconsistent_peaks\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'n_inconsistent_peaks_ct' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: n_inconsistent_peaks_ct\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'average_batch_r2_global' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: average_batch_r2_global\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'average_batch_r2_ct' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: average_batch_r2_ct\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Metric 'flowsom_mean_mapping_similarity' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n Metric id: flowsom_mean_mapping_similarity\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'shuffle_integration' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: shuffle_integration\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'shuffle_integration_by_batch' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: shuffle_integration_by_batch\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'shuffle_integration_by_cell_type' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: shuffle_integration_by_cell_type\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'harmonypy' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: harmonypy\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'limma_remove_batch_effect' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: limma_remove_batch_effect\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'no_integration' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: no_integration\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'perfect_integration_horizontal' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: perfect_integration_horizontal\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'perfect_integration_vertical' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: perfect_integration_vertical\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'combat' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: combat\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'cycombine_nocontrols' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: cycombine_nocontrols\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'gaussnorm' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: gaussnorm\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Method 'cytonorm_controls' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n method id: cytonorm_controls\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Raw results", + "name": "Dataset 'leomazzi_cyto_spleen' %missing", + "value": 0.0, + "severity": 0, + "severity_value": 0.0, + "code": "pct_missing <= .1", + "message": "Percentage of missing results should be less than 10%.\n Task id: task_cyto_batch_integration\n dataset id: leomazzi_cyto_spleen\n Percentage missing: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration emd_mean_ct_horiz", + "value": 0.0241, + "severity": 0, + "severity_value": -0.0241, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_mean_ct_horiz\n Worst score: 0.0241%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration emd_mean_ct_horiz", + "value": 0.0241, + "severity": 0, + "severity_value": 0.01205, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_mean_ct_horiz\n Best score: 0.0241%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch emd_mean_ct_horiz", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_mean_ct_horiz\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch emd_mean_ct_horiz", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_mean_ct_horiz\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type emd_mean_ct_horiz", + "value": 0.78, + "severity": 0, + "severity_value": -0.78, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_mean_ct_horiz\n Worst score: 0.78%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type emd_mean_ct_horiz", + "value": 0.78, + "severity": 0, + "severity_value": 0.39, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_mean_ct_horiz\n Best score: 0.78%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy emd_mean_ct_horiz", + "value": 0.7864, + "severity": 0, + "severity_value": -0.7864, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_mean_ct_horiz\n Worst score: 0.7864%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy emd_mean_ct_horiz", + "value": 0.7864, + "severity": 0, + "severity_value": 0.3932, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_mean_ct_horiz\n Best score: 0.7864%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect emd_mean_ct_horiz", + "value": 0.7723, + "severity": 0, + "severity_value": -0.7723, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_mean_ct_horiz\n Worst score: 0.7723%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect emd_mean_ct_horiz", + "value": 0.7723, + "severity": 0, + "severity_value": 0.38615, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_mean_ct_horiz\n Best score: 0.7723%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration emd_mean_ct_horiz", + "value": 0.7453, + "severity": 0, + "severity_value": -0.7453, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_mean_ct_horiz\n Worst score: 0.7453%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration emd_mean_ct_horiz", + "value": 0.7453, + "severity": 0, + "severity_value": 0.37265, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_mean_ct_horiz\n Best score: 0.7453%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal emd_mean_ct_horiz", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_mean_ct_horiz\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal emd_mean_ct_horiz", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_mean_ct_horiz\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical emd_mean_ct_horiz", + "value": 0.8783, + "severity": 0, + "severity_value": -0.8783, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_mean_ct_horiz\n Worst score: 0.8783%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical emd_mean_ct_horiz", + "value": 0.8783, + "severity": 0, + "severity_value": 0.43915, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_mean_ct_horiz\n Best score: 0.8783%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat emd_mean_ct_horiz", + "value": 0.7766, + "severity": 0, + "severity_value": -0.7766, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_mean_ct_horiz\n Worst score: 0.7766%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat emd_mean_ct_horiz", + "value": 0.7766, + "severity": 0, + "severity_value": 0.3883, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_mean_ct_horiz\n Best score: 0.7766%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols emd_mean_ct_horiz", + "value": 0.823, + "severity": 0, + "severity_value": -0.823, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_mean_ct_horiz\n Worst score: 0.823%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols emd_mean_ct_horiz", + "value": 0.823, + "severity": 0, + "severity_value": 0.4115, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_mean_ct_horiz\n Best score: 0.823%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm emd_mean_ct_horiz", + "value": 0.7423, + "severity": 0, + "severity_value": -0.7423, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_mean_ct_horiz\n Worst score: 0.7423%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm emd_mean_ct_horiz", + "value": 0.7423, + "severity": 0, + "severity_value": 0.37115, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_mean_ct_horiz\n Best score: 0.7423%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls emd_mean_ct_horiz", + "value": 0.8328, + "severity": 0, + "severity_value": -0.8328, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_mean_ct_horiz\n Worst score: 0.8328%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls emd_mean_ct_horiz", + "value": 0.8328, + "severity": 0, + "severity_value": 0.4164, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_mean_ct_horiz\n Best score: 0.8328%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration emd_max_ct_horiz", + "value": 0.0338, + "severity": 0, + "severity_value": -0.0338, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_max_ct_horiz\n Worst score: 0.0338%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration emd_max_ct_horiz", + "value": 0.0338, + "severity": 0, + "severity_value": 0.0169, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_max_ct_horiz\n Best score: 0.0338%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch emd_max_ct_horiz", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_max_ct_horiz\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch emd_max_ct_horiz", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_max_ct_horiz\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type emd_max_ct_horiz", + "value": 0.5382, + "severity": 0, + "severity_value": -0.5382, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_max_ct_horiz\n Worst score: 0.5382%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type emd_max_ct_horiz", + "value": 0.5382, + "severity": 0, + "severity_value": 0.2691, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_max_ct_horiz\n Best score: 0.5382%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy emd_max_ct_horiz", + "value": 0.559, + "severity": 0, + "severity_value": -0.559, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_max_ct_horiz\n Worst score: 0.559%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy emd_max_ct_horiz", + "value": 0.559, + "severity": 0, + "severity_value": 0.2795, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_max_ct_horiz\n Best score: 0.559%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect emd_max_ct_horiz", + "value": 0.5529, + "severity": 0, + "severity_value": -0.5529, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_max_ct_horiz\n Worst score: 0.5529%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect emd_max_ct_horiz", + "value": 0.5529, + "severity": 0, + "severity_value": 0.27645, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_max_ct_horiz\n Best score: 0.5529%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration emd_max_ct_horiz", + "value": 0.5373, + "severity": 0, + "severity_value": -0.5373, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_max_ct_horiz\n Worst score: 0.5373%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration emd_max_ct_horiz", + "value": 0.5373, + "severity": 0, + "severity_value": 0.26865, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_max_ct_horiz\n Best score: 0.5373%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal emd_max_ct_horiz", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_max_ct_horiz\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal emd_max_ct_horiz", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_max_ct_horiz\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical emd_max_ct_horiz", + "value": 0.5979, + "severity": 0, + "severity_value": -0.5979, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_max_ct_horiz\n Worst score: 0.5979%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical emd_max_ct_horiz", + "value": 0.5979, + "severity": 0, + "severity_value": 0.29895, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_max_ct_horiz\n Best score: 0.5979%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat emd_max_ct_horiz", + "value": 0.5441, + "severity": 0, + "severity_value": -0.5441, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_max_ct_horiz\n Worst score: 0.5441%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat emd_max_ct_horiz", + "value": 0.5441, + "severity": 0, + "severity_value": 0.27205, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_max_ct_horiz\n Best score: 0.5441%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols emd_max_ct_horiz", + "value": 0.5993, + "severity": 0, + "severity_value": -0.5993, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_max_ct_horiz\n Worst score: 0.5993%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols emd_max_ct_horiz", + "value": 0.5993, + "severity": 0, + "severity_value": 0.29965, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_max_ct_horiz\n Best score: 0.5993%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm emd_max_ct_horiz", + "value": 0.54, + "severity": 0, + "severity_value": -0.54, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_max_ct_horiz\n Worst score: 0.54%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm emd_max_ct_horiz", + "value": 0.54, + "severity": 0, + "severity_value": 0.27, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_max_ct_horiz\n Best score: 0.54%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls emd_max_ct_horiz", + "value": 0.6814, + "severity": 0, + "severity_value": -0.6814, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_max_ct_horiz\n Worst score: 0.6814%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls emd_max_ct_horiz", + "value": 0.6814, + "severity": 0, + "severity_value": 0.3407, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_max_ct_horiz\n Best score: 0.6814%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration emd_mean_global_horiz", + "value": 0.2001, + "severity": 0, + "severity_value": -0.2001, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_mean_global_horiz\n Worst score: 0.2001%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration emd_mean_global_horiz", + "value": 0.2001, + "severity": 0, + "severity_value": 0.10005, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_mean_global_horiz\n Best score: 0.2001%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch emd_mean_global_horiz", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_mean_global_horiz\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch emd_mean_global_horiz", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_mean_global_horiz\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type emd_mean_global_horiz", + "value": 0.518, + "severity": 0, + "severity_value": -0.518, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_mean_global_horiz\n Worst score: 0.518%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type emd_mean_global_horiz", + "value": 0.518, + "severity": 0, + "severity_value": 0.259, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_mean_global_horiz\n Best score: 0.518%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy emd_mean_global_horiz", + "value": 0.6002, + "severity": 0, + "severity_value": -0.6002, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_mean_global_horiz\n Worst score: 0.6002%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy emd_mean_global_horiz", + "value": 0.6002, + "severity": 0, + "severity_value": 0.3001, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_mean_global_horiz\n Best score: 0.6002%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect emd_mean_global_horiz", + "value": 0.5896, + "severity": 0, + "severity_value": -0.5896, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_mean_global_horiz\n Worst score: 0.5896%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect emd_mean_global_horiz", + "value": 0.5896, + "severity": 0, + "severity_value": 0.2948, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_mean_global_horiz\n Best score: 0.5896%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration emd_mean_global_horiz", + "value": 0.3619, + "severity": 0, + "severity_value": -0.3619, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_mean_global_horiz\n Worst score: 0.3619%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration emd_mean_global_horiz", + "value": 0.3619, + "severity": 0, + "severity_value": 0.18095, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_mean_global_horiz\n Best score: 0.3619%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal emd_mean_global_horiz", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_mean_global_horiz\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal emd_mean_global_horiz", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_mean_global_horiz\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical emd_mean_global_horiz", + "value": 0.6847, + "severity": 0, + "severity_value": -0.6847, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_mean_global_horiz\n Worst score: 0.6847%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical emd_mean_global_horiz", + "value": 0.6847, + "severity": 0, + "severity_value": 0.34235, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_mean_global_horiz\n Best score: 0.6847%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat emd_mean_global_horiz", + "value": 0.6018, + "severity": 0, + "severity_value": -0.6018, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_mean_global_horiz\n Worst score: 0.6018%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat emd_mean_global_horiz", + "value": 0.6018, + "severity": 0, + "severity_value": 0.3009, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_mean_global_horiz\n Best score: 