{"repo":"jiazhao97/INSPIRE","free":true,"listed":false,"github":"https://github.com/jiazhao97/INSPIRE","clone":"git clone https://github.com/jiazhao97/INSPIRE.git","description":"INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources","language":"Python","stars":21,"topics":["data-integration","data-interpretation","deep-learning","spatial-transcriptomics"],"license":null,"category":"machine-learning","readme_excerpt":"INSPIRE Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources An effective and efficient method for joint analyses of multiple spatial transcriptomics datasets. Check out our manuscript in Nature Genetics: + Nature Genetics website + Preprint in bioRxiv We develop INSPIRE, a deep learning-based method for integrating and interpreting multiple spatial transcriptomics (ST) datasets from diverse sources. It integrates information across sections in a shared latent space, where meaningful biological variations from the input sections are preserved, while complex unwanted variations are eliminated. Utilizing this shared latent space, INSPIRE achieves an integrated NMF on gene expressions across sections, decomposing biological signals in different sections into consistent and interpretable spatial factors with associated gene programs. These inferred spatial factors often correspond to distinct cell populations and biological processes within the analyzed tissues. INSPIRE takes gene expression count matrices and spatial coordinates from multiple ST sections as input, and generates three key outputs: latent representations of cells or spatial spots, non-negative spatial factors for cells or spatial spots, and non-negative gene loadings shared among datasets. By integrating multiple ST datasets with INSPIRE, users can: Identify spatial trajectories and major spatial regions consistently across datasets using latent ","default_branch":null,"files":null,"tree":[],"storefront":"/r/jiazhao97","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/jiazhao97/INSPIRE/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}