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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Sep 15, 2025

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

INLAomics for Scalable and Interpretable Spatial Multiomic Data Integration.

Lukas Arnroth¹, Sanja Vickovic^1,2,3

¹Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden.

Biorxiv : the Preprint Server for Biology

|July 14, 2025

Summary

This summary is machine-generated.

INLAomics integrates spatial transcriptomics and proteomics for biological insights. This Bayesian framework improves gene-protein network analysis and spatial protein prediction, offering a scalable, interpretable solution.

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Published on: August 8, 2019

Area of Science:

Computational biology
Systems biology
Bioinformatics

Background:

Spatial multi-omics integration is crucial for understanding tissue architecture and biological regulation.
Current methods often ignore spatial context, limiting interpretability and scalability.
Existing approaches struggle with dimensionality reduction and autoencoders for multi-modal data.

Purpose of the Study:

To develop a novel multivariate hierarchical Bayesian framework, INLAomics, for integrating spatial transcriptomics and proteomics data.
To address limitations of current methods by incorporating histological features and spatial factors.
To enable accurate modeling of protein abundance within intact tissue architecture.

Main Methods:

Developed INLAomics, a multivariate hierarchical Bayesian framework.
Leveraged histological features and latent spatial factors from spatial transcriptomics.
Modeled protein abundance in tissue sections for integrative analysis.

Main Results:

INLAomics identified novel spatial gene co-expression programs and gene-protein associations.
The framework significantly improved spatial protein expression prediction accuracy.
Demonstrated computational efficiency and biological interpretability across diverse datasets.

Conclusions:

INLAomics provides a scalable and interpretable solution for spatial multi-omics integration.
The framework enhances understanding of biological regulation within tissue context.
Offers a powerful tool for discovering gene-protein relationships and predicting protein expression.