Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.
Emily LaubscherXuefei Julie WangNitzan RazinTom DoughertyRosalind J XuLincoln OmbeletsEdward PaoWilliam GrafJeffrey R MoffittYisong YueDavid Van ValenPublished in: bioRxiv : the preprint server for biology (2023)
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 MERFSIH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library ( https://github.com/vanvalenlab/deepcell-spots ) and https://www.deepcell.org .
Keyphrases
- single cell
- deep learning
- rna seq
- gene expression
- single molecule
- artificial intelligence
- convolutional neural network
- high throughput
- machine learning
- dna methylation
- genome wide
- loop mediated isothermal amplification
- big data
- healthcare
- high resolution
- transcription factor
- quantum dots
- real time pcr
- data analysis
- cell therapy
- social media
- copy number
- sensitive detection