MARADONER

MARADONER: Motif Activity Response Analysis Done Right

MARADONER is a tool for analyzing motif activities using promoter expression data. It provides a streamlined workflow to estimate parameters, predict deviations, and export results in a tabular form.

Basic Workflow

A typical MARADONER analysis session involves running commands sequentially for a given project:

1. create: Initialize the project. This step parses your input files (promoter expression, motif loadings, optional motif expression, and sample groupings), performs initial filtering, and sets up the project's internal data structures.

   # Example: Initialize a project named 'my_project'
   maradoner create my_project path/to/expression.tsv path/to/loadings.tsv --sample-groups path/to/groups.json [other options...]

   • Input files are typically tabular (.tsv, .csv), potentially compressed.
   • You only need to provide input data files at this stage.

2. fit: Estimate the model's variance parameters and mean motif activities using the data prepared by create.

   maradoner fit my_project [options...]

3. predict: Estimate the deviations of motif activities from their means for each sample or group, based on the parameters estimated by fit.

   maradoner predict my_project [options...]

4. export: Save the final results, including estimated motif activities (mean + deviations), parameter estimates, goodness-of-fit statistics, and potentially statistical test results (like ANOVA) to a specified output folder.

   maradoner export my_project path/to/output_folder [options...]

Other Useful Commands

• gof: After fit, calculate Goodness-of-Fit statistics (like Fraction of Variance Explained or Correlation) to evaluate how well the model components explain the observed expression data.

  maradoner gof my_project [options...]

• select-motifs: If you provided multiple loading matrices in create (e.g., from different databases) with unique postfixes, this command helps select the single "best" variant for each motif based on statistical criteria. The output is a list of motif names intended to be used with the --motif-filename option in a subsequent create run.

  maradoner select-motifs my_project best_motifs.txt
  # Then, potentially re-run create using the generated list:
  # maradoner create my_project_filtered ... --motif-filename best_motifs.txt

• generate: Create a synthetic dataset with known properties for testing or demonstration purposes.

  maradoner generate path/to/synthetic_data_output [options...]

Getting Help

Each command has various options for customization. To see the full list of commands and their detailed options, use the --help flag:

maradoner --help
maradoner create --help
maradoner fit --help
# and so on for each command