v1.3.5

Changes

• --curve_curator arg is replaced with --no_refitting (default False)
• Changed GPL-3 license to MIT license
• _split_early_stopping_data -> no longer private function because it is also used in the pipeline
• Updated which functions are used in the pipeline

Bug fixes

• Meaningful error message when visualization does not work because of an empty dataframe

New features

• CLI interface is now drevalpy --help and drevalpy-report --helpaccessible also without cloning. Removed run_suite.py and create_report.py: Command Line Interfaces (#243) @PascalIversen
• We added a demo notebook for an easy quickstart :)

Maintenance

• New package versions

v1.3.4

Changes

• Updated versions but no support for scipy 1.16.0 yet because of statsmodels/statsmodels#9584 (comment)
• Dataset.py: FeatureDataset from_csv now extracts meta info, to_csv saves it
• Removed hard-coded cell line/drug identifiers in visualization/utils.py

🐛 Bug Fixes

• various fixes (#235) @PascalIversen:
  • Fixed inverse transform in cross_study_prediction
  • Inverse transforming in train_and_predict for train and early stopping as well now
  • Removed nested final_model directory
  • Copying drug features before supplying it to train/predict functions, too, now. Forgot that previously

v.1.3.3

Changes

• Bumped Dockerfile to Python 3.13, too, and added unzip for Nextflow

v1.3.2

Preprint is out! Linking it in the README

Changes

• [DEPENDABOT]: Bump codecov/codecov-action from 5.4.2 to 5.4.3 (#218) @dependabot[bot]
• Transformation on the features like scaling, variance feature selection, … are now implemented as sklearn transformer so that they can be fitted in train and applied for predicting. Because of that, cell line/drug features are now copied in experiment.py
• Added SingleDrugProteomicsRandomForest, ProteomicsElasticNet
• Made Naive Models nicer/less redundant
• Forcing NaiveMeanEffectsPredictor to be always run

🚀 Features

• Optional final production model fitting procedure after CV (#221) @PascalIversen. Flag final_model_on_full data: if True, a final/production model is saved in the results directory. If hyperparameter_tuning is true, the final model is produced according to the hyperparameter tuning procedure, which was evaluated in the nested cross-validation. Accordingly, all models now have save() and load() functions.
• Memes on the documentation page!
• Added no_hyperparameter_tuning flag

🐛 Bug Fixes

• Fix Feature Scaling (#220) @PascalIversen
• CurveCurator: name the new data columns like the constants CELL_LINE_IDENTIFIER (=cell_line_name), DRUG_IDENTIFIER (=pubchem_id) instead of "cell_line_id", "drug_id"
• response from_csv: look for measure, cell line identifier, and drug identifier column; cast drug identifier to str; same for to_dataframe.
• feature from_csv: forgot metainfo, some fixes
• load_custom, evaluate_file: forgot dataset_name

🧰 Maintenance

• Bump to Python 3.13 (now supporting 3.11, 3.12, 3.13)
• New versions, separate pre-commit action

v1.3.1

Small Changes

• Defaults for visualization result and data directory
• Fixed bug that CD diagram is overwritten
• Updated documentation
• New package versions

v1.3.0

Changes

• New support for Leave-tissue-out cross-validation (#192) @PascalIversen
• Implemented ProteomicsRF (#183) @JudithBernett
• Included NaiveTissueMeanPredictor
• New safety scan
• [DEPENDABOT]: Bump codecov/codecov-action from 5.4.0 to 5.4.2 (#196) @dependabot[bot]
• [DEPENDABOT]: Bump crazy-max/ghaction-github-labeler from 5.2.0 to 5.3.0 (#179) @dependabot[bot]
• [DEPENDABOT]: Bump actions/cache from 4.2.2 to 4.2.3 (#172) @dependabot[bot]

🐛 Bug Fixes

• fix LTO for low tissue validations sets (#202) @PascalIversen
• Lots of restructuring in visualization, kick out partial correlation, instead normalized metrics (#81) @JudithBernett
• fix measure being not picked for datasets (#186) @PascalIversen
• Multiomics input fix for MultiOmicsNN (#201) @JudithBernett
• Fixed an error for early splitting for when there are less than 4 groups in validation

🧰 Maintenance

• Update README.md (#198) @PascalIversen
• Link Hosted Report in Readme (#187) @PascalIversen

v1.2.7

Changes

• forced NaiveMeanEffectsPredictor (#169) @PascalIversen

🐛 Bug Fixes

• fix logic when no baseline is selected (#176) @PascalIversen
• fixed randomization. (#180) @PascalIversen
• Minor bug fixes for DIPK, MOLIR, and SuperFELTR (#161) @JudithBernett

v1.2.6

Changes

• forced NaiveMeanEffectsPredictor (#169) @PascalIversen
• updated requirements
• test restructuring: now baseline tests, single drug model tests, neural network tests for non-single drug models, adapted tests for the bugs
• MultiOmicsNN: removed unused self.methylation_features

🐛 Bug Fixes

• Minor bug fixes for DIPK, MOLIR, and SuperFELTR (#161) @JudithBernett
  • DIPK fix for batch size 1
  • MOLIR/SuperFELTR: removed VarianceThreshold selection. Now always selecting min(1000, n_features) most variable features from the dataset
  • MOLIR/SuperFELTR fix for if self.model is None
  • MOLIR/SuperFELTR: moved duplicated code to utils

v1.2.5

Changes

• New package versions

🐛 Bug Fixes

• Metrics fix (#159) @JudithBernett : If there were NaNs in the predicted values, the metrics could not be computed -> also adapted test

v1.2.4

Many things were going wrong for cross study prediction -> fixed

Changes

• Instead of Toy_Data, we now have 2 datasets: TOYv1 (subsetted from CTRPv2) and TOYv2 (subsetted from GDSC2) for testing cross study
• We needed lists that contain the intersection of all OMICs features across datasets
• rename Toy_Data and add TOYv2 for testing cross study predictions (#153) @PascalIversen
• load_and_reduce_gene_features -> load_and_select_gene_features
• New versions of packages
• [DEPENDABOT]: Bump actions/cache from 4.2.1 to 4.2.2 (#146) @dependabot[bot]
• [DEPENDABOT]: Bump codecov/codecov-action from 5.3.1 to 5.4.0 (#145) @dependabot[bot]

🐛 Bug Fixes

• Bug for pipeline: consolidate single drug model predictions was receiving cross study datasets instead of the dataset names: Consolidation fix (#156) @JudithBernett
• The features have to have the same order for all datasets -> now happens in load_and_select_gene_features: fix the order of genes across datasets when selecting a gene/methylation island subset of features (#154) @PascalIversen
• Bug for MOLIR, SuperFELTR: meta-info of feature dataset was not subsetted for variancethreshold filtering
• Bug for MOLIR, SuperFELTR: features used in training have to be remembered such that the cross-study prediction dataset can be subsetted
• Bug for MultiOMICs RF: methylation features have to occur in both datasets -> now uses list
• DIPK fix: gene expression data has to be encoded again for cross study prediction
  updated tests