{
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  "Type": "Package",
  "Title": "Marker Enrichment Modeling (MEM)",
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  "Authors@R": "c(person(\"Sierra\",\"Lima\", role = c(\"aut\"),\nemail = \"sierrambarone@gmail.com\",\ncomment = c(ORCID = \"0000-0001-5944-750X\")),\nperson(\"Kirsten\",\"Diggins\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-1622-0158\")),\nperson(\"Jonathan\",\"Irish\", role = c(\"aut\",\"cre\"),\nemail = \"jonathan.irish@vanderbilt.edu\",\ncomment = c(ORCID = \"0000-0001-9428-8866\")))",
  "Description": "MEM, Marker Enrichment Modeling, automatically generates\nand displays quantitative labels for cell populations that have\nbeen identified from single-cell data. The input for MEM is a\ndataset that has pre-clustered or pre-gated populations with\ncells in rows and features in columns. Labels convey a list of\nmeasured features and the features' levels of relative\nenrichment on each population. MEM can be applied to a wide\nvariety of data types and can compare between MEM labels from\nflow cytometry, mass cytometry, single cell RNA-seq, and\nspectral flow cytometry using RMSD.",
  "License": "GPL-3",
  "Collate": "'MEM_function.R''create_labels_txt.R''IQR_thresh.R''build_heatmaps.R''add_cluster_ID.R''add_fileID_to_clusterID.R''choose_reference_pop.R''choose_markers.R''create_labels.R''format_data.R''get_files.R''rename_markers.R''MEM_RMSD.R''zero_reference.R'",
  "biocViews": "Proteomics, SystemsBiology, Classification, FlowCytometry,\nDataRepresentation, DataImport, CellBiology, SingleCell,\nClustering",
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  "Repository": "https://bioc-release.r-universe.dev",
  "Date/Publication": "2026-04-28 12:58:08 UTC",
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    "User": "root"
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  "Author": "Sierra Lima [aut] (ORCID: <https://orcid.org/0000-0001-5944-750X>),\nKirsten Diggins [aut] (ORCID: <https://orcid.org/0000-0003-1622-0158>),\nJonathan Irish [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9428-8866>)",
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    "classification",
    "flowcytometry",
    "datarepresentation",
    "dataimport",
    "cellbiology",
    "singlecell",
    "clustering"
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    "name": "Bioconductor",
    "description": "Software for the analysis and comprehension of high-throughput genomic data"
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    "extra/citation.json",
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  "_realowner": "bioc",
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  "_exports": [
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      "name": "MEM_matrix",
      "title": "MEM matrix",
      "object": "MEM_matrix",
      "file": "MEM_matrix.RData",
      "class": [
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        "array"
      ],
      "fields": [
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        "CD117",
        "CD11b",
        "CD4",
        "CD8",
        "CD20",
        "CD34",
        "CD61",
        "CD123",
        "CD45RA",
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        "CD14",
        "CD69",
        "CD15",
        "CD16",
        "CD44",
        "CD38",
        "CD25",
        "CD3",
        "IgM",
        "HLADR",
        "CD56"
      ],
      "rows": 7,
      "table": true,
      "tojson": true
    },
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      "name": "MEM_values",
      "title": "MEM values",
      "object": "MEM_values",
      "file": "MEM_values.RData",
      "class": [
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      ],
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      "table": true,
      "tojson": true
    },
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      "title": "Normal Human Peripheral Blood Mononuclear Cells (PBMCs)",
      "object": "PBMC",
      "file": "PBMC.RData",
      "class": [
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        "array"
      ],
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        "cluster"
      ],
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      "page": "build_heatmaps",
      "title": "Build heatmaps",
      "topics": [
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      ]
    },
    {
      "page": "MEM",
      "title": "Marker Enrichment Modeling",
      "topics": [
        "MEM"
      ]
    },
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      "title": "MEM matrix",
      "topics": [
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      "title": "MEM RMSD similarity between populations",
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      ]
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      "title": "Intro_to_Marker_Enrichment_Modeling_Analysis",
      "author": "Kirsten Diggins, Sierra Lima, Jonathan Irish",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Intro to Marker Enrichment Modeling",
        "Installing MEM",
        "Example Data: Normal Human Peripheral Blood Cells (PBMC)",
        "Input data: File format and structure",
        "Multiple Files",
        "Reading files in to R",
        "Use of file.is.clust and add.fileID",
        "Data Format",
        "Arcsinh Transformation",
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        "Arguments of build_heatmaps()",
        "Generating RMSD (similarity) scores"
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