Michael Totty
Currently an F32 NRSA Postdoctoral Fellow in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health working with Dr. Stephanie Hicks and Dr. Keri Martinowich. Interested in developing novel therapeutics for psychiatric disorders via reverse translation by combining next-generation sequencing across species with sophisticated behavioral design in preclinical models. PhD training from the Texas A&M Institute for Neuroscience.
Sessions
Quality control (QC) is a crucial step to ensure the reliability and accuracy of the data obtained from sequencing experiments. Recent technological advances now allow for whole-transcriptome profiling with spatial resolution. Until now, standard procedures for the QC for spatially-resolved transcriptomics (SRT) data adopt methods developed for single-cell RNA sequencing (scRNA-seq). We show here that QC methods developed for scRNA-seq are inappropriate for SRT. Additionally, SRT are subject to large technical artifacts, such hangnails or tissue folds, that arise due to tissue processing errors unique to SRT. To address this, we have developed SpotSweeper; an R/Bioconductor package for the detection and removal of both local outliers and technical artifacts in SRT using spatially-aware methods. By comparing individual spots to their local neighborhood, we show that SpotSweeper avoids the bias present in global (i.e, whole tissue) comparisons. Similarly, we demonstrate that technical artifacts can be classified with high accuracy using the local variance in mitochondrial ratio. Collectively, SpotSweeper is the first computational methods developed specifically for SRT.