Different genes in different cells follow different spatial patterns of expression. We can clearly tell by eye. Our gut feeling tells us that some patterns look rather uniform, others spotty, and others pretty circular.
Understanding cell-gene spatial patterns will further our biological knowledge. But how do we quantify these patterns?
We need a platform that is robust enough to take into account all possible cell shapes, sizes, and orientations. A pipeline that can work regardless of the amount of transcripts. We turn thus to Topological Data Analysis (TDA).
(One day of this I will write a better summary of this topic here.)
¡Preprint: In progress!
—
Code Available: Github repository with a series of Python-based Jupyter notebooks detailing the image-processing pipeline we wrote to detect Cuscuta from skewer and background. It also contains code on how time series were created and later analyzed.
—As slides: Presented at SIAM-CSS 2024. University of Missouri–Kansas City, Kansas City, Missouri. October 2024.
As a poster: Presented at the CAFNR Research Symposium. University of Missouri. Columbia, Missouri. October 2024.
——————————