{
  "_id": "6aca65bffe5cc25786e88687",
  "Package": "numbat",
  "Title": "Haplotype-Aware CNV Analysis from scRNA-Seq",
  "URL": "https://github.com/kharchenkolab/numbat/,\nhttps://kharchenkolab.github.io/numbat/",
  "Version": "1.5.2",
  "Authors@R": "c(person(\"Teng\",\"Gao\", email=\"tgaoteng@gmail.com\", role=c(\"cre\", \"aut\")), person(\"Ruslan\", \"Soldatov\", email=\"soldatr@mskcc.org\", role=\"aut\"), person(\"Hirak\", \"Sarkar\", email=\"hirak_sarkar@hms.harvard.edu\", role=\"aut\"), person(\"Evan\", \"Biederstedt\", email=\"evan.biederstedt@gmail.com\", role=\"aut\"), person(\"Peter\", \"Kharchenko\", email = \"peter_kharchenko@hms.harvard.edu\", role = \"aut\"))",
  "Description": "A computational method that infers copy number variations\n(CNVs) in cancer scRNA-seq data and reconstructs the tumor\nphylogeny. 'numbat' integrates signals from gene expression,\nallelic ratio, and population haplotype structures to\naccurately infer allele-specific CNVs in single cells and\nreconstruct their lineage relationship. 'numbat' can be used\nto: 1. detect allele-specific copy number variations from\nsingle-cells; 2. differentiate tumor versus normal cells in the\ntumor microenvironment; 3. infer the clonal architecture and\nevolutionary history of profiled tumors. 'numbat' does not\nrequire tumor/normal-paired DNA or genotype data, but operates\nsolely on the donor scRNA-data data (for example, 10x Cell\nRanger output). Additional examples and documentations are\navailable at <https://kharchenkolab.github.io/numbat/>. For\ndetails on the method please see Gao et al. Nature\nBiotechnology (2022) <doi:10.1038/s41587-022-01468-y>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "biocViews": "",
  "Config/testthat/edition": "3",
  "NeedsCompilation": "yes",
  "SystemRequirements": "GNU make",
  "Author": "Teng Gao [cre, aut], Ruslan Soldatov [aut], Hirak Sarkar [aut],\nEvan Biederstedt [aut], Peter Kharchenko [aut]",
  "Maintainer": "Teng Gao <tgaoteng@gmail.com>",
  "RoxygenNote": "7.3.2",
  "Config/pak/sysreqs": "libcairo2-dev cmake libfontconfig1-dev\nlibfreetype6-dev libglpk-dev make libicu-dev libpng-dev\nlibuv1-dev libxml2-dev",
  "Repository": "https://kharchenkolab.r-universe.dev",
  "Date/Publication": "2026-02-04 04:56:07 UTC",
  "RemoteUrl": "https://github.com/kharchenkolab/numbat",
  "RemoteRef": "HEAD",
  "RemoteSha": "b444f3c51f1ed38f400b2b8bc435cd40fabe6f4e",
  "Packaged": {
    "Date": "2026-10-10 15:23:00 UTC",
    "User": "root"
  },
  "_user": "kharchenkolab",
  "_type": "src",
  "_file": "numbat_1.5.2.tar.gz",
  "_fileid": "https://r2.ropensci.org/754e0efdc7e337f6deaacc27a704fb41999b0a3e4893183c38f656ba7e16f9c1",
  "_filesize": 4455366,
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  "_expires": "2027-01-18T16:20:12.000Z",
  "_created": "2026-10-10T15:23:00.000Z",
  "_published": "2026-10-10T16:20:14.836Z",
  "_buildurl": "https://github.com/r-universe/kharchenkolab/actions/runs/38063124345",
  "_status": "success",
  "_host": "GitHub-Actions",
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  "_upstream": "https://github.com/kharchenkolab/numbat",
  "_commit": {
    "id": "b444f3c51f1ed38f400b2b8bc435cd40fabe6f4e",
    "author": "teng-gao <tgaoteng@gmail.com>",
    "committer": "teng-gao <tgaoteng@gmail.com>",
    "message": "Fix detect_clonal_loh example error by filtering empty gene names\n\nThe function was failing when encountering genes with empty string names.\nThe filter !is.na(gene) doesn't catch empty strings, causing summarise()\nto fail when unique(na.omit(Y_obs)) returns an empty vector.\n\nFixed by also filtering out empty gene names: gene != \"\"\n\nFixes example error in R CMD check.\n",
    "time": 1770180967
  },
  "_maintainer": {
    "name": "Teng Gao",
    "email": "tgaoteng@gmail.com",
    "login": "teng-gao",
    "twitter": "@tGaoTeng",
    "description": "Postdoctoral fellow at Boston Children’s Hospital and Harvard Medical School",
    "uuid": 13375875
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  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
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  "_selfowned": true,
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  "_tags": [
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  "_topics": [
    "cancer-genomics",
