Quantitative Issues in Cancer Research Working Seminar

Phillip Nicol
Doctoral Student, Department of Biostatistics, Harvard University

“Model-based dimension reduction for spatial transcriptomics data”
ABSTRACT: Recently developed technologies can measure gene expression at single cell resolution while simultaneously preserving the spatial location of samples. Standard dimension reduction techniques such as principal component analysis (PCA) can be applied to find a small set of genes that contribute biologically relevant variation. However, standard approaches do not model the count nature of the data which can lead to spurious results. Moreover, the resulting PCA factors may not be spatially coherent in the sense that nearby cells could have very different factor scores. In this talk I will discuss preliminary work on adding spatial penalties to a Poisson-based model for dimension reduction of single-cell gene expression data (scGBM). We will demonstrate the ability of our method to produce spatially coherent factors on both real and simulated data.