0.6018%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols emd_mean_global_horiz", + "value": 0.5829, + "severity": 0, + "severity_value": -0.5829, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_mean_global_horiz\n Worst score: 0.5829%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols emd_mean_global_horiz", + "value": 0.5829, + "severity": 0, + "severity_value": 0.29145, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_mean_global_horiz\n Best score: 0.5829%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm emd_mean_global_horiz", + "value": 0.4575, + "severity": 0, + "severity_value": -0.4575, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_mean_global_horiz\n Worst score: 0.4575%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm emd_mean_global_horiz", + "value": 0.4575, + "severity": 0, + "severity_value": 0.22875, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_mean_global_horiz\n Best score: 0.4575%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls emd_mean_global_horiz", + "value": 0.6444, + "severity": 0, + "severity_value": -0.6444, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_mean_global_horiz\n Worst score: 0.6444%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls emd_mean_global_horiz", + "value": 0.6444, + "severity": 0, + "severity_value": 0.3222, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_mean_global_horiz\n Best score: 0.6444%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration emd_max_global_horiz", + "value": 0.1315, + "severity": 0, + "severity_value": -0.1315, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_max_global_horiz\n Worst score: 0.1315%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration emd_max_global_horiz", + "value": 0.1315, + "severity": 0, + "severity_value": 0.06575, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_max_global_horiz\n Best score: 0.1315%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch emd_max_global_horiz", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_max_global_horiz\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch emd_max_global_horiz", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_max_global_horiz\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type emd_max_global_horiz", + "value": 0.583, + "severity": 0, + "severity_value": -0.583, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_max_global_horiz\n Worst score: 0.583%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type emd_max_global_horiz", + "value": 0.583, + "severity": 0, + "severity_value": 0.2915, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_max_global_horiz\n Best score: 0.583%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy emd_max_global_horiz", + "value": 0.5859, + "severity": 0, + "severity_value": -0.5859, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_max_global_horiz\n Worst score: 0.5859%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy emd_max_global_horiz", + "value": 0.5859, + "severity": 0, + "severity_value": 0.29295, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_max_global_horiz\n Best score: 0.5859%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect emd_max_global_horiz", + "value": 0.5721, + "severity": 0, + "severity_value": -0.5721, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_max_global_horiz\n Worst score: 0.5721%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect emd_max_global_horiz", + "value": 0.5721, + "severity": 0, + "severity_value": 0.28605, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_max_global_horiz\n Best score: 0.5721%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration emd_max_global_horiz", + "value": 0.2369, + "severity": 0, + "severity_value": -0.2369, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_max_global_horiz\n Worst score: 0.2369%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration emd_max_global_horiz", + "value": 0.2369, + "severity": 0, + "severity_value": 0.11845, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_max_global_horiz\n Best score: 0.2369%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal emd_max_global_horiz", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_max_global_horiz\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal emd_max_global_horiz", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_max_global_horiz\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical emd_max_global_horiz", + "value": 0.2369, + "severity": 0, + "severity_value": -0.2369, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_max_global_horiz\n Worst score: 0.2369%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical emd_max_global_horiz", + "value": 0.2369, + "severity": 0, + "severity_value": 0.11845, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_max_global_horiz\n Best score: 0.2369%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat emd_max_global_horiz", + "value": 0.5295, + "severity": 0, + "severity_value": -0.5295, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_max_global_horiz\n Worst score: 0.5295%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat emd_max_global_horiz", + "value": 0.5295, + "severity": 0, + "severity_value": 0.26475, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_max_global_horiz\n Best score: 0.5295%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols emd_max_global_horiz", + "value": 0.5337, + "severity": 0, + "severity_value": -0.5337, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_max_global_horiz\n Worst score: 0.5337%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols emd_max_global_horiz", + "value": 0.5337, + "severity": 0, + "severity_value": 0.26685, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_max_global_horiz\n Best score: 0.5337%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm emd_max_global_horiz", + "value": 0.4733, + "severity": 0, + "severity_value": -0.4733, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_max_global_horiz\n Worst score: 0.4733%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm emd_max_global_horiz", + "value": 0.4733, + "severity": 0, + "severity_value": 0.23665, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_max_global_horiz\n Best score: 0.4733%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls emd_max_global_horiz", + "value": 0.6241, + "severity": 0, + "severity_value": -0.6241, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_max_global_horiz\n Worst score: 0.6241%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls emd_max_global_horiz", + "value": 0.6241, + "severity": 0, + "severity_value": 0.31205, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_max_global_horiz\n Best score: 0.6241%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration emd_mean_global_vert", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_mean_global_vert\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration emd_mean_global_vert", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_mean_global_vert\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch emd_mean_global_vert", + "value": 0.6413, + "severity": 0, + "severity_value": -0.6413, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_mean_global_vert\n Worst score: 0.6413%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch emd_mean_global_vert", + "value": 0.6413, + "severity": 0, + "severity_value": 0.32065, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_mean_global_vert\n Best score: 0.6413%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type emd_mean_global_vert", + "value": 0.4341, + "severity": 0, + "severity_value": -0.4341, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_mean_global_vert\n Worst score: 0.4341%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type emd_mean_global_vert", + "value": 0.4341, + "severity": 0, + "severity_value": 0.21705, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_mean_global_vert\n Best score: 0.4341%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy emd_mean_global_vert", + "value": 0.2491, + "severity": 0, + "severity_value": -0.2491, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_mean_global_vert\n Worst score: 0.2491%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy emd_mean_global_vert", + "value": 0.2491, + "severity": 0, + "severity_value": 0.12455, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_mean_global_vert\n Best score: 0.2491%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect emd_mean_global_vert", + "value": 0.2382, + "severity": 