Merge branch 'development' into pub_fig

Author: JudithBernett

.github/pull_request_template.md

@@ -38,3 +38,12 @@ Then,
 1. Open a PR from development to main with these changes.
 2. Wait for a review and merge.
 3. Create a new release on GitHub with the version number. Update the release notes with the changes made in this version.
+4. If the Docker github action fails (e.g., no space left on device), you can do it manually:
+   - Enable Docker buildx locally. If you don't have a builder that supports multi-arch, create one:
+   ```{bash}
+   docker buildx ls
+   docker buildx create --use --name multiarch-builder
+   docker buildx inspect --bootstrap
+   ```
+   - Login to ghcr `docker login --username <GitHub-Username> --password <personal-access-token> ghcr.io`
+   - Build the multi-platform image, insert the correct version `docker buildx build --platform linux/amd64,linux/arm64 -t ghcr.io/daisybio/drevalpy:v<INSERT-VERSION> -t ghcr.io/daisybio/drevalpy:latest --push .`

.gitignore

@@ -9,6 +9,7 @@ data/TOYv1
 data/TOYv2
 data/CTRPv1
 data/CTRPv2
+data/meta
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -22,30 +22,34 @@ By contributing your model to the DrEval catalog, you can increase your work's e
 
 ![DrEval](docs/_static/img/overview.png)
 
-Use DrEval to Build Drug Response Models That Have an Impact
+---
 
-    1. Maintained, up-to-date baseline catalog, no need to re-implement literature models
+Use DrEval to build drug response models that have an impact
 
-    2. Gold standard datasets for benchmarking
+1. Maintained, up-to-date baseline catalog, no need to re-implement literature models
+2. Gold standard datasets for benchmarking
+3. Consistent application-driven evaluation
+4. Ablation studies with permutation tests
+5. Cross-study evaluation for generalization analysis
+6. Optimized nextflow pipeline for fast experiments
+7. Easy-to-use hyperparameter tuning
+8. Paper-ready visualizations to display performance
 
-    3. Consistent application-driven evaluation
-
-    4. Ablation studies with permutation tests
-
-    5. Cross-study evaluation for generalization analysis
-
-    6. Optimized nextflow pipeline for fast experiments
-
-    7. Easy-to-use hyperparameter tuning
-
-    8. Paper-ready visualizations to display performance
+---
 
 This project is a collaboration of the Technical University of Munich (TUM, Germany)
 and the Freie Universität Berlin (FU, Germany).
 
+## Demo
+
 Check out our demo notebook in Colab:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/daisybio/drevalpy/blob/development/dreval_colab_demo.ipynb)
 
+Expected runtime on a normal machine:
+
+- Standalone demo: 38 minutes
+- Nextflow demo: 5 minutes
+
 ## Installation
 
 Using pip:
@@ -176,34 +180,70 @@ nextflow run nf-core/drugresponseeval \
     -profile docker \
     --run_id ec50_run \
     --dataset_name CTRPv2 \
-    --cross_study_datasets CTRPv1,CCLE,GDSC1,GDSC2 \
+    --cross_study_datasets CTRPv1,CCLE,GDSC1,GDSC2,PDX_Bruna,BeatAML2 \
     --models RandomForest \
     --baselines NaiveMeanEffectsPredictor \
-    --test_mode LPO,LCO,LTO,LDO \
+    --test_mode LCO \
     --measure pEC50
 
 # AUC run
 nextflow run nf-core/drugresponseeval \
     -profile docker \
     --run_id auc_run \
     --dataset_name CTRPv2 \
-    --cross_study_datasets CTRPv1,CCLE,GDSC1,GDSC2 \
+    --cross_study_datasets CTRPv1,CCLE,GDSC1,GDSC2,PDX_Bruna,BeatAML2 \
     --models RandomForest \
     --baselines NaiveMeanEffectsPredictor \
-    --test_mode LPO,LCO,LTO,LDO \
+    --test_mode LCO \
     --measure AUC
 
 # Invariant ablation run
+# Run this on CPU
 nextflow run nf-core/drugresponseeval \
     -profile docker \
-    --run_id invariant_ablation \
+    --run_id invariant-rf \
     --dataset_name CTRPv2 \
-    --models MultiOmicsRandomForest,DIPK \
+    --models MultiOmicsRandomForest \
     --baselines NaiveMeanEffectsPredictor \
     --test_mode LPO,LCO,LDO \
     --randomization_mode SVRC,SVRD \
     --randomization_type invariant \
     --measure LN_IC50
+
+# modify the profile to run this on GPU, if possible
+nextflow run nf-core/drugresponseeval \
+    -profile docker \
+    --run_id invariant-dipk \
+    --dataset_name CTRPv2 \
+    --models DIPK \
+    --baselines NaiveMeanEffectsPredictor \
+    --test_mode LPO,LCO,LDO \
+    --randomization_mode SVRC,SVRD \
+    --randomization_type invariant \
+    --measure LN_IC50
+
+## Inference on BeatAMl2, PDX_Bruna
+# run this on CPU
+nextflow run nf-core/drugresponseeval \
+    -profile docker \
+    --run_id infer_pdx_beat \
+    --dataset_name CTRPv2 \
+    --cross_study_datasets PDX_Bruna,BeatAML2 \
+    --models RandomForest,SimpleNeuralNetwork,GradientBoosting,SRMF,ElasticNet,NaivePredictor,NaiveDrugMeanPredictor,NaiveCellLineMeanPredictor \
+    --baselines NaiveMeanEffectsPredictor \
+    --test_mode LPO,LCO,LDO \
+    --measure LN_IC50
+
+# modify profile to run this on GPU, if possible
+nextflow run nf-core/drugresponseeval \
+    -profile docker \
+    --run_id dipk_pdx_beat \
+    --dataset_name CTRPv2 \
+    --cross_study_datasets PDX_Bruna,BeatAML2 \
+    --models DIPK \
+    --baselines NaiveMeanEffectsPredictor \
+    --test_mode LPO,LCO,LDO \
+    --measure LN_IC50
 ```
 
 ## Contact

docs/conf.py

@@ -56,9 +56,9 @@
 # the built documents.
 #
 # The short X.Y version.
-version = "1.3.6"
+version = "1.4.0"
 # The full version, including alpha/beta/rc tags.
-release = "1.3.6"
+release = "1.4.0"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.