FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data.

ABSTRACT:

Motivation

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.

Results

To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets

SUBMITTER: Koutrouli M 

PROVIDER: S-EPMC10868155 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature