nf-core/drugresponseeval
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Pipeline for testing drug response prediction models in a statistically and biologically sound way.

cell-linescross-validationdeep-learningdrug-responsedrug-response-predictiondrugsfair-principlesgeneralizationhyperparameter-tuningmachine-learningrandomization-testsrobustness-assessmenttraining

This is the development version of the pipeline.

Launch development version https://github.com/nf-core/drugresponseeval

Introduction

DrugResponseEval is a workflow designed to ensure that drug response prediction models are evaluated in a consistent and reproducible manner. We offer three settings:

Leave-Pair-Out (LPO): Random pairs of cell lines and drugs are left out for testing but both the drug and the cell line might already be present in the training set. This is the easiest setting for your model but also the most uninformative one. The only application scenario for this setting is when you want to test whether your model can complete the missing values in the training set.
Leave-Cell-Line-Out (LCO): Random cell lines are left out for testing but the drugs might already be present in the training set. This setting is more challenging than LPO but still relatively easy. The application scenario for this setting is when you want to test whether your model can predict the response of a new cell line. This is very relevant for personalized medicine or drug repurposing.
Leave-Drug-Out (LDO): Random drugs are left out for testing but the cell lines might already be present in the training set. This setting is the most challenging one. The application scenario for this setting is when you want to test whether your model can predict the response of a new drug. This is very relevant for drug development.

An underlying issue is that drugs have a rather unique IC50 range. That means that by just predicting the mean IC50 that a drug has in the training set (aggregated over all cell lines), you can already achieve a rather good prediction. This is why we also offer the possibility to compare your model to a NaivePredictor that predicts the mean IC50 of all drugs in the training set. We also offer two more advanced naive predictors: NaiveCellLineMeanPredictor and NaiveDrugMeanPredictor. The former predicts the mean IC50 of a cell line in the training set and the latter predicts the mean IC50 of a drug in the training set.

Furthermore, we offer a variety of more advanced baseline models and some state-of-the-art models to compare your model against. Similarly, we provide commonly used datasets to evaluate your model on (GDSC1, GDSC2, CCLE, CTRPv2). You can also provide your own dataset or your own model by contributing to our PyPI package drevalpy Before contributing, you can pull our respective repositories. More information can be found in the drevalpy readthedocs.

We first identify the best hyperparameters for all models and baselines in a cross-validation setting. Then, we train the models on the whole training set and evaluate them on the test set. Furthermore, we offer randomization and robustness tests.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/drugresponseeval \
   -profile <docker/singularity/.../institute> \
   --run_id myRun \
   --test_mode <LPO/LCO/LDO> \
   --models <model1,model2,...> \
   --baselines <baseline1,baseline2,...> \
   --dataset_name <dataset_name> \
   --path_data <path_data>

This will launch the pipeline with the docker/singularity/.../institute configuration profile. See below for more information about profiles.

In your outdir, a folder named myRun will be created containing the results of the pipeline run.

The test_mode parameter specifies the evaluation setting, e.g., --test_mode LCO.

The models and baselines parameters are lists of models and baselines to be evaluated, e.g., --models ElasticNet,RandomForest --baselines NaivePredictor,NaiveCellLineMeanPredictor,NaiveDrugMeanPredictor.

The dataset_name parameter specifies the dataset to be used for evaluation, e.g., --dataset_name GDSC2.

If you do not want to re-download the data every time you run the pipeline, you can specify the path to the data with the path_data parameter, e.g., --path_data /path/to/data.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir), defaults to 'results'
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

Warning

Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/drugresponseeval -profile docker -params-file params.yaml

with:

params.yaml

models: 'ElasticNet'
baselines: 'NaivePredictor,NaiveCellLineMeanPredictor,NaiveDrugMeanPredictor'
dataset_name: 'GDSC2'
path_data: '/path/to/data'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Available Models

Single-Drug Models fit one model for each drug in the training set. They also cannot generalize to new drugs, hence those models cannot be used in the LDO setting. Multi-Drug Models fit one model for all drugs in the training set. They can be used in all three settings.

The following models are available:

Model Name	Baseline / Published Model	Multi-Drug Model / Single-Drug Model	Description
NaivePredictor	Baseline Method	Multi-Drug Model	Most simple method. Predicts the mean response of all drugs in the training set.
NaiveCellLineMeanPredictor	Baseline Method	Multi-Drug Model	Predicts the mean response of a cell line in the training set.
NaiveDrugMeanPredictor	Baseline Method	Multi-Drug Model	Predicts the mean response of a drug in the training set.
ElasticNet	Baseline Method	Multi-Drug Model	Fits an Sklearn Elastic Net, Lasso, or Ridge model on gene expression data and drug fingerprints (concatenated input matrix).
GradientBoosting	Baseline Method	Multi-Drug Model	Fits an Sklearn Gradient Boosting Regressor gene expression data and drug fingerprints.
RandomForest	Baseline Method	Multi-Drug Model	Fits an Sklearn Random Forest Regressor on gene expression data and drug fingerprints.
MultiOmicsRandomForest	Baseline Method	Multi-Drug Model	Fits an Sklearn Random Forest Regressor on gene expression, methylation, mutation, copy number variation data, and drug fingerprints (concatenated matrix). The dimensionality of the methylation data is reduced with a PCA to the first 100 components before it is fed to the model.
SingleDrugRandomForest	Baseline Method	Single-Drug Model	Fits an Sklearn Random Forest Regressor on gene expression data for each drug separately.
SVR	Baseline Method	Multi-Drug Model	Fits an Sklearn Support Vector Regressor gene expression data and drug fingerprints.
SimpleNeuralNetwork	Baseline Method	Multi-Drug Model	Fits a simple feedforward neural network (implemented with Pytorch Lightning) on gene expression and drug fingerprints (concatenated input) with 3 layers of varying dimensions and Dropout layers.
MultiOmicsNeuralNetwork	Baseline Method	Multi-Drug Model	Fits a simple feedforward neural network (implemented with Pytorch Lightning) on gene expression, methylation, mutation, copy number variation data, and drug fingerprints (concatenated input) with 3 layers of varying dimensions and Dropout layers. The dimensionality of the methylation data is reduced with a PCA to the first 100 components before it is fed to the model.
SRMF	Published Model	Multi-Drug Model	Similarity Regularization Matrix Factorization model by Wang et al. on gene expression data and drug fingerprints. Re-implemented Matlab code into Python. The basic idea is represent each drug and each cell line by their respective similarities to all other drugs/cell lines. Those similarities are mapped into a shared latent low-dimensional space from which responses are predicted.
MOLIR	Published Model	Single-Drug Model	Regression extension of MOLI: multi-omics late integration deep neural network. by Sharifi-Noghabi et al. Takes somatic mutation, copy number variation and gene expression data as input. MOLI reduces the dimensionality of each omics type with a hidden layer, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. We implemented a regression adaption with MSE loss and an adapted triplet loss for regression.
SuperFELTR	Published Model	Single-Drug Model	Regression extension of SuperFELT: supervised feature extraction learning using triplet loss for drug response by Park et al. Very similar to MOLI(R). In MOLI(R), encoders and the classifier were trained jointly. Super.FELT(R) trains them independently. MOLI(R) was trained without feature selection (except for the Variance Threshold on the gene expression). Super.FELT(R) uses feature selection for all omics data.
DIPK	Published Model	Multi-Drug Model	Deep neural network Integrating Prior Knowledge from Li et al. Uses gene interaction relationships (encoded by a graph auto-encoder), gene expression profiles (encoded by a denoising auto-encoder), and molecular topologies (encoded by MolGNet). Those features are integrated using multi-head attention layers.

Available Datasets

The following datasets are available and can be supplied via --dataset_name:

Dataset Name	Number of drugs	Number of Cell Lines	Description
GDSC1	345	987	The Genomics of Drug Sensitivity in Cancer (GDSC) dataset version 1.
GDSC2	192	809	The Genomics of Drug Sensitivity in Cancer (GDSC) dataset version 2.
CCLE	18	471	The Cancer Cell Line Encyclopedia (CCLE) dataset. The response data will soon be replaced with the data from CTRPv2.

Our pipeline also supports cross-study prediction, i.e., training on one dataset and testing on another (or multiple others) to assess the generalization of the model. This dataset name can be supplied via --cross_study_datasets.

Available Randomization Tests

We have several randomization modes and types available.

The modes are supplied via --randomization_mode and the types via --randomization_type.:

SVCC: Single View Constant for Cell Lines: A single cell line view (e.g., gene expression) is held unperturbed while the others are randomized.
SVCD: Single View Constant for Drugs: A single drug view (e.g., drug fingerprints) is held unperturbed while the others are randomized.
SVRC: Single View Random for Cell Lines: A single cell line view (e.g., gene expression) is randomized while the others are held unperturbed.
SVRD: Single View Random for Drugs: A single drug view (e.g., drug fingerprints) is randomized while the others are held unperturbed.

Currently, we support two ways of randomizing the data. The default is permututation.

Permutation: Permutes the features over the instances, keeping the distribution of the features the same but dissolving the relationship to the target.
Invariant: The randomization is done in a way that a key characteristic of the feature is preserved. In case of matrices, this is the mean and standard deviation of the feature view for this instance, for networks it is the degree distribution.

Robustness Tests

The robustness test is a test where the model is trained with varying seeds. This is done multiple times to see how stable the model is. Via --n_trials_robustness, you can specify the number of trials for the robustness tests.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/drugresponseeval

Reproducibility

It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/drugresponseeval releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Tip

If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments

Note

These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).

`-profile`

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

Info

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer enviroment.

test
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
docker
- A generic configuration profile to be used with Docker
singularity
- A generic configuration profile to be used with Singularity
podman
- A generic configuration profile to be used with Podman
shifter
- A generic configuration profile to be used with Shifter
charliecloud
- A generic configuration profile to be used with Charliecloud
apptainer
- A generic configuration profile to be used with Apptainer
wave
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
conda
- A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

`-resume`

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

`-c`

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

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