0, + "severity_value": -0.2382, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_mean_global_vert\n Worst score: 0.2382%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect emd_mean_global_vert", + "value": 0.2382, + "severity": 0, + "severity_value": 0.1191, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_mean_global_vert\n Best score: 0.2382%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration emd_mean_global_vert", + "value": 0.0543, + "severity": 0, + "severity_value": -0.0543, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_mean_global_vert\n Worst score: 0.0543%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration emd_mean_global_vert", + "value": 0.0543, + "severity": 0, + "severity_value": 0.02715, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_mean_global_vert\n Best score: 0.0543%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal emd_mean_global_vert", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_mean_global_vert\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal emd_mean_global_vert", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_mean_global_vert\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical emd_mean_global_vert", + "value": 0.1673, + "severity": 0, + "severity_value": -0.1673, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_mean_global_vert\n Worst score: 0.1673%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical emd_mean_global_vert", + "value": 0.1673, + "severity": 0, + "severity_value": 0.08365, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_mean_global_vert\n Best score: 0.1673%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat emd_mean_global_vert", + "value": 0.2513, + "severity": 0, + "severity_value": -0.2513, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_mean_global_vert\n Worst score: 0.2513%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat emd_mean_global_vert", + "value": 0.2513, + "severity": 0, + "severity_value": 0.12565, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_mean_global_vert\n Best score: 0.2513%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols emd_mean_global_vert", + "value": 0.2659, + "severity": 0, + "severity_value": -0.2659, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_mean_global_vert\n Worst score: 0.2659%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols emd_mean_global_vert", + "value": 0.2659, + "severity": 0, + "severity_value": 0.13295, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_mean_global_vert\n Best score: 0.2659%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm emd_mean_global_vert", + "value": 0.2413, + "severity": 0, + "severity_value": -0.2413, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_mean_global_vert\n Worst score: 0.2413%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm emd_mean_global_vert", + "value": 0.2413, + "severity": 0, + "severity_value": 0.12065, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_mean_global_vert\n Best score: 0.2413%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls emd_mean_global_vert", + "value": 0.2422, + "severity": 0, + "severity_value": -0.2422, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_mean_global_vert\n Worst score: 0.2422%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls emd_mean_global_vert", + "value": 0.2422, + "severity": 0, + "severity_value": 0.1211, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_mean_global_vert\n Best score: 0.2422%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration emd_max_global_vert", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_max_global_vert\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration emd_max_global_vert", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: emd_max_global_vert\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch emd_max_global_vert", + "value": 0.4225, + "severity": 0, + "severity_value": -0.4225, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_max_global_vert\n Worst score: 0.4225%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch emd_max_global_vert", + "value": 0.4225, + "severity": 0, + "severity_value": 0.21125, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: emd_max_global_vert\n Best score: 0.4225%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type emd_max_global_vert", + "value": 0.2146, + "severity": 0, + "severity_value": -0.2146, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_max_global_vert\n Worst score: 0.2146%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type emd_max_global_vert", + "value": 0.2146, + "severity": 0, + "severity_value": 0.1073, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: emd_max_global_vert\n Best score: 0.2146%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy emd_max_global_vert", + "value": 0.1647, + "severity": 0, + "severity_value": -0.1647, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_max_global_vert\n Worst score: 0.1647%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy emd_max_global_vert", + "value": 0.1647, + "severity": 0, + "severity_value": 0.08235, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: emd_max_global_vert\n Best score: 0.1647%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect emd_max_global_vert", + "value": 0.1784, + "severity": 0, + "severity_value": -0.1784, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_max_global_vert\n Worst score: 0.1784%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect emd_max_global_vert", + "value": 0.1784, + "severity": 0, + "severity_value": 0.0892, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: emd_max_global_vert\n Best score: 0.1784%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration emd_max_global_vert", + "value": 0.0084, + "severity": 0, + "severity_value": -0.0084, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_max_global_vert\n Worst score: 0.0084%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration emd_max_global_vert", + "value": 0.0084, + "severity": 0, + "severity_value": 0.0042, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: emd_max_global_vert\n Best score: 0.0084%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal emd_max_global_vert", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_max_global_vert\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal emd_max_global_vert", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: emd_max_global_vert\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical emd_max_global_vert", + "value": 0.1784, + "severity": 0, + "severity_value": -0.1784, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_max_global_vert\n Worst score: 0.1784%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical emd_max_global_vert", + "value": 0.1784, + "severity": 0, + "severity_value": 0.0892, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: emd_max_global_vert\n Best score: 0.1784%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat emd_max_global_vert", + "value": 0.1831, + "severity": 0, + "severity_value": -0.1831, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_max_global_vert\n Worst score: 0.1831%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat emd_max_global_vert", + "value": 0.1831, + "severity": 0, + "severity_value": 0.09155, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: emd_max_global_vert\n Best score: 0.1831%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols emd_max_global_vert", + "value": 0.176, + "severity": 0, + "severity_value": -0.176, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_max_global_vert\n Worst score: 0.176%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols emd_max_global_vert", + "value": 0.176, + "severity": 0, + "severity_value": 0.088, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: emd_max_global_vert\n Best score: 0.176%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm emd_max_global_vert", + "value": 0.0301, + "severity": 0, + "severity_value": -0.0301, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_max_global_vert\n Worst score: 0.0301%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm emd_max_global_vert", + "value": 0.0301, + "severity": 0, + "severity_value": 0.01505, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: emd_max_global_vert\n Best score: 0.0301%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls emd_max_global_vert", + "value": 0.1853, + "severity": 0, + "severity_value": -0.1853, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_max_global_vert\n