Nextflow Pipeline for Running and Evaluating Multiple moDiNA Configurations

This pipeline enables running multiple moDiNA configurations in an reproducible and scalable environment and evaluating configurations using different schemes.

If you want to use the Python package, please refer to this repository.

A detailed documentation of the individual steps is provided at our readthedocs website: https://dyhealthnet.github.io/moDiNA/index.html.

Installation

If you are new to Nextflow, please refer to this page: https://nf-co.re/docs/usage/installation

Then clone the moDiNA Nextflow pipeline repository:

git clone https://github.com/DyHealthNet/moDiNA_nf_pipeline.git

TODO: create conda environments if this is not working automatically...

Parameters

The Nextflow pipeline is designed to run multiple configurations in parallel, allowing users to systematically evaluate different settings and their impact on the results. To specify the configurations, you can either change the parameters in the nextflow.config file or create a configuration file (e.g., params.yml).

General Parameters

Parameter	Type	Default	Description
name_context_1	string	context1	Label assigned to the first context (e.g. Female or Control).
name_context_2	string	context2	Label assigned to the second context (e.g. Male or Disease).
run_type	string	file	Determines how moDiNA configurations are specified. One of single (one fixed configuration), all (all valid combinations), or file (read from a CSV samplesheet).
data_type	string	simulation	Source of input data. Either simulation (generate synthetic data via copula) or real (provide real-world data files).
out_dir	path	''	Absolute path to the directory where all pipeline outputs are written.

Environment Parameters

Parameter	Type	Description
conda_modina_env	path	Absolute path to the conda environment used for all core moDiNA processes (network inference, ranking, …).
conda_eval_env	path	Absolute path to the conda environment used for R-based evaluation process.

Simulation Parameters (simulation.*)

Parameter	Type	Default	Description
simulation.n_simulations	integer	3	Number of independent simulation replicates to run.
simulation.n_bi	integer	50	Number of binary nodes to simulate.
simulation.n_cont	integer	50	Number of continuous nodes to simulate.
simulation.n_cat	integer	50	Number of categorical nodes to simulate.
simulation.n_samples	integer	500	Number of samples (observations) per context.
simulation.n_shift_cont	integer	4	Number of continuous nodes with an artificially introduced mean shift between contexts.
simulation.n_shift_bi	integer	4	Number of binary nodes with an artificially introduced mean shift between contexts.
simulation.n_shift_cat	integer	4	Number of categorical nodes with an artificially introduced mean shift between contexts.
simulation.n_corr_cont_cont	integer	2	Number of continuous–continuous node pairs with an artificially introduced correlation difference between contexts.
simulation.n_corr_bi_bi	integer	2	Number of binary–binary node pairs with an artificially introduced correlation difference between contexts.
simulation.n_corr_cat_cat	integer	2	Number of categorical–categorical node pairs with an artificially introduced correlation difference between contexts.
simulation.n_corr_bi_cont	integer	2	Number of binary–continuous node pairs with an artificially introduced correlation difference between contexts.
simulation.n_corr_bi_cat	integer	2	Number of binary–categorical node pairs with an artificially introduced correlation difference between contexts.
simulation.n_corr_cont_cat	integer	2	Number of continuous–categorical node pairs with an artificially introduced correlation difference between contexts.
simulation.n_both_cont_cont	integer	2	Number of continuous–continuous node pairs with both a mean shift and a correlation difference.
simulation.n_both_bi_bi	integer	2	Number of binary–binary node pairs with both a mean shift and a correlation difference.
simulation.n_both_cat_cat	integer	2	Number of categorical–categorical node pairs with both a mean shift and a correlation difference.
simulation.n_both_bi_cont	integer	2	Number of binary–continuous node pairs with both a mean shift and a correlation difference.
simulation.n_both_bi_cat	integer	2	Number of binary–categorical node pairs with both a mean shift and a correlation difference.
simulation.n_both_cont_cat	integer	2	Number of continuous–categorical node pairs with both a mean shift and a correlation difference.
simulation.shift	float	0.5	Magnitude of the mean shift introduced in shifted nodes. Must be ≥ 0.
simulation.corr	float	0.7	Target correlation difference introduced in correlated node pairs. Must be in [0, 1].

