Integrating Spatial Transcriptomics Data for Cross-Species Molecular Region Comparison

Comparative analysis of brain patterns across species can advance understanding of different biological processes and functions. Spatially resolved transcriptomics (SRT) technologies present the ability to measure gene expression of single cells within tissues, enabling the detection of unique spatial molecular patterns in the brain. Several computational methods that rely on cellular neighborhood information have been developed for characterizing molecular tissue regions in SRT data. Here, we show that spatial integration (SPIN) improves the performance of existing methods and enables the clustering of molecular tissue regions. Then, we test SPIN and signal-processing approaches on SRT data from mouse and macaque brains. We integrate the brain atlases of these two species to identify shared and distinct spatial molecular patterns. This work offers new insights into spatial molecular features between mouse and macaque brains and proposes a framework for integrating SRT datasets on a large scale.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Li, Bridget

Advisor dc:contributor.advisor

• Wang, Xiao

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156756

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156756