Lukas M. Weber, Ph.D., a Postdoctoral Fellow in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health and candidate for a faculty position in the Division of Biostatistics, will present:
“Unsupervised Statistical Methods and Data-Driven Analysis Workflows for Spatially-Resolved Transcriptomics”
Abstract: High-throughput genomic platforms are widely applied for characterizing variability within biological samples, for example to identify cell populations and states, differentially expressed genes between healthy and diseased conditions, or biologically informative genes. Spatially-resolved transcriptomics enables the measurement of transcriptome-wide gene expression at single-cell or near-single-cell resolution along with spatial coordinates, thus enabling an understanding of the spatial organization of cells and gene expression patterns within complex tissues. Unsupervised statistical methods facilitate exploratory, data-driven analyses of high-dimensional genomic data through discovery-based analysis workflows, including steps such as preprocessing, feature selection, and clustering. In this talk, I will present a new, scalable method (nnSVG) for identifying spatially variable genes based on nearest-neighbor Gaussian processes, which outperforms existing methods and scales linearly with the number of spatial measurement locations. Secondly, I will present an unsupervised analysis of single-nucleus and spatially-resolved data from the locus coeruleus in postmortem human brain samples, which is the first transcriptome-wide characterization of the gene expression landscape of this region in human tissue. Additionally, I will discuss work to develop reproducible workflows, benchmarking evaluations, and open-source software in the context of single-cell RNA sequencing and high-dimensional cytometry platforms. All methods presented in this talk are freely accessible as open-source software through the R/Bioconductor project. The development of statistically rigorous, accessible, and scalable methods for discovery-based analyses in emerging high-throughput platforms will empower the biological research community and contribute to improved biological understanding and novel insights relating to properties of cell populations and gene expression in healthy and diseased tissues.
All are Welcome.