Worst score: 0.1853%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls emd_max_global_vert", + "value": 0.1853, + "severity": 0, + "severity_value": 0.09265, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: emd_max_global_vert\n Best score: 0.1853%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration n_inconsistent_peaks", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: n_inconsistent_peaks\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration n_inconsistent_peaks", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: n_inconsistent_peaks\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch n_inconsistent_peaks", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: n_inconsistent_peaks\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch n_inconsistent_peaks", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: n_inconsistent_peaks\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type n_inconsistent_peaks", + "value": 0.5, + "severity": 0, + "severity_value": -0.5, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: n_inconsistent_peaks\n Worst score: 0.5%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type n_inconsistent_peaks", + "value": 0.5, + "severity": 0, + "severity_value": 0.25, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: n_inconsistent_peaks\n Best score: 0.5%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": -0.75, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: n_inconsistent_peaks\n Worst score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": 0.375, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: n_inconsistent_peaks\n Best score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": -0.75, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: n_inconsistent_peaks\n Worst score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": 0.375, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: n_inconsistent_peaks\n Best score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": -0.75, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: n_inconsistent_peaks\n Worst score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": 0.375, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: n_inconsistent_peaks\n Best score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal n_inconsistent_peaks", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: n_inconsistent_peaks\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal n_inconsistent_peaks", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: n_inconsistent_peaks\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical n_inconsistent_peaks", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: n_inconsistent_peaks\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical n_inconsistent_peaks", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: n_inconsistent_peaks\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat n_inconsistent_peaks", + "value": 0.625, + "severity": 0, + "severity_value": -0.625, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: n_inconsistent_peaks\n Worst score: 0.625%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat n_inconsistent_peaks", + "value": 0.625, + "severity": 0, + "severity_value": 0.3125, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: n_inconsistent_peaks\n Best score: 0.625%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": -0.75, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: n_inconsistent_peaks\n Worst score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": 0.375, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: n_inconsistent_peaks\n Best score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm n_inconsistent_peaks", + "value": 0.625, + "severity": 0, + "severity_value": -0.625, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: n_inconsistent_peaks\n Worst score: 0.625%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm n_inconsistent_peaks", + "value": 0.625, + "severity": 0, + "severity_value": 0.3125, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: n_inconsistent_peaks\n Best score: 0.625%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": -0.75, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: n_inconsistent_peaks\n Worst score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls n_inconsistent_peaks", + "value": 0.75, + "severity": 0, + "severity_value": 0.375, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: n_inconsistent_peaks\n Best score: 0.75%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration n_inconsistent_peaks_ct", + "value": 0.0034, + "severity": 0, + "severity_value": -0.0034, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.0034%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration n_inconsistent_peaks_ct", + "value": 0.0034, + "severity": 0, + "severity_value": 0.0017, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.0034%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch n_inconsistent_peaks_ct", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch n_inconsistent_peaks_ct", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: n_inconsistent_peaks_ct\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type n_inconsistent_peaks_ct", + "value": 0.7655, + "severity": 0, + "severity_value": -0.7655, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.7655%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type n_inconsistent_peaks_ct", + "value": 0.7655, + "severity": 0, + "severity_value": 0.38275, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.7655%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy n_inconsistent_peaks_ct", + "value": 0.8724, + "severity": 0, + "severity_value": -0.8724, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.8724%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy n_inconsistent_peaks_ct", + "value": 0.8724, + "severity": 0, + "severity_value": 0.4362, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.8724%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect n_inconsistent_peaks_ct", + "value": 0.869, + "severity": 0, + "severity_value": -0.869, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.869%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect n_inconsistent_peaks_ct", + "value": 0.869, + "severity": 0, + "severity_value": 0.4345, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.869%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration n_inconsistent_peaks_ct", + "value": 0.869, + "severity": 0, + "severity_value": -0.869, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.869%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration n_inconsistent_peaks_ct", + "value": 0.869, + "severity": 0, + "severity_value": 0.4345, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.869%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal n_inconsistent_peaks_ct", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: n_inconsistent_peaks_ct\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal n_inconsistent_peaks_ct", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: n_inconsistent_peaks_ct\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical n_inconsistent_peaks_ct", + "value": 0.9345, + "severity": 0, + "severity_value": -0.9345, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.9345%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical n_inconsistent_peaks_ct", + "value": 0.9345, + "severity": 0, + "severity_value": 0.46725, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.9345%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat n_inconsistent_peaks_ct", + "value": 0.8655, + "severity": 0, + "severity_value": -0.8655, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.8655%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat n_inconsistent_peaks_ct", + "value": 0.8655, + "severity": 0, + "severity_value": 0.43275, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.8655%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols n_inconsistent_peaks_ct", + "value": 0.8655, + "severity": 0, + "severity_value": -0.8655, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.8655%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols n_inconsistent_peaks_ct", + "value": 0.8655, + "severity": 0, + "severity_value": 0.43275, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.8655%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm n_inconsistent_peaks_ct", + "value": 0.8828, + "severity": 