Abstract
Motivation Imaging Spatial Transcriptomics (iST) techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods.
Results We describe the Joint Sparse method for Imaging Transcriptomics (JSIT), an algorithm for decoding lower magnification IT data than that used in standard experimental workflows. JSIT incorporates codebook knowledge and sparsity assumptions into an optimization problem which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization (MERFISH) on tissue from mouse motor cortex, we demonstrate that JSIT enables improved throughput and recovery performance over standard decoding methods.
Contact yonina.eldar{at}weizmann.ac.il, sfarhi{at}broadinstitute.org, bcleary{at}bu.edu
Availability and Implementation Software implementation of JSIT, together with example files, are available at https://github.com/jpbryan13/JSIT.
Supplementary Information Supplementary data are available at Bioinformatics online.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵* These authors jointly supervised this work