"Tumor subclone detection and niche differential expression analysis on spatial transcriptomics data"

Single cells influence, and are shaped by, their local tissue microenvironment. High resolution technologies for in situ profiling of gene expression at the transcriptome scale are rapidly maturing, enabling the detailed interrogation of the distribution of cell types in situ as well as the elucidation of local signaling patterns between cell types. In this talk, I will describe new computational methods for the analysis of spatial transcriptomics data, and illustrate their application to the study of cancer. First, I will focus on the detection of somatic copy number aberrations from spatial transcriptomic and single cell data, and the use of somatic copy numbers in the differentiation of malignant from normal tissue and the characterization of tumor subclonal evolution. Next, I will discuss niche-differential expression (niche-DE) analysis. Niche-DE identifies cell-type specific niche-associated genes, defined as genes whose single cell expression is significantly up- or down-regulated in the context of specific spatial niches. Although niche-DE is conceptually defined on the single-cell level, we show that niche-DE genes can be recovered from lower resolution spatial transcriptomic (ST) data where each observation is a spot containing a mixture of cell types. We apply the methods to the study of the tumor microenvironment on spatial transcriptomic data from multiple cancer types.