Methods for evaluating unsupervised vector representations of genomic regions.

Area of Science:

• Genomics
• Bioinformatics
• Machine Learning

Background:

• Representation learning models generate embeddings for biological entities like genomic regions.
• Unsupervised methods learn genomic region relationships from data, bypassing curated metadata.
• Evaluating unsupervised embeddings is challenging due to the lack of metadata for quality assessment.

Purpose of the Study:

• To develop novel evaluation metrics for unsupervised genomic region embeddings.
• To address the need for assessing embedding quality and reliability in the absence of metadata.
• To guide the optimization of representation learning models for genomics.

Main Methods:

• Proposed four novel evaluation metrics: cluster tendency score (CTS), reconstruction score (RCS), genome distance scaling score (GDSS), and neighborhood preserving score (NPS).
• CTS and RCS statistically measure clustering capability and information preservation.
• GDSS and NPS leverage genomic proximity to assess biological function representation.

Main Results:

• Demonstrated the utility of the proposed metrics for evaluating unsupervised genomic region embeddings.
• Showcased how statistical and biological properties can be quantified in embeddings.
• Provided evidence that these metrics can guide the learning of more reliable genomic embeddings.

Conclusions:

• The developed metrics offer a robust framework for assessing unsupervised genomic region embeddings.
• These evaluation tools are crucial for reliable downstream analyses in genomics.
• The proposed metrics facilitate the tuning of models for optimal representation learning in genomics.