Package 'numbat' reference manual

Title:	Haplotype-Aware CNV Analysis from scRNA-Seq
Description:	A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.
Authors:	Teng Gao [cre, aut], Ruslan Soldatov [aut], Hirak Sarkar [aut], Evan Biederstedt [aut], Peter Kharchenko [aut]
Maintainer:	Teng Gao <[email protected]>
License:	MIT + file LICENSE
Version:	1.4.2
Built:	2024-11-18 05:26:04 UTC
Source:	https://github.com/kharchenkolab/numbat

centromere regions (hg19)

Description

centromere regions (hg19)

Usage

acen_hg19
acen_hg19

Format

An object of class tbl_df (inherits from tbl, data.frame) with 22 rows and 3 columns.

centromere regions (hg38)

Description

centromere regions (hg38)

Usage

acen_hg38
acen_hg38

Format

An object of class tbl_df (inherits from tbl, data.frame) with 22 rows and 3 columns.

Utility function to make reference gene expression profiles

Description

Utility function to make reference gene expression profiles

Usage

aggregate_counts(count_mat, annot, normalized = TRUE, verbose = TRUE)
aggregate_counts(count_mat, annot, normalized = TRUE, verbose = TRUE)

Arguments

`count_mat`	matrix/dgCMatrix Gene expression counts
`annot`	dataframe Cell annotation with columns "cell" and "group"
`normalized`	logical Whether to return normalized expression values
`verbose`	logical Verbosity

Value

matrix Reference gene expression levels

Examples

ref_custom = aggregate_counts(count_mat_ref, annot_ref, verbose = FALSE)
ref_custom = aggregate_counts(count_mat_ref, annot_ref, verbose = FALSE)

Call CNVs in a pseudobulk profile using the Numbat joint HMM

Description

Call CNVs in a pseudobulk profile using the Numbat joint HMM

Usage

analyze_bulk(
  bulk,
  t = 1e-05,
  gamma = 20,
  theta_min = 0.08,
  logphi_min = 0.25,
  nu = 1,
  min_genes = 10,
  exp_only = FALSE,
  allele_only = FALSE,
  bal_cnv = TRUE,
  retest = TRUE,
  find_diploid = TRUE,
  diploid_chroms = NULL,
  classify_allele = FALSE,
  run_hmm = TRUE,
  prior = NULL,
  exclude_neu = TRUE,
  phasing = TRUE,
  verbose = TRUE
)
analyze_bulk(
  bulk,
  t = 1e-05,
  gamma = 20,
  theta_min = 0.08,
  logphi_min = 0.25,
  nu = 1,
  min_genes = 10,
  exp_only = FALSE,
  allele_only = FALSE,
  bal_cnv = TRUE,
  retest = TRUE,
  find_diploid = TRUE,
  diploid_chroms = NULL,
  classify_allele = FALSE,
  run_hmm = TRUE,
  prior = NULL,
  exclude_neu = TRUE,
  phasing = TRUE,
  verbose = TRUE
)

Arguments

`bulk`	dataframe Pesudobulk profile
`t`	numeric Transition probability
`gamma`	numeric Dispersion parameter for the Beta-Binomial allele model
`theta_min`	numeric Minimum imbalance threshold
`logphi_min`	numeric Minimum log expression deviation threshold
`nu`	numeric Phase switch rate
`min_genes`	integer Minimum number of genes to call an event
`exp_only`	logical Whether to run expression-only HMM
`allele_only`	logical Whether to run allele-only HMM
`bal_cnv`	logical Whether to call balanced amplifications/deletions
`retest`	logical Whether to retest CNVs after Viterbi decoding
`find_diploid`	logical Whether to run diploid region identification routine
`diploid_chroms`	character vector User-given chromosomes that are known to be in diploid state
`classify_allele`	logical Whether to only classify allele (internal use only)
`run_hmm`	logical Whether to run HMM (internal use only)
`prior`	numeric vector Prior probabilities of states (internal use only)
`exclude_neu`	logical Whether to exclude neutral segments from retesting (internal use only)
`phasing`	logical Whether to use phasing information (internal use only)
`verbose`	logical Verbosity