    "cnv-detection",
    "lineage-tracing",
    "phylogeny",
    "single-cell",
    "single-cell-analysis",
    "single-cell-atac-seq",
    "single-cell-omics",
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  "_stars": 231,
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  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/numbat"
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  "_rbuild": "4.6.1",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/numbat.html",
    "extra/readme.html",
    "extra/readme.md",
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  "_homeurl": "https://github.com/kharchenkolab/numbat",
  "_realowner": "kharchenkolab",
  "_cranurl": true,
  "_releases": [
    {
      "version": "1.0.2",
      "date": "2022-09-15"
    },
    {
      "version": "1.1.0",
      "date": "2022-11-29"
    },
    {
      "version": "1.2.1",
      "date": "2023-01-14"
    },
    {
      "version": "1.2.2",
      "date": "2023-02-14"
    },
    {
      "version": "1.3.0",
      "date": "2023-04-02"
    },
    {
      "version": "1.3.2",
      "date": "2023-06-06"
    },
    {
      "version": "1.3.2-1",
      "date": "2023-06-17"
    },
    {
      "version": "1.4.0",
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    {
      "version": "1.4.2",
      "date": "2024-09-20"
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    {
      "version": "1.5.1",
      "date": "2025-10-21"
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    {
      "version": "1.5.2",
      "date": "2026-02-04"
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  ],
  "_exports": [
    "aggregate_counts",
    "analyze_bulk",
    "annotate_genes",
    "cnv_heatmap",
    "detect_clonal_loh",
    "get_bulk",
    "get_gtree",
    "Numbat",
    "plot_bulks",
    "plot_consensus",
    "plot_exp_roll",
    "plot_mut_history",
    "plot_phylo_heatmap",
    "plot_psbulk",
    "plot_sc_tree",
    "run_numbat"
  ],
  "_datasets": [
    {
      "name": "acen_hg19",
      "title": "centromere regions (hg19)",
      "object": "acen_hg19",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "acen_hg38",
      "title": "centromere regions (hg38)",
      "object": "acen_hg38",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "annot_ref",
      "title": "example reference cell annotation",
      "object": "annot_ref",
      "class": [
        "data.frame"
      ],
      "fields": [
        "cell",
        "group"
      ],
      "rows": 50,
      "table": true,
      "tojson": true
    },
    {
      "name": "bulk_example",
      "title": "example pseudobulk dataframe",
      "object": "bulk_example",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "snp_id",
        "CHROM",
        "POS",
        "cM",
        "REF",
        "ALT",
        "GT",
        "gene",
        "AD",
        "DP",
        "AR",
        "snp_index",
        "pBAF",
        "pAD",
        "inter_snp_cm",
        "p_s",
        "Y_obs",
        "lambda_obs",
        "lambda_ref",
        "d_obs",
        "gene_start",
        "gene_end",
        "region",
        "gene_length",
        "gene_index",
        "logFC",
        "lnFC",
        "sse",
        "n_cells",
        "members",
        "sample",
        "state",
        "boundary",
        "seg_start_index",
        "seg_end_index",
        "loh",
        "diploid",
        "mu",
        "sig",
        "state_post",
        "haplo_theta_min",
        "major_count",
        "minor_count",
        "nu",
        "seg_start",
        "seg_end",
        "seg_length",
        "seg",
        "seg_cons",
        "cnv_state",
        "n_genes",
        "n_snps",
        "theta_hat",
        "theta_mle",
        "theta_sigma",
        "L_y_n",
        "L_y_d",
        "L_y_a",
        "phi_mle",
        "phi_sigma",
        "L_x_n",
        "L_x_d",
        "L_x_a",
        "Z_cnv",
        "Z_n",
        "Z",
        "logBF",
        "p_neu",
        "p_loh",
        "p_amp",
        "p_del",
        "p_bamp",
        "p_bdel",
        "LLR_x",
        "LLR_y",
        "LLR",
        "cnv_state_post",
        "p_up",
        "haplo_post",
        "haplo_naive",
        "theta_hat_roll",
        "phi_mle_roll",
        "gamma"
      ],
      "rows": 3935,
      "table": true,
      "tojson": true
    },
    {
