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  "Title": "Enhanced 'PAM50' Subtyping of Breast Cancer",
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  "Description": "Accurate classification of breast cancer tumors based on\ngene expression data is not a trivial task, and it lacks\nstandard practices.The 'PAM50' classifier, which uses 50 gene\ncentroid correlation distances to classify tumors, faces\nchallenges with balancing estrogen receptor (ER) status and\ngene centering. The 'PCAPAM50' package leverages principal\ncomponent analysis and iterative 'PAM50' calls to create a gene\nexpression-based ER-balanced subset for gene centering,\navoiding the use of protein expression-based ER data resulting\ninto an enhanced Breast Cancer subtyping.",
  "License": "GPL (>= 3)",
  "Copyright": "2024 Windber Research Institute, Windber, PA - 15963. All\nRights Reserved.",
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    {
      "page": "makeCalls.ihc",
      "title": "Make a Conventional PAM50 Intrinsic Subtype Calls",
      "topics": [
        "makeCalls.ihc"
      ]
    },
    {
      "page": "makeCalls.PC1ihc",
      "title": "Make intermediate intrinsic subtype calls",
      "topics": [
        "makeCalls.PC1ihc"
      ]
    },
    {
      "page": "makeCalls.v1PAM",
      "title": "Make PCAPAM50 calls",
      "topics": [
        "makeCalls.v1PAM"
      ]
    },
    {
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      "title": "Modeling after plotPCA of DESeq",
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      ]
    }
  ],
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      "source": "PCAPAM50_vignette.Rmd",
      "filename": "PCAPAM50_vignette.html",
      "title": "PCAPAM50: An R Package for Principal Component Analysis-Based Iterative PAM50 Subtyping",
      "author": "Praveen-Kumar Raj-Kumar Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA P.RajKumar@wriwindber.org",
      "engine": "knitr::rmarkdown",
      "headings": [
        "",
        "Contents",
        "<a id=\"Section.1\" style=\"color:#159957;\">1. Introduction</a>",
        "<a id=\"Section.2\" style=\"color:#159957;\">2. Installation</a>",
        "Prerequisites",
        "Installing Dependencies",
        "Installing PCA-PAM50",
        "Loading PCA-PAM50 and Dependencies",
        "<a id=\"Section.3\" style=\"color:#159957;\">3. Deriving PCAPAM50 Calls Using Provided Test Data</a>",
        "<a id=\"Section.3.1\" style=\"color:#159957;\">3.1. Pipeline Overview</a>",
        "<a id=\"Section.3.1.1\" style=\"color:#159957;\">3.1.1. makeCalls.PC1ihc - Intermediate Intrinsic Subtype Calls</a>",
        "1) Load the Test data",
        "2) Prepare the Data",
        "3) Create the Clinical Subtype Data Frame",
        "4) Call the Function",
        "<a id=\"Section.3.1.2\" style=\"color:#159957;\">3.1.2. makeCalls.v1PAM - PCAPAM50 Calls</a>",
        "Call the Function",
        "<a id=\"Section.3.2\" style=\"color:#159957;\">3.2. The Conventional PAM50 Approach</a>",
        "<a id=\"Section.3.2.1\" style=\"color:#159957;\">3.2.1. makeCalls.ihc - Conventional PAM50 Intrinsic Subtype Calls</a>",
        "<a id=\"Section.3.3\" style=\"color:#159957;\">3.3. - Plotting Data</a>",
        "<a id=\"Section.3.3.1\" style=\"color:#159957;\">3.3.1. - my.plotPCA -</a>",
        "<a id=\"Section.4\" style=\"color:#159957;\">4. Comparing Results - PCAPAM50 vs PAM50</a>",
        "<a id=\"Section.4.1\" style=\"color:#159957;\">4.1. Test Data - PCAPAM50 vs PAM50</a>",
        "<a id=\"Section.4.2\" style=\"color:#159957;\">4.2. TCGA Data - PCAPAM50 vs PAM50</a>",
        "1) Load TCGA data",
        "2) Prepare the data",
        "2) - PCAPAM50 vs PAM50",
        "<a id=\"Section.5\" style=\"color:#159957;\">5. Citations</a>"
      ],
      "created": "2024-07-09 02:44:35",
      "modified": "2025-01-09 17:10:05",
      "commits": 3
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