0, + "severity_value": -0.8828, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.8828%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm n_inconsistent_peaks_ct", + "value": 0.8828, + "severity": 0, + "severity_value": 0.4414, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.8828%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls n_inconsistent_peaks_ct", + "value": 0.8793, + "severity": 0, + "severity_value": -0.8793, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: n_inconsistent_peaks_ct\n Worst score: 0.8793%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls n_inconsistent_peaks_ct", + "value": 0.8793, + "severity": 0, + "severity_value": 0.43965, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: n_inconsistent_peaks_ct\n Best score: 0.8793%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration average_batch_r2_global", + "value": 0.5241, + "severity": 0, + "severity_value": -0.5241, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: average_batch_r2_global\n Worst score: 0.5241%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration average_batch_r2_global", + "value": 0.5241, + "severity": 0, + "severity_value": 0.26205, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: average_batch_r2_global\n Best score: 0.5241%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch average_batch_r2_global", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: average_batch_r2_global\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch average_batch_r2_global", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: average_batch_r2_global\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type average_batch_r2_global", + "value": 0.7157, + "severity": 0, + "severity_value": -0.7157, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: average_batch_r2_global\n Worst score: 0.7157%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type average_batch_r2_global", + "value": 0.7157, + "severity": 0, + "severity_value": 0.35785, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: average_batch_r2_global\n Best score: 0.7157%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy average_batch_r2_global", + "value": 0.759, + "severity": 0, + "severity_value": -0.759, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: average_batch_r2_global\n Worst score: 0.759%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy average_batch_r2_global", + "value": 0.759, + "severity": 0, + "severity_value": 0.3795, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: average_batch_r2_global\n Best score: 0.759%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect average_batch_r2_global", + "value": 0.7624, + "severity": 0, + "severity_value": -0.7624, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: average_batch_r2_global\n Worst score: 0.7624%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect average_batch_r2_global", + "value": 0.7624, + "severity": 0, + "severity_value": 0.3812, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: average_batch_r2_global\n Best score: 0.7624%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration average_batch_r2_global", + "value": 0.2176, + "severity": 0, + "severity_value": -0.2176, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: average_batch_r2_global\n Worst score: 0.2176%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration average_batch_r2_global", + "value": 0.2176, + "severity": 0, + "severity_value": 0.1088, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: average_batch_r2_global\n Best score: 0.2176%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal average_batch_r2_global", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: average_batch_r2_global\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal average_batch_r2_global", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: average_batch_r2_global\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical average_batch_r2_global", + "value": 0.5898, + "severity": 0, + "severity_value": -0.5898, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: average_batch_r2_global\n Worst score: 0.5898%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical average_batch_r2_global", + "value": 0.5898, + "severity": 0, + "severity_value": 0.2949, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: average_batch_r2_global\n Best score: 0.5898%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat average_batch_r2_global", + "value": 0.7545, + "severity": 0, + "severity_value": -0.7545, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: average_batch_r2_global\n Worst score: 0.7545%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat average_batch_r2_global", + "value": 0.7545, + "severity": 0, + "severity_value": 0.37725, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: average_batch_r2_global\n Best score: 0.7545%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols average_batch_r2_global", + "value": 0.6779, + "severity": 0, + "severity_value": -0.6779, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: average_batch_r2_global\n Worst score: 0.6779%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols average_batch_r2_global", + "value": 0.6779, + "severity": 0, + "severity_value": 0.33895, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: average_batch_r2_global\n Best score: 0.6779%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm average_batch_r2_global", + "value": 0.5408, + "severity": 0, + "severity_value": -0.5408, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: average_batch_r2_global\n Worst score: 0.5408%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm average_batch_r2_global", + "value": 0.5408, + "severity": 0, + "severity_value": 0.2704, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: average_batch_r2_global\n Best score: 0.5408%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls average_batch_r2_global", + "value": 0.7641, + "severity": 0, + "severity_value": -0.7641, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: average_batch_r2_global\n Worst score: 0.7641%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls average_batch_r2_global", + "value": 0.7641, + "severity": 0, + "severity_value": 0.38205, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: average_batch_r2_global\n Best score: 0.7641%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration average_batch_r2_ct", + "value": 0.0598, + "severity": 0, + "severity_value": -0.0598, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: average_batch_r2_ct\n Worst score: 0.0598%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration average_batch_r2_ct", + "value": 0.0598, + "severity": 0, + "severity_value": 0.0299, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: average_batch_r2_ct\n Best score: 0.0598%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch average_batch_r2_ct", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: average_batch_r2_ct\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch average_batch_r2_ct", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: average_batch_r2_ct\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type average_batch_r2_ct", + "value": 0.8421, + "severity": 0, + "severity_value": -0.8421, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: average_batch_r2_ct\n Worst score: 0.8421%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type average_batch_r2_ct", + "value": 0.8421, + "severity": 0, + "severity_value": 0.42105, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: average_batch_r2_ct\n Best score: 0.8421%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy average_batch_r2_ct", + "value": 0.7969, + "severity": 0, + "severity_value": -0.7969, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: average_batch_r2_ct\n Worst score: 0.7969%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy average_batch_r2_ct", + "value": 0.7969, + "severity": 0, + "severity_value": 0.39845, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: average_batch_r2_ct\n Best score: 0.7969%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect average_batch_r2_ct", + "value": 0.7542, + "severity": 0, + "severity_value": -0.7542, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: average_batch_r2_ct\n Worst score: 0.7542%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect average_batch_r2_ct", + "value": 0.7542, + "severity": 0, + "severity_value": 0.3771, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: average_batch_r2_ct\n Best score: 0.7542%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration average_batch_r2_ct", + "value": 0.7063, + "severity": 0, + "severity_value": -0.7063, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: average_batch_r2_ct\n Worst score: 0.7063%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration average_batch_r2_ct", + "value": 0.7063, + "severity": 0, + "severity_value": 0.35315, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: average_batch_r2_ct\n Best score: 0.7063%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal average_batch_r2_ct", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: average_batch_r2_ct\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal average_batch_r2_ct", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: average_batch_r2_ct\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical average_batch_r2_ct", + "value": 0.8482, + "severity": 0, + "severity_value": -0.8482, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: average_batch_r2_ct\n Worst score: 0.8482%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical average_batch_r2_ct", + "value": 0.8482, + "severity": 0, + "severity_value": 0.4241, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: average_batch_r2_ct\n Best score: 0.8482%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat average_batch_r2_ct", + "value": 0.7587, + "severity": 0, + "severity_value": -0.7587, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: average_batch_r2_ct\n Worst score: 0.7587%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat average_batch_r2_ct", + "value": 0.7587, + "severity": 0, + "severity_value": 0.37935, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: average_batch_r2_ct\n Best score: 0.7587%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols average_batch_r2_ct", + "value": 0.8541, + "severity": 0, + "severity_value": -0.8541, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: average_batch_r2_ct\n Worst score: 0.8541%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols average_batch_r2_ct", + "value": 0.8541, + "severity": 0, + "severity_value": 0.42705, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: average_batch_r2_ct\n Best score: 0.8541%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm average_batch_r2_ct", + "value": 0.7234, + "severity": 0, + "severity_value": -0.7234, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: average_batch_r2_ct\n Worst score: 0.7234%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm average_batch_r2_ct", + "value": 0.7234, + "severity": 0, + "severity_value": 0.3617, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: average_batch_r2_ct\n Best score: 0.7234%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls average_batch_r2_ct", + "value": 0.8641, + "severity": 0, + "severity_value": -0.8641, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: average_batch_r2_ct\n Worst score: 0.8641%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls average_batch_r2_ct", + "value": 0.8641, + "severity": 0, + "severity_value": 0.43205, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: average_batch_r2_ct\n Best score: 0.8641%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration flowsom_mean_mapping_similarity", + "value": 0.0002, + "severity": 0, + "severity_value": -0.0002, + "code": "worst_score >= -1", + "message": "Method shuffle_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.0002%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration flowsom_mean_mapping_similarity", + "value": 0.0002, + "severity": 0, + "severity_value": 0.0001, + "code": "best_score <= 2", + "message": "Method shuffle_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.0002%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_batch flowsom_mean_mapping_similarity", + "value": 0, + "severity": 0, + "severity_value": -0.0, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_batch flowsom_mean_mapping_similarity", + "value": 0, + "severity": 0, + "severity_value": 0.0, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_batch\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score shuffle_integration_by_cell_type flowsom_mean_mapping_similarity", + "value": 0.9761, + "severity": 0, + "severity_value": -0.9761, + "code": "worst_score >= -1", + "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9761%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score shuffle_integration_by_cell_type flowsom_mean_mapping_similarity", + "value": 0.9761, + "severity": 0, + "severity_value": 0.48805, + "code": "best_score <= 2", + "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: shuffle_integration_by_cell_type\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9761%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score harmonypy flowsom_mean_mapping_similarity", + "value": 0.9817, + "severity": 0, + "severity_value": -0.9817, + "code": "worst_score >= -1", + "message": "Method harmonypy performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9817%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score harmonypy flowsom_mean_mapping_similarity", + "value": 0.9817, + "severity": 0, + "severity_value": 0.49085, + "code": "best_score <= 2", + "message": "Method harmonypy performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: harmonypy\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9817%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score limma_remove_batch_effect flowsom_mean_mapping_similarity", + "value": 0.9809, + "severity": 0, + "severity_value": -0.9809, + "code": "worst_score >= -1", + "message": "Method limma_remove_batch_effect performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9809%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score limma_remove_batch_effect flowsom_mean_mapping_similarity", + "value": 0.9809, + "severity": 0, + "severity_value": 0.49045, + "code": "best_score <= 2", + "message": "Method limma_remove_batch_effect performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: limma_remove_batch_effect\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9809%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score no_integration flowsom_mean_mapping_similarity", + "value": 0.9803, + "severity": 0, + "severity_value": -0.9803, + "code": "worst_score >= -1", + "message": "Method no_integration performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9803%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score no_integration flowsom_mean_mapping_similarity", + "value": 0.9803, + "severity": 0, + "severity_value": 0.49015, + "code": "best_score <= 2", + "message": "Method no_integration performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: no_integration\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9803%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_horizontal flowsom_mean_mapping_similarity", + "value": 1, + "severity": 0, + "severity_value": -1.0, + "code": "worst_score >= -1", + "message": "Method perfect_integration_horizontal performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_horizontal flowsom_mean_mapping_similarity", + "value": 1, + "severity": 0, + "severity_value": 0.5, + "code": "best_score <= 2", + "message": "Method perfect_integration_horizontal performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_horizontal\n Metric id: flowsom_mean_mapping_similarity\n Best score: 1%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score perfect_integration_vertical flowsom_mean_mapping_similarity", + "value": 0.9923, + "severity": 0, + "severity_value": -0.9923, + "code": "worst_score >= -1", + "message": "Method perfect_integration_vertical performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9923%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score perfect_integration_vertical flowsom_mean_mapping_similarity", + "value": 0.9923, + "severity": 0, + "severity_value": 0.49615, + "code": "best_score <= 2", + "message": "Method perfect_integration_vertical performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: perfect_integration_vertical\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9923%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score combat flowsom_mean_mapping_similarity", + "value": 0.981, + "severity": 0, + "severity_value": -0.981, + "code": "worst_score >= -1", + "message": "Method combat performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.981%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score combat flowsom_mean_mapping_similarity", + "value": 0.981, + "severity": 