MIMOmics Software

MIMOmics has developed several methods for the analysis of (multiple) omics datasets. For many methods code has been made available our MIMOmics github (https://github.com/mimomics). For some methods user-friendly R packages, Java interfaces, Python libraries or programs in C++ have been developed. For other methods just code has been made available:

Data Cleaning


  • R-code for construction of networks regarding specific covariates.

Sparse Supervised Gaussian methods

  • SSG Python Library to run various methods, to visualise the learned models, and to compute performance indices.

Data integration and dimension reduction

  • OmicsPLS R-package which performs O2PLS data integration method for two datasets yielding joint and data-specific parts for each dataset. The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
  • https://cran.r-project.org/web/packages/OmicsPLS/index.html
  • R code to perform Probabilistic Partial Least Squares.

Prediction using omics datasets

  • Augmented prediction. R code to perform constrained regularized regression to determine added value of one omic dataset on top of another omic dataset.
  • PredNet. R code for combining network and prediction models to obtain stable and better interpretable prediction models.

Genome Wide Association Analysis

  • MultiABEL R package to perform Multi-Trait GWAS.
  • ProABEL C++ code for genome-wide association analysis of imputed genetic data.
  • OmicABELnoMM C++ code for a high-performance computing algorithm for linear regression of omics data.
  • OmicABEL C code for high-performance implementation of genome-wide association analysis using mixed models.

Using the geneXplain platform, an online toolbox and workflow management system for a broad range of bioinformatic and systems biology applications

  • geneXplainr a R-package which provides an R client for the geneXplain platform.
  • geneXplain API a Java programming interface.