07-24, 15:15–15:23 (US/Eastern), Tomatis Auditorium
In the realm of cancer research understanding the tumor microenvironment (TME) plays a role, in predicting tumor behavior and patient outcomes. Due to the nature of TME advanced imaging methods like Multiplexed Ion Beam Imaging by Time of Flight (MIBI TOF) are necessary to gather spatial information. Our research presents an approach using spatial Latent Dirichlet Allocation (LDA) to analyze this data comparing it with traditional LDA to showcase its effectiveness in capturing spatial variations within TME. Our methodology involves a two step process; initially using LDA to identify patterns in cell phenotype distributions and then utilizing a modified linear model to address spatial differences among cells. This efficient method showcases the capabilities of spatial LDA in understanding TME complexity.
