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        <datestamp>2026-09-19T04:46:50Z</datestamp>
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          <dc:title>CODEX Data Human Hepatocellular carcinoma Molecular Subtypes</dc:title>
          <dc:creator>Renumathy Dhanasekaran (19416724)</dc:creator>
          <dc:subject>Proteomics and metabolomics</dc:subject>
          <dc:subject>HCC, hepatocellular carcinoma</dc:subject>
          <dc:subject>Spatial</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;We utilized CO-Detection by indEXing (CODEX) technology to image a total of hepatocellular carcinoma (HCC) cells, analyzing them with a comprehensive 56-marker panel. In this procedure, DNA-barcoded antibodies targeted tissue antigens and were visualized through iterative hybridization with complementary fluorescent DNA oligonucleotides. Our algorithmic pipeline processed the raw imaging data to segment and identify single cells, accurately localize them within tissues, and quantify their marker expressions. Further, unsupervised clustering, enhanced by manual curation, utilized marker expression, and tissue localization to identify specific cell types. The imaging data were segmented into single cells based on nuclear staining, using DAPI as a reference. Spatial coordinates (X/Y) were assigned to each cell, allowing us to pinpoint each cell's precise location on the slide. The methodologies employed are detailed further in the accompanying manuscript. The "Celllevel_rawdata_8_12_24.csv" table has been compiled to include annotations of cell types, expression profiles, coordinates of all segmented objects, and the cohort to which each cell belongs.&lt;br&gt;&lt;/p&gt;</dc:description>
          <dc:date>2026-09-19T04:46:50Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.33941104.v1</dc:identifier>
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