      "name": "chrom_sizes_hg19",
      "title": "chromosome sizes (hg19)",
      "object": "chrom_sizes_hg19",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "size"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "chrom_sizes_hg38",
      "title": "chromosome sizes (hg38)",
      "object": "chrom_sizes_hg38",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "size"
      ],
      "rows": 22,
      "table": true,
      "tojson": true
    },
    {
      "name": "count_mat_example",
      "title": "example gene expression count matrix",
      "object": "count_mat_example",
      "class": [
        "dgCMatrix"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "count_mat_ref",
      "title": "example reference count matrix",
      "object": "count_mat_ref",
      "class": [
        "dgCMatrix"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "df_allele_example",
      "title": "example allele count dataframe",
      "object": "df_allele_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "cell",
        "snp_id",
        "CHROM",
        "POS",
        "cM",
        "REF",
        "ALT",
        "AD",
        "DP",
        "GT",
        "gene"
      ],
      "rows": 41167,
      "table": true,
      "tojson": true
    },
    {
      "name": "gaps_hg19",
      "title": "genome gap regions (hg19)",
      "object": "gaps_hg19",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 28,
      "table": true,
      "tojson": true
    },
    {
      "name": "gaps_hg38",
      "title": "genome gap regions (hg38)",
      "object": "gaps_hg38",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "CHROM",
        "start",
        "end"
      ],
      "rows": 30,
      "table": true,
      "tojson": true
    },
    {
      "name": "gexp_roll_example",
      "title": "example smoothed gene expression dataframe",
      "object": "gexp_roll_example",
      "class": [
        "data.frame"
      ],
      "fields": [],
      "rows": 10,
      "table": true,
      "tojson": true
    },
    {
      "name": "gtf_hg19",
      "title": "gene model (hg19)",
      "object": "gtf_hg19",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "gene",
        "gene_start",
        "gene_end",
        "CHROM",
        "gene_length"
      ],
      "rows": 26841,
      "table": true,
      "tojson": true
    },
    {
      "name": "gtf_hg38",
      "title": "gene model (hg38)",
      "object": "gtf_hg38",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "gene",
        "gene_start",
        "gene_end",
        "CHROM",
        "gene_length"
      ],
      "rows": 26807,
      "table": true,
      "tojson": true
    },
    {
      "name": "gtf_mm10",
      "title": "gene model (mm10)",
      "object": "gtf_mm10",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "gene",
        "gene_start",
        "gene_end",
        "CHROM",
        "gene_length"
      ],
      "rows": 30336,
      "table": true,
      "tojson": true
    },
    {
      "name": "hc_example",
      "title": "example hclust tree",
      "object": "hc_example",
      "class": [
        "hclust"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "joint_post_example",
      "title": "example joint single-cell cnv posterior dataframe",
      "object": "joint_post_example",
      "class": [
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        "data.frame"
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        "CHROM",
        "seg",
        "cnv_state",
        "l11_x",
        "l20_x",
        "l10_x",
        "l21_x",
        "l31_x",
        "l22_x",
        "l00_x",
        "Z_x",
        "Z_cnv_x",
        "Z_n_x",
        "logBF_x",
        "l11_y",
        "l20_y",
        "l10_y",
        "l21_y",
        "l31_y",
        "l22_y",
        "l00_y",
        "Z_y",
        "Z_cnv_y",
        "Z_n_y",
        "logBF_y",
        "n_snp",
        "MAF",
        "major",
        "seg_start",
        "seg_end",
        "n_genes",
        "n_snps",
        "prior_loh",
        "prior_amp",
        "prior_del",
        "prior_bamp",
        "prior_bdel",
        "LLR",
        "LLR_x",
        "LLR_y",
        "l11",
        "l20",
        "l10",
        "l21",
        "l31",
        "l22",
        "l00",
        "Z_amp",
        "Z_loh",
        "Z_del",
        "Z_bamp",
        "Z_bdel",
        "Z_n",
        "Z",
        "Z_cnv",
        "p_amp",
        "p_neu",
        "p_del",
        "p_loh",
        "p_bamp",
        "p_bdel",
        "logBF",
        "p_cnv",
        "p_n",
        "p_cnv_x",
        "p_cnv_y",
        "cnv_state_mle",
        "cnv_state_map",
        "seg_label",
        "avg_entropy"
      ],
      "rows": 3806,
      "table": true,
      "tojson": true
    },
    {
      "name": "mut_graph_example",
      "title": "example mutation graph",
      "object": "mut_graph_example",
      "class": [
        "igraph"
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      "fields": [],
      "table": false,
      "tojson": false
    },
    {
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      "class": [
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        "igraph"
      ],
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      "tojson": false
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      "name": "pre_likelihood_hmm",
      "title": "HMM object for unit tests",
      "object": "pre_likelihood_hmm",
      "class": [
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      "table": false,
      "tojson": true
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      "title": "reference expression magnitudes from HCA",
      "object": "ref_hca",
      "class": [
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        "array"
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        "Fibroblast",
        "Macrophage",
        "CD4+T",
        "CD8+T",
        "Endothelial",
        "Myeloid",
        "Monocyte",
        "Dendritic",
        "Plasma",
        "B",
        "Epithelial"
      ],
      "rows": 24756,
      "table": true,
      "tojson": true
    },
    {
      "name": "ref_hca_counts",
      "title": "reference expression counts from HCA",
      "object": "ref_hca_counts",
      "class": [
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        "Fibroblast",
        "Macrophage",
        "CD4+T",
        "CD8+T",
        "Endothelial",
        "Myeloid",
        "Monocyte",
        "Dendritic",
        "Plasma",
        "B",
        "Epithelial"
      ],
      "rows": 24857,
      "table": true,
      "tojson": true
    },
    {
      "name": "segs_example",
      "title": "example CNV segments dataframe",
      "object": "segs_example",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "sample",
        "CHROM",
        "seg",
        "cnv_state",
        "cnv_state_post",
        "seg_start",
        "seg_end",
        "seg_start_index",
        "seg_end_index",
        "theta_mle",
        "theta_sigma",
        "phi_mle",
        "phi_sigma",
        "p_loh",
        "p_del",
        "p_amp",
        "p_bamp",
        "p_bdel",
        "LLR",
        "LLR_y",
        "LLR_x",
        "n_genes",
        "n_snps",
        "component",
        "LLR_sample",
        "seg_length",
        "seg_cons",
        "n_states",
        "cnv_states",
        "group"
      ],
      "rows": 27,
      "table": true,
      "tojson": true
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    {
      "name": "vcf_meta",
      "title": "example VCF header",
      "object": "vcf_meta",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
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  ],
  "_help": [
    {
      "page": "acen_hg19",
      "title": "centromere regions (hg19)",
      "topics": [
        "acen_hg19"
      ]
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    {
      "page": "acen_hg38",
      "title": "centromere regions (hg38)",
      "topics": [
        "acen_hg38"
      ]
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    {
      "page": "aggregate_counts",
      "title": "Utility function to make reference gene expression profiles",
      "topics": [
        "aggregate_counts"
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    },
    {
      "page": "analyze_bulk",
      "title": "Call CNVs in a pseudobulk profile using the Numbat joint HMM",
      "topics": [
        "analyze_bulk"
      ]
    },
    {
      "page": "annot_ref",
      "title": "example reference cell annotation",
      "topics": [
        "annot_ref"
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    },
    {
      "page": "annotate_genes",
      "title": "Annotate genes on allele dataframe",
      "topics": [
        "annotate_genes"
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    {
      "page": "bulk_example",
      "title": "example pseudobulk dataframe",
      "topics": [
        "bulk_example"
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    {
      "page": "chrom_sizes_hg19",
      "title": "chromosome sizes (hg19)",
      "topics": [
        "chrom_sizes_hg19"
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    },
    {
      "page": "chrom_sizes_hg38",
      "title": "chromosome sizes (hg38)",
      "topics": [
        "chrom_sizes_hg38"
      ]
    },
    {
      "page": "cnv_heatmap",
      "title": "Plot CNV heatmap",
      "topics": [
        "cnv_heatmap"
      ]
    },
    {
      "page": "count_mat_example",
      "title": "example gene expression count matrix",
      "topics": [
        "count_mat_example"
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    {
      "page": "count_mat_ref",
      "title": "example reference count matrix",
      "topics": [
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    {
      "page": "detect_clonal_loh",
      "title": "Call clonal LOH using SNP density.  Rcommended for cell lines or tumor samples with no normal cells.",
      "topics": [
        "detect_clonal_loh"
      ]
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      "page": "df_allele_example",
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      "topics": [
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      ]
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    {
      "page": "gaps_hg19",
      "title": "genome gap regions (hg19)",
      "topics": [
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    {
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      "title": "genome gap regions (hg38)",
      "topics": [
        "gaps_hg38"
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    {
      "page": "get_bulk",
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      "topics": [
        "get_bulk"
      ]
    },
    {
      "page": "get_gtree",
      "title": "Get a tidygraph tree with simplified mutational history.",
      "topics": [
        "get_gtree"
      ]
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    {
      "page": "gexp_roll_example",
      "title": "example smoothed gene expression dataframe",
      "topics": [
        "gexp_roll_example"
      ]
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    {
      "page": "gtf_hg19",
      "title": "gene model (hg19)",
      "topics": [
        "gtf_hg19"
      ]
    },
    {
      "page": "gtf_hg38",
      "title": "gene model (hg38)",
      "topics": [
        "gtf_hg38"
      ]
    },
    {
      "page": "gtf_mm10",
      "title": "gene model (mm10)",
      "topics": [
        "gtf_mm10"
      ]
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    {
      "page": "hc_example",
      "title": "example hclust tree",
      "topics": [
        "hc_example"
      ]
    },
    {
      "page": "joint_post_example",
      "title": "example joint single-cell cnv posterior dataframe",
      "topics": [
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    {
      "page": "mut_graph_example",
      "title": "example mutation graph",
      "topics": [
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    {
      "page": "Numbat",
      "title": "Numbat R6 class",
      "topics": [
        "Numbat"
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      "page": "phylogeny_example",
      "title": "example single-cell phylogeny",
      "topics": [
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      "page": "plot_bulks",
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      "topics": [
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    {
      "page": "plot_consensus",
      "title": "Plot consensus CNVs",
      "topics": [
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      "topics": [
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      "topics": [
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      "topics": [
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      "title": "Plot a pseudobulk HMM profile",
      "topics": [
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