0, + "severity_value": 0.4905, + "code": "best_score <= 2", + "message": "Method combat performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: combat\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.981%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cycombine_nocontrols flowsom_mean_mapping_similarity", + "value": 0.9835, + "severity": 0, + "severity_value": -0.9835, + "code": "worst_score >= -1", + "message": "Method cycombine_nocontrols performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9835%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cycombine_nocontrols flowsom_mean_mapping_similarity", + "value": 0.9835, + "severity": 0, + "severity_value": 0.49175, + "code": "best_score <= 2", + "message": "Method cycombine_nocontrols performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cycombine_nocontrols\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9835%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score gaussnorm flowsom_mean_mapping_similarity", + "value": 0.9759, + "severity": 0, + "severity_value": -0.9759, + "code": "worst_score >= -1", + "message": "Method gaussnorm performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9759%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score gaussnorm flowsom_mean_mapping_similarity", + "value": 0.9759, + "severity": 0, + "severity_value": 0.48795, + "code": "best_score <= 2", + "message": "Method gaussnorm performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: gaussnorm\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9759%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Worst score cytonorm_controls flowsom_mean_mapping_similarity", + "value": 0.9838, + "severity": 0, + "severity_value": -0.9838, + "code": "worst_score >= -1", + "message": "Method cytonorm_controls performs much worse than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: flowsom_mean_mapping_similarity\n Worst score: 0.9838%\n" + }, + { + "task_id": "task_cyto_batch_integration", + "category": "Scaling", + "name": "Best score cytonorm_controls flowsom_mean_mapping_similarity", + "value": 0.9838, + "severity": 0, + "severity_value": 0.4919, + "code": "best_score <= 2", + "message": "Method cytonorm_controls performs a lot better than baselines.\n Task id: task_cyto_batch_integration\n Method id: cytonorm_controls\n Metric id: flowsom_mean_mapping_similarity\n Best score: 0.9838%\n" + } +] \ No newline at end of file diff --git a/results/cyto_batch_integration/data/results.json b/results/cyto_batch_integration/data/results.json new file mode 100644 index 00000000..469e6506 --- /dev/null +++ b/results/cyto_batch_integration/data/results.json @@ -0,0 +1,482 @@ +[ + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "combat", + "metric_values": { + "average_batch_r2_ct": 0.0523, + "average_batch_r2_global": 0.008, + "emd_max_ct_horiz": 1.2193, + "emd_max_global_horiz": 0.3177, + "emd_max_global_vert": 0.6408, + "emd_mean_ct_horiz": 0.1348, + "emd_mean_global_horiz": 0.0749, + "emd_mean_global_vert": 0.1587, + "flowsom_mean_mapping_similarity": 97.8579, + "n_inconsistent_peaks": 3, + "n_inconsistent_peaks_ct": 39 + }, + "scaled_scores": { + "average_batch_r2_ct": 0.7587, + "average_batch_r2_global": 0.7545, + "emd_max_ct_horiz": 0.5441, + "emd_max_global_horiz": 0.5295, + "emd_max_global_vert": 0.1831, + "emd_mean_ct_horiz": 0.7766, + "emd_mean_global_horiz": 0.6018, + 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0, + "emd_mean_global_vert": 0.6413, + "flowsom_mean_mapping_similarity": 0, + "n_inconsistent_peaks": 0, + "n_inconsistent_peaks_ct": 0 + }, + "mean_score": 0.0967, + "resources": { + "submit": "2025-05-23 12:44:43", + "exit_code": 0, + "duration_sec": 44.6, + "cpu_pct": 106.6, + "peak_memory_mb": 3482, + "disk_read_mb": 486, + "disk_write_mb": 480 + } + }, + { + "dataset_id": "leomazzi_cyto_spleen", + "method_id": "shuffle_integration_by_cell_type", + "metric_values": { + "average_batch_r2_ct": 0.0342, + "average_batch_r2_global": 0.0093, + "emd_max_ct_horiz": 1.235, + "emd_max_global_horiz": 0.2816, + "emd_max_global_vert": 0.6163, + "emd_mean_ct_horiz": 0.1328, + "emd_mean_global_horiz": 0.0907, + "emd_mean_global_vert": 0.1204, + "flowsom_mean_mapping_similarity": 97.3047, + "n_inconsistent_peaks": 4, + "n_inconsistent_peaks_ct": 68 + }, + "scaled_scores": { + "average_batch_r2_ct": 0.8421, + "average_batch_r2_global": 0.7157, + "emd_max_ct_horiz": 0.5382, + "emd_max_global_horiz": 0.583, + "emd_max_global_vert": 0.2146, + "emd_mean_ct_horiz": 0.78, + "emd_mean_global_horiz": 0.518, + "emd_mean_global_vert": 0.4341, + "flowsom_mean_mapping_similarity": 0.9761, + "n_inconsistent_peaks": 0.5, + "n_inconsistent_peaks_ct": 0.7655 + }, + "mean_score": 0.6243, + "resources": { + "submit": "2025-05-23 12:44:42", + "exit_code": 0, + "duration_sec": 56, + "cpu_pct": 92.4, + "peak_memory_mb": 3482, + "disk_read_mb": 486, + "disk_write_mb": 480 + } + } +] diff --git a/results/cyto_batch_integration/data/state.yaml b/results/cyto_batch_integration/data/state.yaml new file mode 100644 index 00000000..abbb0fc1 --- /dev/null +++ b/results/cyto_batch_integration/data/state.yaml @@ -0,0 +1,9 @@ +id: process +output_scores: !file results.json +output_method_info: !file method_info.json +output_metric_info: !file metric_info.json +output_dataset_info: !file dataset_info.json +output_task_info: !file task_info.json +output_qc: !file quality_control.json +output_metric_execution_info: !file metric_execution_info.json + diff --git a/results/cyto_batch_integration/data/task_info.json b/results/cyto_batch_integration/data/task_info.json new file mode 100644 index 00000000..44971ae3 --- /dev/null +++ b/results/cyto_batch_integration/data/task_info.json @@ -0,0 +1,50 @@ +{ + "task_id": "task_cyto_batch_integration", + "commit_sha": null, + "task_name": "Cyto Batch Integration", + "task_summary": "Benchmarking of batch integration algorithms for cytometry data.", + "task_description": "Cytometry is a non-sequencing single cell profiling technique commonly used in clinical studies. \nIt is very sensitive to batch effects, which can lead to biases in the interpretation of the result. \nBatch integration algorithms are often used to mitigate this effect.\n\nIn this project, we are building a pipeline for reproducible and continuous benchmarking \nof batch integration algorithms for cytometry data.\nAs input, methods require cleaned and normalised (using arc-sinh or logicle transformation)\ndata with multiple batches, cell type labels, and biological subjects, with paired samples\nfrom a subject profiled across multiple batches.\nThe batch integrated output must be an integrated marker by cell matrix stored in \nAnndata format.\nAll markers in the input data must be returned, regardless of whether they were integrated or not.\nThis output is then evaluated using metrics that assess how well the batch effects\nwere removed and how much biological signals were preserved. \n", + "repo": "https://github.com/openproblems-bio/task_cyto_batch_integration", + "issue_tracker": "https://github.com/openproblems-bio/task_cyto_batch_integration/issues", + "authors": [ + { + "name": "Luca Leomazzi", + "roles": ["author", "maintainer"], + "info": { + "github": "LuLeom" + } + }, + { + "name": "Givanna Putri", + "roles": ["author", "maintainer"], + "info": { + "github": "ghar1821", + "orcid": "0000-0002-7399-8014" + } + }, + { + "name": "Robrecht Cannoodt", + "roles": "author", + "info": { + "github": "rcannood", + "orcid": "0000-0003-3641-729X" + } + }, + { + "name": "Katrien Quintelier", + "roles": "contributor", + "info": { + "github": "KatrienQ" + } + }, + { + "name": "Sofie Van Gassen", + "roles": "contributor", + "info": { + "github": "SofieVG" + } + } + ], + "version": "build_main", + "license": "MIT" +} diff --git a/results/cyto_batch_integration/index.qmd b/results/cyto_batch_integration/index.qmd new file mode 100644 index 00000000..a5bb8857 --- /dev/null +++ b/results/cyto_batch_integration/index.qmd @@ -0,0 +1,22 @@ +--- +title: "Cyto Batch Integration" +subtitle: "Benchmarking of batch integration algorithms for cytometry data." +image: thumbnail.svg +page-layout: full +css: ../_include/task_template.css +engine: knitr +fig-cap-location: bottom +citation-location: document +bibliography: + - library.bib + - ../../library.bib +toc: false +--- + +```{r} +#| include: false +params <- list(data_dir = "results/cyto_batch_integration/data") +params <- list(data_dir = "./data") +``` + +{{< include ../_include/_task_template.qmd >}}