{
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  "Package": "iCellR",
  "Type": "Package",
  "Title": "Analyzing High-Throughput Single Cell Sequencing Data",
  "Version": "1.7.0",
  "Authors@R": "c(\nperson(given = 'Alireza', family = 'Khodadadi-Jamayran',role = c('aut','cre'), email = 'alireza.khodadadi.j@gmail.com', comment = c(ORCID = '0000-0003-2495-7504')),\nperson(given = 'Joseph', family = 'Pucella',role = c('aut','ctb'), comment = c(ORCID = '0000-0003-0875-8046')),\nperson(given = 'Hua', family = 'Zhou',role = c('aut','ctb'), comment = c(ORCID = '0000-0003-1822-1306')),\nperson(given = 'Nicole', family = 'Doudican',role = c('aut','ctb'), comment = c(ORCID = '0000-0003-3827-9644')),\nperson(given = 'John', family = 'Carucci',role = c('aut','ctb'), comment = c(ORCID = '0000-0001-6817-9439')),\nperson(given = 'Adriana', family = 'Heguy',role = c('aut','ctb')),\nperson(given = 'Boris', family = 'Reizis',role = c('aut','ctb'), comment = c(ORCID = '0000-0003-1140-7853')),\nperson(given = \"Aristotelis\", family = \"Tsirigos\",role = c('aut','ctb'), comment = c(ORCID = '0000-0002-7512-8477'))\n)",
  "Maintainer": "Alireza Khodadadi-Jamayran <alireza.khodadadi.j@gmail.com>",
  "Description": "A toolkit that allows scientists to work with data from\nsingle cell sequencing technologies such as scRNA-seq,\nscVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics\n(ST). Single (i) Cell R package ('iCellR') provides\nunprecedented flexibility at every step of the analysis\npipeline, including normalization, clustering, dimensionality\nreduction, imputation, visualization, and so on. Users can\ndesign both unsupervised and supervised models to best suit\ntheir research. In addition, the toolkit provides 2D and 3D\ninteractive visualizations, differential expression analysis,\nfilters based on cells, genes and clusters, data merging,\nnormalizing for dropouts, data imputation methods, correcting\nfor batch differences, pathway analysis, tools to find marker\ngenes for clusters and conditions, predict cell types and\npseudotime analysis. See Khodadadi-Jamayran, et al (2020)\n<doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al\n(2020) <doi:10.1101/2020.03.31.019109> for more details.",
  "License": "GPL-2",
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  "BugReports": "https://github.com/rezakj/iCellR/issues",
  "URL": "https://github.com/rezakj/iCellR",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-08 08:26:07 UTC",
    "User": "root"
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  "Author": "Alireza Khodadadi-Jamayran [aut, cre]\n(<https://orcid.org/0000-0003-2495-7504>), Joseph Pucella [aut,\nctb] (<https://orcid.org/0000-0003-0875-8046>), Hua Zhou [aut,\nctb] (<https://orcid.org/0000-0003-1822-1306>), Nicole Doudican\n[aut, ctb] (<https://orcid.org/0000-0003-3827-9644>), John\nCarucci [aut, ctb] (<https://orcid.org/0000-0001-6817-9439>),\nAdriana Heguy [aut, ctb], Boris Reizis [aut, ctb]\n(<https://orcid.org/0000-0003-1140-7853>), Aristotelis Tsirigos\n[aut, ctb] (<https://orcid.org/0000-0002-7512-8477>)",
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  "Repository": "https://rezakj.r-universe.dev",
  "Date/Publication": "2025-11-10 15:27:23 UTC",
  "RemoteUrl": "https://github.com/rezakj/icellr",
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  "_exports": [
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    "add.adt",
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    "bubble.gg.plot",
    "capture.image.10x",
    "cc",
    "cell.cycle",
    "cell.filter",
    "cell.gating",
    "cell.type.pred",
    "change.clust",
    "clono.plot",
    "clust.avg.exp",
    "clust.cond.info",
    "clust.ord",
    "clust.rm",
    "clust.stats.plot",
    "cluster.plot",
    "data.aggregation",
    "data.scale",
    "down.sample",
    "find_neighbors",
    "find.dim.genes",
    "findMarkers",
    "gate.to.clust",
    "gene.plot",
    "gene.stats",
    "gg.cor",
    "heatmap.gg.plot",
    "hto.anno",
    "i.score",
    "iba",
    "iclust",
    "load.h5",
    "load10x",
    "make.bed",
    "make.gene.model",
    "make.obj",
    "myImp",
    "norm.adt",
    "norm.data",
    "opt.pcs.plot",
    "prep.vdj",
    "pseudotime",
    "pseudotime.knetl",
    "pseudotime.tree",
    "qc.stats",
    "Rphenograph",
    "run.anchor",
    "run.cca",
    "run.clustering",
    "run.diff.exp",
    "run.diffusion.map",
    "run.impute",
    "run.knetl",
    "run.mnn",
    "run.pc.tsne",
    "run.pca",
    "run.phenograph",
    "run.tsne",
    "run.umap",
    "spatial.plot",
    "stats.plot",
    "top.markers",
    "vdj.stats",
    "volcano.ma.plot"
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  "_datasets": [
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      "name": "g2m.phase",
      "title": "A dataset of G2 and M phase genes",
      "object": "g2m.phase",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "s.phase",
      "title": "A dataset of S phase genes",
      "object": "s.phase",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
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  "_help": [
    {
      "page": "add.10x.image",
