Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Cell systems
Authors
Keywords
Abstract

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.

Year of Publication
2024
Journal
Cell systems
Volume
15
Issue
5
Pages
475-482.e6
Date Published
05/2024
ISSN
2405-4720
DOI
10.1016/j.cels.2024.04.006
PubMed ID
38754367
Links