Exploratory Data Analysis of imaging-based spatial transcriptomics: an application to a lung cancer sample

Imaging-based spatial transcriptomics enables visualization of gene expression in tissue sections while preserving spatial context. It can detect mRNA transcripts as individually resolved spots in multiplexed images, thereby revealing patterns of single-cell gene expression. In this article, we analyze the lung cancer cosMX dataset, which is stored as R objects in a binary format. In a spatial transcriptomics workflow for lung cancer, six genes (CCL18, CD163, CD74, HLA.DPA1, HLADRA, and HLA.DRB1) showed higher expression than other genes.