{
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  "Package": "glyclean",
  "Title": "Perform Preprocessing on Glycomics and Glycoproteomics Data",
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  "Authors@R": "person(\"Bin\", \"Fu\", , \"23110220018@m.fudan.edu.cn\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-8567-2997\"))",
  "Description": "Provides comprehensive data preprocessing tools for\nglycomics and glycoproteomics experiments. The package offers\nboth automated and manual preprocessing pipelines, including\nnormalization (median, quantile, vsn, total area), missing\nvalue handling (filtering, imputation using various methods),\nbatch effect correction (using ComBat and other methods), data\naggregation (at peptide, glycan, or site level), and quality\ncontrol functions. The automated pipeline intelligently selects\nappropriate methods based on data characteristics and sample\nsize, making it easy to prepare clean, analysis-ready data for\ndownstream statistical analysis within the glycoverse\necosystem. Works seamlessly with 'glyexp::experiment()' objects\nto ensure data consistency and interoperability.",
  "License": "MIT + file LICENSE",
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  "Repository": "https://glycoverse.r-universe.dev",
  "Date/Publication": "2026-05-18 02:52:54 UTC",
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  "Author": "Bin Fu [aut, cre] (ORCID: <https://orcid.org/0000-0001-8567-2997>)",
  "Maintainer": "Bin Fu <23110220018@m.fudan.edu.cn>",
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    "normalize_median_abs",
    "normalize_median_quotient",
    "normalize_quantile",
    "normalize_rlr",
    "normalize_rlrma",
    "normalize_rlrmacyc",
    "normalize_total_area",
    "normalize_vsn",
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    "plot_cv_dent",
    "plot_int_boxplot",
    "plot_missing_bar",
    "plot_missing_heatmap",
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    "plot_rep_scatter",
    "plot_rle",
    "plot_tic_bar",
    "remove_constant",
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    "remove_low_var",
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      "title": "Add site-specific sequence information.",
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        "add_site_seq.glyexp_experiment"
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      "title": "Adjust Protein Expression",
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        "adjust_protein.default",
        "adjust_protein.glyexp_experiment"
      ]
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        "glyclean_aggregate.default",
        "glyclean_aggregate.glyexp_experiment"
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      "title": "Automatic Data Preprocessing",
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      "title": "Automatic Batch Correction",
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        "auto_correct_batch_effect"
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      "title": "Correct Batch Effect",
      "topics": [
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        "correct_batch_effect.default",
        "correct_batch_effect.glyexp_experiment",
        "correct_batch_effect.matrix"
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      "title": "Detect batch effect",
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        "detect_batch_effect.default",
        "detect_batch_effect.glyexp_experiment",
        "detect_batch_effect.matrix"
      ]
    },
    {
      "page": "impute_bpca",
      "title": "BPCA Imputation",
      "topics": [
        "impute_bpca",
        "impute_bpca.default",
        "impute_bpca.glyexp_experiment",
        "impute_bpca.matrix"
      ]
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      "title": "Feature-wise KNN Imputation",
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        "impute_fw_knn",
        "impute_fw_knn.default",
        "impute_fw_knn.glyexp_experiment",
        "impute_fw_knn.matrix"
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      "page": "impute_half_sample_min",
      "title": "Half Sample Minimum Imputation",
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        "impute_half_sample_min",
        "impute_half_sample_min.default",
        "impute_half_sample_min.glyexp_experiment",
        "impute_half_sample_min.matrix"
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      "title": "Minimum Probability Imputation",
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        "impute_min_prob.default",
        "impute_min_prob.glyexp_experiment",
        "impute_min_prob.matrix"
      ]
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      "title": "MissForest Imputation",
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        "impute_miss_forest",
        "impute_miss_forest.default",
        "impute_miss_forest.glyexp_experiment",
        "impute_miss_forest.matrix"
      ]
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    {
      "page": "impute_ppca",
      "title": "PPCA Imputation",
      "topics": [
        "impute_ppca",
        "impute_ppca.default",
        "impute_ppca.glyexp_experiment",
        "impute_ppca.matrix"
      ]
    },
    {
      "page": "impute_sample_min",
      "title": "Sample Minimum Imputation",
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        "impute_sample_min",
        "impute_sample_min.default",
        "impute_sample_min.glyexp_experiment",
        "impute_sample_min.matrix"
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    },
    {
      "page": "impute_svd",
      "title": "SVD Imputation",
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        "impute_svd",
        "impute_svd.default",
        "impute_svd.glyexp_experiment",
        "impute_svd.matrix"
      ]
    },
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      "page": "impute_sw_knn",
      "title": "Sample-wise KNN Imputation",
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        "impute_sw_knn.default",
        "impute_sw_knn.glyexp_experiment",
        "impute_sw_knn.matrix"
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      "title": "Zero Imputation",
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        "impute_zero.default",
        "impute_zero.glyexp_experiment",
        "impute_zero.matrix"
      ]
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      "title": "LoessCyc Normalization",
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        "normalize_loesscyc.default",
        "normalize_loesscyc.glyexp_experiment",
        "normalize_loesscyc.matrix"
      ]
    },
    {
      "page": "normalize_loessf",
      "title": "LoessF Normalization",
      "topics": [
        "normalize_loessf",
        "normalize_loessf.default",
        "normalize_loessf.glyexp_experiment",
        "normalize_loessf.matrix"
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    {
      "page": "normalize_median",
      "title": "Median Normalization",
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        "normalize_median",
        "normalize_median.default",
        "normalize_median.glyexp_experiment",
        "normalize_median.matrix"
      ]
    },
    {
      "page": "normalize_median_abs",
      "title": "Absolute Median Normalization",
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        "normalize_median_abs.default",
        "normalize_median_abs.glyexp_experiment",
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        "normalize_median_quotient.default",
        "normalize_median_quotient.glyexp_experiment",
        "normalize_median_quotient.matrix"
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    {
      "page": "normalize_quantile",
      "title": "Quantile Normalization",
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        "normalize_quantile.default",
        "normalize_quantile.glyexp_experiment",
        "normalize_quantile.matrix"
      ]
    },
    {
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      "title": "Robust Linear Regression Normalization",
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      "source": "glyclean.Rmd",
      "filename": "glyclean.html",
      "title": "Get Started with glyclean",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Welcome to the Wild World of Data Preprocessing! 🧬",
        "Meet Our Star Player: Real Glycoproteomics Data 🌟",
        "The Magic Wand: One Function to Rule Them All ✨",
        "Still Like Magic, but With More Control?",
        "Taking the Scenic Route: Step-by-Step Preprocessing 🚶‍♀️",
        "Step 1: Normalization – Getting Everyone on the Same Page 📏",
        "Step 2: Variable Filtering – Saying Goodbye to the Unreliable 🧹",
        "Step 3: Imputation – Filling in the Blanks Intelligently 🔮",
        "Step 4: Aggregation – From Peptides to Glycoforms 🔄",
        "Step 5: Batch Effect Correction – Harmonizing Your Orchestra 🎼",
        "Step 6: Protein Expression Adjustment – Separating Signal from Noise 🎯",
        "🎉 Mission Accomplished!",
        "Advanced Usage: Beyond glyexp::experiment() Objects 🔧",
        "Working with Plain Matrices",
        "Flexible Grouping with Custom Vectors",
        "Quality Control (QC) 📊",
        "What's Next?"
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      "created": "2025-06-17 15:20:39",
      "modified": "2026-05-05 02:41:26",
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