Exploring the latent space of transcriptomic data with topic modeling.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• High-dimensional transcriptomic datasets are rapidly expanding, necessitating advanced computational tools.
• Clustering and dimensionality reduction are common for analyzing gene expression data.
• Topic modeling, successful in natural language processing, offers a novel approach for biological data.

Purpose of the Study:

• To compare the efficacy of various topic modeling techniques for analyzing transcriptomic data.
• To assess the ability of topic models to uncover latent structures in gene expression data.
• To evaluate topic modeling for reconstructing human tissue architecture and differentiating healthy from cancerous tissues.

Main Methods:

• Statistical analogies between text and transcriptomic data were leveraged.
• Multiple topic modeling algorithms were applied to gene expression datasets.
• Latent space properties, including the impact of statistical priors, were examined.
• A neural network classifier was trained on the topic model's latent space.

Main Results:

• Topic modeling accurately identified and reconstructed human tissue structures from transcriptomic data.
• Methods effectively distinguished between healthy and cancerous tissues.
• The latent space generated by topic models proved to be a valuable low-dimensional embedding.
• Statistical priors significantly influenced the interpretability and results of the topic models.

Conclusions:

• Topic modeling provides a powerful framework for analyzing high-dimensional transcriptomic data.
• This approach offers a viable alternative to traditional methods for biological data exploration.
• The latent topic space enables accurate classification of transcriptomic profiles, aiding in disease detection.