Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.
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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
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Journal | Cell systems
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Volume | 15
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Issue | 5
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Pages | 475-482.e6
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Date Published | 05/2024
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ISSN | 2405-4720
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DOI | 10.1016/j.cels.2024.04.006
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PubMed ID | 38754367
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