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RelatedMaterial
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update: {"datacite_list"=>["IsSupplementedBy ", "IsSupplementedBy"]}
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2024-04-18T18:23:38Z
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RelatedMaterial
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create: {"material_type"=>"Code", "availability"=>nil, "link"=>"https://github.com/richardxie1119/MEISTER", "uri"=>"https://github.com/richardxie1119/MEISTER", "uri_type"=>"URL", "citation"=>"https://github.com/richardxie1119/MEISTER", "dataset_id"=>2617, "selected_type"=>"Code", "datacite_list"=>"IsSupplementedBy ", "note"=>nil, "feature"=>nil}
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2024-02-26T16:41:57Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1038/s41592-024-02171-3", "uri"=>"10.1038/s41592-024-02171-3", "uri_type"=>"DOI", "citation"=>"Xie, Y.R., Castro, D.C., Rubakhin, S.S. et al. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02171-3", "dataset_id"=>2617, "selected_type"=>"Article", "datacite_list"=>"IsSupplementTo", "note"=>"", "feature"=>false}
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2024-02-26T16:41:57Z
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Dataset
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update: {"version_comment"=>[nil, ""], "subject"=>[nil, "Physical Sciences"]}
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2024-02-26T16:41:57Z
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Dataset
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update: {"description"=>["The minimal datasets to run the computational pipeline MEISTER introduced in the manuscript titled \"Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry\". The key steps of our computational pipeline include (1) tissue mass spectrometry imaging (MSI) reconstruction; (2) multimodal image registration and 3D reconstruction; (3) regional analysis; and (4) single-cell and tissue data integration. Detailed protocols to reproduce our results in the manuscript are provided with an example data set shared for learning the protocols. Our computational processing codes are implemented mostly in Python as well as MATLAB (for image registration).", "Important Note: the raw transient files need to be downloaded through this separate link: https://uofi.box.com/s/oagdxhea1wi8tvfij4robj0z0w8wq7j4. Once downloaded, place the file within the within the .d folder in the unzipped 20210930_ShortTransient_S3_5 folder to perform reconstruction step.\r\n\r\nThe minimal datasets to run the computational pipeline MEISTER introduced in the manuscript titled \"Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry\". The key steps of our computational pipeline include (1) tissue mass spectrometry imaging (MSI) reconstruction; (2) multimodal image registration and 3D reconstruction; (3) regional analysis; and (4) single-cell and tissue data integration. Detailed protocols to reproduce our results in the manuscript are provided with an example data set shared for learning the protocols. Our computational processing codes are implemented mostly in Python as well as MATLAB (for image registration).\r\n\r\n"]}
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2023-12-21T19:42:24Z
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