Optimization-based decoding of Imaging Spatial Transcriptomics data.

ABSTRACT:

Motivation

Imaging Spatial Transcriptomics 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, an algorithm for decoding lower magnification Imaging Spatial Transcriptomics data than that used in standard experimental workflows. Joint Sparse method for Imaging Transcriptomics 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 M

SUBMITTER: Bryan JP 

PROVIDER: S-EPMC10287917 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature