Exploring brain transcriptomic patterns: a topological analysis using spatial expression networks
Zhana Kuncheva, Michelle L. Krishnan, Giovanni Montana

TL;DR
This study introduces a novel topological analysis of brain transcriptomic data using Spatial Expression Networks (SENs) to identify gene clusters with distinct biological functions across the human brain.
Contribution
The paper proposes a new network-based representation of gene expression patterns, revealing topologically distinct gene clusters linked to specific biological processes.
Findings
Identified three major gene clusters with distinct topological properties.
One cluster is enriched for genes involved in the nervous system and brain disorders.
Other clusters are associated with immunity, transcription, and translation.
Abstract
Characterizing the transcriptome architecture of the human brain is fundamental in gaining an understanding of brain function and disease. A number of recent studies have investigated patterns of brain gene expression obtained from an extensive anatomical coverage across the entire human brain using experimental data generated by the Allen Human Brain Atlas (AHBA) project. In this paper, we propose a new representation of a gene's transcription activity that explicitly captures the pattern of spatial co-expression across different anatomical brain regions. For each gene, we define a Spatial Expression Network (SEN), a network quantifying co-expression patterns amongst several anatomical locations. Network similarity measures are then employed to quantify the topological resemblance between pairs of SENs and identify naturally occurring clusters. Using network-theoretical measures, three…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
