Introducing Liver OmixAtlas

What is Liver OmixAtlas?

Elucidata’s Liver OmixAtlas, the largest, multi-omics collection of liver tissue-derived data from humans, mice, and rats. It contains 9 different types of data derived from 10 public sources, encompassing normal as well as perturbed (disease, drug treatment, gene knockout/knockdown, etc.) states of liver tissue.

Why Did We Build a Liver OmixAtlas?

Every year, vast amounts of biological multi-omics data are being generated and made public by academic labs and organizations worldwide. These data hold tremendous potential for reuse and discovery but are scattered across multiple, disparate sources and lack standardization. Thus, the availability of data does not equate to its easy usability, making the need for an efficient means of exploring molecular data an immediate necessity. 

Additionally, biological systems and processes are intrinsically complex. No single type of data, be it metabolomic, proteomic, or genomic, will be sufficient to capture the complexity of biological phenomena. Adopting an integrated approach could significantly aid the ability to gain a holistic and more accurate understanding of physiology and disease pathology at the molecular level. 

Elucidata’s Liver OmixAtlas addresses the above issues by ensuring that the metadata across different public resources has been curated and harmonized and made ready for downstream machine learning and analytical applications.

A Deep Dive into Liver OmixAtlas:

How are the Data Available in Liver OmixAtlas Different from the Data Available Directly from the Public Resources?

The data in Liver OmixAtlas are curated through Polly’s ML-based curation workflow that structures different types of data, harmonizes metadata, and makes the data analysis ready. All data available in Liver OmixAtlas can be queried and directly used in downstream statistical or ML-based analyses.

Data available in LiverOmix Atlas have the following harmonized metadata:

1. Data descriptors

  • Dataset_id
  • Sample_id
  • Feature_id
  • Description
  • data type
  • dataset source

2. Metadata to identify the biological system and samples being studied:

  • organism
  • tissue
  • cell line
  • cell type
  • disease

3. Metadata to identify specific perturbations:

  • drug
  • genetic modification - type and gene

4. Metadata to understand experiment design

  • experimental factors (*only applicable for GEO, LINCS, Metabolights, metabolomics workbench)
  • total number of samples
  • total number of cells (only applicable for single cell data)

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