Real-World Data Parameters (real_world_data.*)

Only required when data_type = 'real'.

Parameter	Type	Default	Description
real_world_data.path_context_1	path	''	Absolute path to the CSV data file for context 1 (rows: samples, columns: variables).
real_world_data.path_context_2	path	''	Absolute path to the CSV data file for context 2 (rows: samples, columns: variables).
real_world_data.path_meta	path	''	Absolute path to the metadata CSV file containing variable labels and data types.

Differential Network Analysis Parameters (diff_net_analysis.*)

Parameter	Type	Default	Description
diff_net_analysis.path_config	path	'input_samplesheet.csv'	Path to the CSV samplesheet listing moDiNA configurations to run. Required when run_type = 'file'. Columns: node_metric, edge_metric, ranking_algorithm.
diff_net_analysis.node_metric	string	'STC'	Node-level differential metric. Used when run_type = 'single'. One of: STC, DC-P, DC-E, WDC-P, WDC-E, PRC-P, None.
diff_net_analysis.edge_metric	string	'pre-LS'	Edge-level differential metric. Used when run_type = 'single'. One of: pre-P, pre-E, post-E, post-P, int-IS, pre-LS, post-LS, pre-PE, post-PE, None.
diff_net_analysis.ranking_algorithm	string	'PageRank+'	Algorithm used to rank nodes and edges in the differential network. Used when run_type = 'single'. One of: PageRank+, PageRank, absDimontRank, DimontRank, direct_node, direct_edge.
diff_net_analysis.filter_method	string	''	Optional method to filter context-specific networks before differential analysis. One of: degree, density, quantile. Leave empty to skip filtering.
diff_net_analysis.filter_param	integer / float	2	Parameter for the chosen filter method. Integer for degree; float in (0, 1) for density and quantile.
diff_net_analysis.filter_metric	string	'pre-P'	Edge score used as the basis for filtering. One of: pre-P, pre-E. Required when filter_method is set.
diff_net_analysis.filter_rule	string	'zero'	Rule applied when filtering edges. One of: zero (set filtered edges to zero), union (keep edges present in at least one context).
diff_net_analysis.max_path_length	integer	2	Maximum path length used for the int-IS edge metric. Must be an integer in [0, 4]. Only relevant when int-IS is selected as an edge metric.
diff_net_analysis.test_type	string	'nonparametric'	Type of statistical test used during context network inference. One of: parametric, nonparametric.
diff_net_analysis.nan_value	integer	-89	Sentinel value in input data that represents missing / NA values.
diff_net_analysis.multiple_testing	string	'bh'	Multiple testing correction method applied to association p-values. One of: bh (Benjamini–Hochberg), by (Benjamini–Yekutieli).

Usage

You can choose between the following profiles: conda, docker, singularity, slurm. The respective profile will be used to execute the pipeline with the appropriate containerization or job scheduling system. You can specify the profile using the -profile flag when running the Nextflow pipeline. For example, to run the pipeline using conda for package management and SLURM for distributed execution, you would use the following command:

Now, you can run the Nextflow pipeline using the following command:

nextflow run main.nf -profile conda,slurm

If you have stored the parameters in a params.yml file instead of the nextflow.config file, you can specify the path to this file using the -params-file flag:

nextflow run main.nf -profile conda,slurm -params-file path/to/your/params.yml

Pipeline Output

The results are stored in the specified output directory, which is organized by differential network analysis steps and evaluation steps.

The main output of the pipeline includes: - Simulated datasets for each simulation replicate (if data_type = 'simulation'). - Inferred context-specific networks for each context and simulation. - Node and edge metrics files for each configuration and simulation. - Ranking results for each configuration and simulation. - Evaluation results

Citation

TODO