Knowledge discovery in large and complex 'omics data sets remains a key challenge in current life sciences. A common practice is to apply computational workflows to 'predigest' the big data into lists of significant features or functions. Although very useful, these 'predigestion' procedures based on meta-data of interest and pior knowledge can lead to loss of potentially important information. To address this issue, we developed OmicsAnalyst to allow researchers to work with big omics data using data-driven methods and prior knowledge, by integrating three key techniques - dimensionality reduction, network integration, and 3D visual analytics with applications for single-cell RNAseq and multi-omics data analysis. Its key features include:

  • Supporting various dimensionality reduction algorithms (PCA, MDS, isomap, t-SNE, and UMAP) for scRNAseq analysis;
  • Supporting well-established multivariate methods (Procrustes analysis, multiple co-inertia analysis, and regularized CCA) for multi-omics integration;
  • Navigating the data space through clusters analysis and 3D visual analytics;
  • Interpreting clusters through exploring impactful features and functional enrichment analysis

List Integration

Starting with multiple lists of significant molecules using knowledge-based networks

Matrix Integration

Starting with different omics tables (abundance profiles) using multivariate methods

Hybrid Integration

Flexible integration of different data types using statistical- and knowledge-driven approach.

Tutorial Webinars

Different video tutorials introducing the key features and common analysis tracks.


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