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  "Title": "Rasterization Preprocessing Framework for Scalable Spatial Omics\nData Analysis",
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  "Authors@R": "c(person(\"Gohta\", \"Aihara\", email = \"gohta.aihara@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-2492-9610\")),\nperson(\"Mayling\", \"Chen\", email = \"mchen161@jhu.edu\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0009-0009-0961-6665\")),\nperson(\"Lyla\", \"Atta\", email = \"lylaatta@jhmi.edu\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-6113-0082\")),\nperson(\"Jean\", \"Fan\", email = \"jeanfan@jhu.edu\",\nrole = c(\"aut\", \"rev\"),\ncomment = c(ORCID = \"0000-0002-0212-5451\")))",
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  "Description": "SEraster is a rasterization preprocessing framework that\naggregates cellular information into spatial pixels to reduce\nresource requirements for spatial omics data analysis. SEraster\nreduces the number of spatial points in spatial omics datasets\nfor downstream analysis through a process of rasterization\nwhere single cells’ gene expression or cell-type labels are\naggregated into equally sized pixels based on a user-defined\nresolution. SEraster is built on an R/Bioconductor S4 class\ncalled SpatialExperiment. SEraster can be incorporated with\nother packages to conduct downstream analyses for spatial omics\ndatasets, such as detecting spatially variable genes.",
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