Integrating the biological knowledge from protein databases into spatial RNA sequencing analyses
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] pracownik
2024
referat
angielski
- data integration
- genomic signatures
- spatial transcriptomics
- machine learning
EN Spatial transcriptomics has emerged in recent years as an advanced technique integrating modern microscopy and single-cell RNA sequencing (RNA-seq). The computational aspect of spatial transcrip- tomics analysis is actively evolving, with software packages like Squidpy at the forefront. A critical challenge in this field is achieving reliable image segmentation based on RNA expression levels within cells, neces- sitating robust sets of genomic signatures. In this paper, we present our approach to addressing this challenge through data integration and by leveraging proteome signatures from The Human Protein Atlas as a reference standard, applicable to tran- scriptomic profiles. Our heuristic-based segmentation method provides biological validation for unsupervised techniques and serves as a prelim- inary proof of concept. dditionally, it establishes a benchmark for the future application of artificial intelligence with the use of transcriptome foundation models.