Value

a pseudobulk profile dataframe with called CNV information

Examples

bulk_analyzed = analyze_bulk(bulk_example, t = 1e-5, find_diploid = FALSE, retest = FALSE)
bulk_analyzed = analyze_bulk(bulk_example, t = 1e-5, find_diploid = FALSE, retest = FALSE)

example reference cell annotation

Description

example reference cell annotation

Usage

annot_ref
annot_ref

Format

An object of class data.frame with 50 rows and 2 columns.

Annotate genes on allele dataframe

Description

Annotate genes on allele dataframe

Usage

annotate_genes(df, gtf)
annotate_genes(df, gtf)

Arguments

`df`	dataframe Allele count dataframe
`gtf`	dataframe Gene gtf

Value

dataframe Allele dataframe with gene column

example pseudobulk dataframe

Description

example pseudobulk dataframe

Usage

bulk_example
bulk_example

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3935 rows and 83 columns.

chromosome sizes (hg19)

Description

chromosome sizes (hg19)

Usage

chrom_sizes_hg19
chrom_sizes_hg19

Format

An object of class data.table (inherits from data.frame) with 22 rows and 2 columns.

chromosome sizes (hg38)

Description

chromosome sizes (hg38)

Usage

chrom_sizes_hg38
chrom_sizes_hg38

Format

An object of class data.table (inherits from data.frame) with 22 rows and 2 columns.

Plot CNV heatmap

Description

Plot CNV heatmap

Usage

cnv_heatmap(
  segs,
  var = "group",
  label_group = TRUE,
  legend = TRUE,
  exclude_gap = TRUE,
  genome = "hg38"
)
cnv_heatmap(
  segs,
  var = "group",
  label_group = TRUE,
  legend = TRUE,
  exclude_gap = TRUE,
  genome = "hg38"
)

Arguments

`segs`	dataframe Segments to plot. Need columns "seg_start", "seg_end", "cnv_state"
`var`	character Column to facet by
`label_group`	logical Label the groups
`legend`	logical Display the legend
`exclude_gap`	logical Whether to mark gap regions
`genome`	character Genome build, either 'hg38' or 'hg19'

Value

ggplot Heatmap of CNVs along the genome

Examples

p = cnv_heatmap(segs_example)
p = cnv_heatmap(segs_example)

example gene expression count matrix

Description

example gene expression count matrix

Usage

count_mat_example
count_mat_example

Format

An object of class dgCMatrix with 1024 rows and 173 columns.

example reference count matrix

Description

example reference count matrix

Usage

count_mat_ref
count_mat_ref

Format

An object of class dgCMatrix with 1000 rows and 50 columns.

Call clonal LOH using SNP density. Rcommended for cell lines or tumor samples with no normal cells.

Description

Call clonal LOH using SNP density. Rcommended for cell lines or tumor samples with no normal cells.

Usage

detect_clonal_loh(bulk, t = 1e-05, snp_rate_loh = 5, min_depth = 0)
detect_clonal_loh(bulk, t = 1e-05, snp_rate_loh = 5, min_depth = 0)

Arguments

`bulk`	dataframe Pseudobulk profile
`t`	numeric Transition probability
`snp_rate_loh`	numeric The assumed SNP density in clonal LOH regions
`min_depth`	integer Minimum coverage to filter SNPs

Value

dataframe LOH segments

Examples

segs_loh = detect_clonal_loh(bulk_example)
segs_loh = detect_clonal_loh(bulk_example)

example allele count dataframe

Description

example allele count dataframe

Usage

df_allele_example
df_allele_example

Format

An object of class data.frame with 41167 rows and 11 columns.

genome gap regions (hg19)

Description

genome gap regions (hg19)

Usage

gaps_hg19
gaps_hg19

Format

An object of class data.table (inherits from data.frame) with 28 rows and 3 columns.

genome gap regions (hg38)

Description

genome gap regions (hg38)

Usage

gaps_hg38
gaps_hg38

Format

An object of class data.table (inherits from data.frame) with 30 rows and 3 columns.

Aggregate single-cell data into combined bulk expression and allele profile

Description

Aggregate single-cell data into combined bulk expression and allele profile

Usage

get_bulk(
  count_mat,
  lambdas_ref,
  df_allele,
  gtf,
  subset = NULL,
  min_depth = 0,
  nu = 1,
  segs_loh = NULL,
  verbose = TRUE
)
get_bulk(
  count_mat,
  lambdas_ref,
  df_allele,
  gtf,
  subset = NULL,
  min_depth = 0,
  nu = 1,
  segs_loh = NULL,
  verbose = TRUE
)

Arguments

`count_mat`	dgCMatrix Gene expression counts
`lambdas_ref`	matrix Reference expression profiles
`df_allele`	dataframe Single-cell allele counts
`gtf`	dataframe Transcript gtf
`subset`	vector Subset of cells to aggregate
`min_depth`	integer Minimum coverage to filter SNPs
`nu`	numeric Phase switch rate
`segs_loh`	dataframe Segments with clonal LOH to be excluded
`verbose`	logical Verbosity

Value

dataframe Pseudobulk gene expression and allele profile

Examples

bulk_example = get_bulk(
    count_mat = count_mat_example,
    lambdas_ref = ref_hca,
    df_allele = df_allele_example,
    gtf = gtf_hg38)
bulk_example = get_bulk(
    count_mat = count_mat_example,
    lambdas_ref = ref_hca,
    df_allele = df_allele_example,
    gtf = gtf_hg38)

Get a tidygraph tree with simplified mutational history.

Description

Specify either max_cost or n_cut. max_cost works similarly as h and n_cut works similarly as k in stats::cutree. The top-level normal diploid clone is always included.

Usage

get_gtree(tree, P, n_cut = 0, max_cost = 0)
get_gtree(tree, P, n_cut = 0, max_cost = 0)

Arguments

`tree`	phylo Single-cell phylogenetic tree
`P`	matrix Genotype probability matrix
`n_cut`	integer Number of cuts on the phylogeny to define subclones
`max_cost`	numeric Likelihood threshold to collapse internal branches

Value

tbl_graph Phylogeny annotated with branch lengths and mutation events

example smoothed gene expression dataframe

Description

example smoothed gene expression dataframe

Usage

gexp_roll_example
gexp_roll_example

Format

An object of class data.frame with 10 rows and 2000 columns.

gene model (hg19)

Description

gene model (hg19)

Usage

gtf_hg19
gtf_hg19

Format

An object of class data.table (inherits from data.frame) with 26841 rows and 5 columns.

gene model (hg38)

Description

gene model (hg38)

Usage

gtf_hg38
gtf_hg38

Format

An object of class data.table (inherits from data.frame) with 26807 rows and 5 columns.

gene model (mm10)

Description

gene model (mm10)

Usage

gtf_mm10
gtf_mm10

Format

An object of class data.table (inherits from data.frame) with 30336 rows and 5 columns.

example hclust tree

Description

example hclust tree

Usage

hc_example
hc_example

Format

An object of class hclust of length 7.

example joint single-cell cnv posterior dataframe

Description

example joint single-cell cnv posterior dataframe

Usage

joint_post_example
joint_post_example

Format

An object of class data.table (inherits from data.frame) with 3806 rows and 71 columns.

example mutation graph

Description

example mutation graph

Usage

mut_graph_example
mut_graph_example

Format

An object of class igraph of length 5.

Numbat R6 class

Description

Used to allow users to plot results

Value

a new 'Numbat' object

Public fields

label: character Sample name
gtf: dataframe Transcript annotation
joint_post: dataframe Joint posterior
exp_post: dataframe Expression posterior
allele_post: dataframe Allele posetrior
bulk_subtrees: dataframe Bulk profiles of lineage subtrees
bulk_clones: dataframe Bulk profiles of clones
segs_consensus: dataframe Consensus segments
tree_post: list Tree posterior
mut_graph: igraph Mutation history graph
gtree: tbl_graph Single-cell phylogeny
clone_post: dataframe Clone posteriors
gexp_roll_wide: matrix Smoothed expression of single cells
P: matrix Genotype probability matrix
treeML: matrix Maximum likelihood tree as phylo object
hc: hclust Initial hierarchical clustering

Methods

Method `new()`

initialize Numbat class

Usage

Numbat$new(out_dir, i = 2, gtf = gtf_hg38, verbose = TRUE)

Arguments

out_dir: character string Output directory
i: integer Get results from which iteration (default=2)
gtf: dataframe Transcript gtf (default=gtf_hg38)
verbose: logical Whether to output verbose results (default=TRUE)

Returns

a new 'Numbat' object

Method `plot_phylo_heatmap()`

Plot the single-cell CNV calls in a heatmap and the corresponding phylogeny

Usage

Numbat$plot_phylo_heatmap(...)

Arguments

...: additional parameters passed to plot_phylo_heatmap()

Method `plot_exp_roll()`

Plot window-smoothed expression profiles

Usage

Numbat$plot_exp_roll(k = 3, n_sample = 300, ...)

Arguments

k: integer Number of clusters
n_sample: integer Number of cells to subsample
...: additional parameters passed to plot_exp_roll()

Method `plot_mut_history()`

Plot the mutation history of the tumor

Usage

Numbat$plot_mut_history(...)

Arguments

...: additional parameters passed to plot_mut_history()

Method `plot_sc_tree()`

Plot the single cell phylogeny

Usage

Numbat$plot_sc_tree(...)

Arguments

...: additional parameters passed to plot_sc_tree()

Method `plot_consensus()`

Plot consensus segments

Usage

Numbat$plot_consensus(...)

Arguments

...: additional parameters passed to plot_sc_tree()

Method `plot_clone_profile()`

Plot clone cnv profiles

Usage

Numbat$plot_clone_profile(...)

Arguments

...: additional parameters passed to plot_clone_profile()

Method `cutree()`

Re-define subclones on the phylogeny.

Usage

Numbat$cutree(max_cost = 0, n_cut = 0)

Arguments

max_cost: numeric Likelihood threshold to collapse internal branches
n_cut: integer Number of cuts on the phylogeny to define subclones

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Numbat$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

example single-cell phylogeny

Description

example single-cell phylogeny

Usage

phylogeny_example
phylogeny_example

Format

An object of class tbl_graph (inherits from igraph) of length 345.

Plot a group of pseudobulk HMM profiles

Description

Plot a group of pseudobulk HMM profiles

Usage

plot_bulks(bulks, ..., ncol = 1, title = TRUE, title_size = 8)
plot_bulks(bulks, ..., ncol = 1, title = TRUE, title_size = 8)

Arguments

`bulks`	dataframe Pseudobulk profiles annotated with "sample" column
`...`	additional parameters passed to plot_psbulk()
`ncol`	integer Number of columns
`title`	logical Whether to add titles to individual plots
`title_size`	numeric Size of titles

Value

a ggplot object

Examples

p = plot_bulks(bulk_example)
p = plot_bulks(bulk_example)

Plot consensus CNVs

Description

Plot consensus CNVs

Usage

plot_consensus(segs)
plot_consensus(segs)

Arguments

segs

dataframe Consensus segments

Value

ggplot object

Examples

p = plot_consensus(segs_example)
p = plot_consensus(segs_example)

Plot single-cell smoothed expression magnitude heatmap

Description

Plot single-cell smoothed expression magnitude heatmap

Usage

plot_exp_roll(
  gexp_roll_wide,
  hc,
  k,
  gtf,
  lim = 0.8,
  n_sample = 300,
  reverse = TRUE,
  plot_tree = TRUE
)
plot_exp_roll(
  gexp_roll_wide,
  hc,
  k,
  gtf,
  lim = 0.8,
  n_sample = 300,
  reverse = TRUE,
  plot_tree = TRUE
)

Arguments

`gexp_roll_wide`	matrix Cell x gene smoothed expression magnitudes
`hc`	hclust Hierarchical clustring result
`k`	integer Number of clusters
`gtf`	dataframe Transcript GTF
`lim`	numeric Limit for expression magnitudes
`n_sample`	integer Number of cells to subsample
`reverse`	logical Whether to reverse the cell order
`plot_tree`	logical Whether to plot the dendrogram

Value

ggplot A single-cell heatmap of window-smoothed expression CNV signals

Examples

p = plot_exp_roll(gexp_roll_example, gtf = gtf_hg38, hc = hc_example, k = 3)
p = plot_exp_roll(gexp_roll_example, gtf = gtf_hg38, hc = hc_example, k = 3)

Plot mutational history

Description

Plot mutational history

Usage

plot_mut_history(
  G,
  clone_post = NULL,
  edge_label_size = 4,
  node_label_size = 6,
  node_size = 10,
  arrow_size = 2,
  show_clone_size = TRUE,
  show_distance = TRUE,
  legend = TRUE,
  edge_label = TRUE,
  node_label = TRUE,
  horizontal = TRUE,
  pal = NULL
)
plot_mut_history(
  G,
  clone_post = NULL,
  edge_label_size = 4,
  node_label_size = 6,
  node_size = 10,
  arrow_size = 2,
  show_clone_size = TRUE,
  show_distance = TRUE,
  legend = TRUE,
  edge_label = TRUE,
  node_label = TRUE,
  horizontal = TRUE,
  pal = NULL
)

Arguments

`G`	igraph Mutation history graph
`clone_post`	dataframe Clone assignment posteriors
`edge_label_size`	numeric Size of edge label
`node_label_size`	numeric Size of node label
`node_size`	numeric Size of nodes
`arrow_size`	numeric Size of arrows
`show_clone_size`	logical Whether to show clone size
`show_distance`	logical Whether to show evolutionary distance between clones
`legend`	logical Whether to show legend
`edge_label`	logical Whether to label edges
`node_label`	logical Whether to label nodes
`horizontal`	logical Whether to use horizontal layout
`pal`	named vector Node colors

Value

ggplot object

Examples

p = plot_mut_history(mut_graph_example)
p = plot_mut_history(mut_graph_example)

Plot single-cell CNV calls along with the clonal phylogeny

Description

Plot single-cell CNV calls along with the clonal phylogeny

Usage

plot_phylo_heatmap(
  gtree,
  joint_post,
  segs_consensus,
  clone_post = NULL,
  p_min = 0.9,
  annot = NULL,
  pal_annot = NULL,
  annot_title = "Annotation",
  annot_scale = NULL,
  clone_dict = NULL,
  clone_bar = TRUE,
  clone_stack = TRUE,
  pal_clone = NULL,
  clone_title = "Genotype",
  clone_legend = TRUE,
  line_width = 0.1,
  tree_height = 1,
  branch_width = 0.2,
  tip_length = 0.2,
  annot_bar_width = 0.25,
  clone_bar_width = 0.25,
  bar_label_size = 7,
  tvn_line = TRUE,
  clone_line = FALSE,
  exclude_gap = FALSE,
  root_edge = TRUE,
  raster = FALSE,
  show_phylo = TRUE
)
plot_phylo_heatmap(
  gtree,
  joint_post,
  segs_consensus,
  clone_post = NULL,
  p_min = 0.9,
  annot = NULL,
  pal_annot = NULL,
  annot_title = "Annotation",
  annot_scale = NULL,
  clone_dict = NULL,
  clone_bar = TRUE,
  clone_stack = TRUE,
  pal_clone = NULL,
  clone_title = "Genotype",
  clone_legend = TRUE,
  line_width = 0.1,
  tree_height = 1,
  branch_width = 0.2,
  tip_length = 0.2,
  annot_bar_width = 0.25,
  clone_bar_width = 0.25,
  bar_label_size = 7,
  tvn_line = TRUE,
  clone_line = FALSE,
  exclude_gap = FALSE,
  root_edge = TRUE,
  raster = FALSE,
  show_phylo = TRUE
)

Arguments

`gtree`	tbl_graph The single-cell phylogeny
`joint_post`	dataframe Joint single cell CNV posteriors
`segs_consensus`	datatframe Consensus segment dataframe
`clone_post`	dataframe Clone assignment posteriors
`p_min`	numeric Probability threshold to display CNV calls
`annot`	dataframe Cell annotations, dataframe with 'cell' and additional annotation columns
`pal_annot`	named vector Colors for cell annotations
`annot_title`	character Legend title for the annotation bar
`annot_scale`	ggplot scale Color scale for the annotation bar
`clone_dict`	named vector Clone annotations, mapping from cell name to clones
`clone_bar`	logical Whether to display clone bar plot
`clone_stack`	character Whether to plot clone assignment probabilities as stacked bar
`pal_clone`	named vector Clone colors
`clone_title`	character Legend title for the clone bar
`clone_legend`	logical Whether to display the clone legend
`line_width`	numeric Line width for CNV heatmap
`tree_height`	numeric Relative height of the phylogeny plot
`branch_width`	numeric Line width in the phylogeny
`tip_length`	numeric Length of tips in the phylogeny
`annot_bar_width`	numeric Width of annotation bar
`clone_bar_width`	numeric Width of clone genotype bar
`bar_label_size`	numeric Size of sidebar text labels
`tvn_line`	logical Whether to draw line separating tumor and normal cells
`clone_line`	logical Whether to display borders for clones in the heatmap
`exclude_gap`	logical Whether to mark gap regions
`root_edge`	logical Whether to plot root edge
`raster`	logical Whether to raster images
`show_phylo`	logical Whether to display phylogeny on y axis

Value

ggplot panel

Examples

p = plot_phylo_heatmap(
    gtree = phylogeny_example,
    joint_post = joint_post_example,
    segs_consensus = segs_example)
p = plot_phylo_heatmap(
    gtree = phylogeny_example,
    joint_post = joint_post_example,
    segs_consensus = segs_example)

Plot a pseudobulk HMM profile

Description

Plot a pseudobulk HMM profile

Usage

plot_psbulk(
  bulk,
  use_pos = TRUE,
  allele_only = FALSE,
  min_LLR = 5,
  min_depth = 8,
  exp_limit = 2,
  phi_mle = TRUE,
  theta_roll = FALSE,
  dot_size = 0.8,
  dot_alpha = 0.5,
  legend = TRUE,
  exclude_gap = TRUE,
  genome = "hg38",
  text_size = 10,
  raster = FALSE
)
plot_psbulk(
  bulk,
  use_pos = TRUE,
  allele_only = FALSE,
  min_LLR = 5,
  min_depth = 8,
  exp_limit = 2,
  phi_mle = TRUE,
  theta_roll = FALSE,
  dot_size = 0.8,
  dot_alpha = 0.5,
  legend = TRUE,
  exclude_gap = TRUE,
  genome = "hg38",
  text_size = 10,
  raster = FALSE
)

Arguments

`bulk`	dataframe Pseudobulk profile
`use_pos`	logical Use marker position instead of index as x coordinate
`allele_only`	logical Only plot alleles
`min_LLR`	numeric LLR threshold for event filtering
`min_depth`	numeric Minimum coverage depth for a SNP to be plotted
`exp_limit`	numeric Expression logFC axis limit
`phi_mle`	logical Whether to plot estimates of segmental expression fold change
`theta_roll`	logical Whether to plot rolling estimates of allele imbalance
`dot_size`	numeric Size of marker dots
`dot_alpha`	numeric Transparency of the marker dots
`legend`	logical Whether to show legend
`exclude_gap`	logical Whether to mark gap regions and centromeres
`genome`	character Genome build, either 'hg38' or 'hg19'
`text_size`	numeric Size of text in the plot
`raster`	logical Whether to raster images