      "title": "Add image data to iCellR object",
      "topics": [
        "add.10x.image"
      ]
    },
    {
      "page": "add.adt",
      "title": "Add CITE-seq antibody-derived tags (ADT)",
      "topics": [
        "add.adt"
      ]
    },
    {
      "page": "add.vdj",
      "title": "Add V(D)J recombination data",
      "topics": [
        "add.vdj"
      ]
    },
    {
      "page": "adt.rna.merge",
      "title": "Merge RNA and ADT data",
      "topics": [
        "adt.rna.merge"
      ]
    },
    {
      "page": "bubble.gg.plot",
      "title": "Create bubble heatmaps for genes in clusters or conditions.",
      "topics": [
        "bubble.gg.plot"
      ]
    },
    {
      "page": "capture.image.10x",
      "title": "Read 10X image data",
      "topics": [
        "capture.image.10x"
      ]
    },
    {
      "page": "cc",
      "title": "Calculate Cell cycle phase prediction",
      "topics": [
        "cc"
      ]
    },
    {
      "page": "cell.cycle",
      "title": "Cell cycle phase prediction",
      "topics": [
        "cell.cycle"
      ]
    },
    {
      "page": "cell.filter",
      "title": "Filter cells",
      "topics": [
        "cell.filter"
      ]
    },
    {
      "page": "cell.gating",
      "title": "Cell gating",
      "topics": [
        "cell.gating"
      ]
    },
    {
      "page": "cell.type.pred",
      "title": "Create heatmaps or dot plots for genes in clusters to find thier cell types using ImmGen data.",
      "topics": [
        "cell.type.pred"
      ]
    },
    {
      "page": "change.clust",
      "title": "Change the cluster number or re-name them",
      "topics": [
        "change.clust"
      ]
    },
    {
      "page": "clono.plot",
      "title": "Make 2D and 3D scatter plots for clonotypes.",
      "topics": [
        "clono.plot"
      ]
    },
    {
      "page": "clust.avg.exp",
      "title": "Create a data frame of mean expression of genes per cluster",
      "topics": [
        "clust.avg.exp"
      ]
    },
    {
      "page": "clust.cond.info",
      "title": "Calculate cluster and conditions frequencies",
      "topics": [
        "clust.cond.info"
      ]
    },
    {
      "page": "clust.ord",
      "title": "Sort and relabel the clusters randomly or based on pseudotime",
      "topics": [
        "clust.ord"
      ]
    },
    {
      "page": "clust.rm",
      "title": "Remove the cells that are in a cluster",
      "topics": [
        "clust.rm"
      ]
    },
    {
      "page": "clust.stats.plot",
      "title": "Plotting tSNE, PCA, UMAP, Diffmap and other dim reductions",
      "topics": [
        "clust.stats.plot"
      ]
    },
    {
      "page": "cluster.plot",
      "title": "Plot nGenes, UMIs and perecent mito",
      "topics": [
        "cluster.plot"
      ]
    },
    {
      "page": "data.aggregation",
      "title": "Merge multiple data frames and add the condition names to their cell ids",
      "topics": [
        "data.aggregation"
      ]
    },
    {
      "page": "data.scale",
      "title": "Scale data",
      "topics": [
        "data.scale"
      ]
    },
    {
      "page": "down.sample",
      "title": "Down sample conditions",
      "topics": [
        "down.sample"
      ]
    },
    {
      "page": "find_neighbors",
      "title": "K Nearest Neighbour Search",
      "topics": [
        "find_neighbors"
      ]
    },
    {
      "page": "find.dim.genes",
      "title": "Find model genes from PCA data",
      "topics": [
        "find.dim.genes"
      ]
    },
    {
      "page": "findMarkers",
      "title": "Find marker genes for each cluster",
      "topics": [
        "findMarkers"
      ]
    },
    {
      "page": "g2m.phase",
      "title": "A dataset of G2 and M phase genes",
      "topics": [
        "g2m.phase"
      ]
    },
    {
      "page": "gate.to.clust",
      "title": "Assign cluster number to cell ids",
      "topics": [
        "gate.to.clust"
      ]
    },
    {
      "page": "gene.plot",
      "title": "Make scatter, box and bar plots for genes",
      "topics": [
        "gene.plot"
      ]
    },
    {
      "page": "gene.stats",
      "title": "Make statistical information for each gene across all the cells (SD, mean, expression, etc.)",
      "topics": [
        "gene.stats"
      ]
    },
    {
      "page": "gg.cor",
      "title": "Gene-gene correlation. This function helps to visulaize and calculate gene-gene correlations.",
      "topics": [
        "gg.cor"
      ]
    },
    {
      "page": "heatmap.gg.plot",
      "title": "Create heatmaps for genes in clusters or conditions.",
      "topics": [
        "heatmap.gg.plot"
      ]
    },
    {
      "page": "hto.anno",
      "title": "Demultiplexing HTOs",
      "topics": [
        "hto.anno"
      ]
    },
    {
      "page": "i.score",
      "title": "Cell cycle phase prediction",
      "topics": [
        "i.score"
      ]
    },
    {
      "page": "iba",
      "title": "iCellR Batch Alignment (IBA)",
      "topics": [
        "iba"
      ]
    },
    {
      "page": "iclust",
      "title": "iCellR Clustering",
      "topics": [
        "iclust"
      ]
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