Overview

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 in a data-driven and unbiased manner, by integrating three key techniques - dimensionality reduction, unsupervised clustering, 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


Sample Space - Elucidating Global Patterns
Feature Space - Enabling Targeted Analytics
Joint Space - Revealing Driving Forces

Acknowledgement

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