Value

ggplot Plot of pseudobulk HMM profile

Examples

p = plot_psbulk(bulk_example)
p = plot_psbulk(bulk_example)

Plot single-cell smoothed expression magnitude heatmap

Description

Plot single-cell smoothed expression magnitude heatmap

Usage

plot_sc_tree(
  gtree,
  label_mut = TRUE,
  label_size = 3,
  dot_size = 2,
  branch_width = 0.5,
  tip = TRUE,
  tip_length = 0.5,
  pal_clone = NULL
)
plot_sc_tree(
  gtree,
  label_mut = TRUE,
  label_size = 3,
  dot_size = 2,
  branch_width = 0.5,
  tip = TRUE,
  tip_length = 0.5,
  pal_clone = NULL
)

Arguments

`gtree`	tbl_graph The single-cell phylogeny
`label_mut`	logical Whether to label mutations
`label_size`	numeric Size of mutation labels
`dot_size`	numeric Size of mutation nodes
`branch_width`	numeric Width of branches in tree
`tip`	logical Whether to plot tip point
`tip_length`	numeric Length of the tips
`pal_clone`	named vector Clone colors

Value

ggplot A single-cell phylogeny with mutation history labeled

Examples

p = plot_sc_tree(phylogeny_example)
p = plot_sc_tree(phylogeny_example)

HMM object for unit tests

Description

HMM object for unit tests

Usage

pre_likelihood_hmm
pre_likelihood_hmm

Format

An object of class list of length 10.

reference expression magnitudes from HCA

Description

reference expression magnitudes from HCA

Usage

ref_hca
ref_hca

Format

An object of class matrix (inherits from array) with 24756 rows and 12 columns.

reference expression counts from HCA

Description

reference expression counts from HCA

Usage

ref_hca_counts
ref_hca_counts

Format

An object of class matrix (inherits from array) with 24857 rows and 12 columns.

Run workflow to decompose tumor subclones

Description

Run workflow to decompose tumor subclones

Usage

run_numbat(
  count_mat,
  lambdas_ref,
  df_allele,
  genome = "hg38",
  out_dir = tempdir(),
  max_iter = 2,
  max_nni = 100,
  t = 1e-05,
  gamma = 20,
  min_LLR = 5,
  alpha = 1e-04,
  eps = 1e-05,
  max_entropy = 0.5,
  init_k = 3,
  min_cells = 50,
  tau = 0.3,
  nu = 1,
  max_cost = ncol(count_mat) * tau,
  n_cut = 0,
  min_depth = 0,
  common_diploid = TRUE,
  min_overlap = 0.45,
  ncores = 1,
  ncores_nni = ncores,
  random_init = FALSE,
  segs_loh = NULL,
  call_clonal_loh = FALSE,
  verbose = TRUE,
  diploid_chroms = NULL,
  segs_consensus_fix = NULL,
  use_loh = NULL,
  min_genes = 10,
  skip_nj = FALSE,
  multi_allelic = TRUE,
  p_multi = 1 - alpha,
  plot = TRUE,
  check_convergence = FALSE,
  exclude_neu = TRUE
)
run_numbat(
  count_mat,
  lambdas_ref,
  df_allele,
  genome = "hg38",
  out_dir = tempdir(),
  max_iter = 2,
  max_nni = 100,
  t = 1e-05,
  gamma = 20,
  min_LLR = 5,
  alpha = 1e-04,
  eps = 1e-05,
  max_entropy = 0.5,
  init_k = 3,
  min_cells = 50,
  tau = 0.3,
  nu = 1,
  max_cost = ncol(count_mat) * tau,
  n_cut = 0,
  min_depth = 0,
  common_diploid = TRUE,
  min_overlap = 0.45,
  ncores = 1,
  ncores_nni = ncores,
  random_init = FALSE,
  segs_loh = NULL,
  call_clonal_loh = FALSE,
  verbose = TRUE,
  diploid_chroms = NULL,
  segs_consensus_fix = NULL,
  use_loh = NULL,
  min_genes = 10,
  skip_nj = FALSE,
  multi_allelic = TRUE,
  p_multi = 1 - alpha,
  plot = TRUE,
  check_convergence = FALSE,
  exclude_neu = TRUE
)

