Sparse Recovery Of Imaging Transcriptomics Data

Abstract

Imaging transcriptomics (IT) techniques enable characterization of gene expression in cells in their native context by imaging barcoded mRNA probes with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods. We propose an algorithm for decoding lower magnification IT data than that used in standard experimental workflows. Our approach, Joint Sparse method for Imaging Transcriptomics (JSIT), incorporates codebook knowledge and sparsity assumptions into an optimization problem. Using simulated low-magnification data, we demonstrate that JSIT enables improved throughput and recovery performance over standard decoding methods.