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

Emily Laubscher, Xuefei (Julie) Wang, Nitzan Razin, Tom Dougherty, Rosalind J. Xu, Lincoln Ombelets, Edward Pao, William Graf, Jeffrey R. Moffitt, Yisong Yue, David Van Valen
bioRxiv (2024)


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 MERFISH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library ( and