Arguments

`count_mat`	dgCMatrix Raw count matrices where rownames are genes and column names are cells
`lambdas_ref`	matrix Either a named vector with gene names as names and normalized expression as values, or a matrix where rownames are genes and columns are pseudobulk names
`df_allele`	dataframe Allele counts per cell, produced by preprocess_allele
`genome`	character Genome version (hg38, hg19, or mm10)
`out_dir`	string Output directory
`max_iter`	integer Maximum number of iterations to run the phyologeny optimization
`max_nni`	integer Maximum number of iterations to run NNI in the ML phylogeny inference
`t`	numeric Transition probability
`gamma`	numeric Dispersion parameter for the Beta-Binomial allele model
`min_LLR`	numeric Minimum LLR to filter CNVs
`alpha`	numeric P value cutoff for diploid finding
`eps`	numeric Convergence threshold for ML tree search
`max_entropy`	numeric Entropy threshold to filter CNVs
`init_k`	integer Number of clusters in the initial clustering
`min_cells`	integer Minimum number of cells to run HMM on
`tau`	numeric Factor to determine max_cost as a function of the number of cells (0-1)
`nu`	numeric Phase switch rate
`max_cost`	numeric Likelihood threshold to collapse internal branches
`n_cut`	integer Number of cuts on the phylogeny to define subclones
`min_depth`	integer Minimum allele depth
`common_diploid`	logical Whether to find common diploid regions in a group of peusdobulks
`min_overlap`	numeric Minimum CNV overlap threshold
`ncores`	integer Number of threads to use
`ncores_nni`	integer Number of threads to use for NNI
`random_init`	logical Whether to initiate phylogney using a random tree (internal use only)
`segs_loh`	dataframe Segments of clonal LOH to be excluded
`call_clonal_loh`	logical Whether to call segments with clonal LOH
`verbose`	logical Verbosity
`diploid_chroms`	vector Known diploid chromosomes
`segs_consensus_fix`	dataframe Pre-determined segmentation of consensus CNVs
`use_loh`	logical Whether to include LOH regions in the expression baseline
`min_genes`	integer Minimum number of genes to call a segment
`skip_nj`	logical Whether to skip NJ tree construction and only use UPGMA
`multi_allelic`	logical Whether to call multi-allelic CNVs
`p_multi`	numeric P value cutoff for calling multi-allelic CNVs
`plot`	logical Whether to plot results
`check_convergence`	logical Whether to terminate iterations based on consensus CNV convergence
`exclude_neu`	logical Whether to exclude neutral segments from CNV retesting (internal use only)

Value

a status code

example CNV segments dataframe

Description

example CNV segments dataframe

Usage

segs_example
segs_example

Format

An object of class data.table (inherits from data.frame) with 27 rows and 30 columns.

UPGMA and WPGMA clustering

Description

UPGMA and WPGMA clustering

Usage

upgma(D, method = "average", ...)
upgma(D, method = "average", ...)

Arguments

`D`	A distance matrix.
`method`	The agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". The default is "average".
`...`	Further arguments passed to or from other methods.

example VCF header

Description

example VCF header

Usage

vcf_meta
vcf_meta

Format

An object of class character of length 65.

Package 'numbat'

Help Index

centromere regions (hg19)

Description

Usage

Format

centromere regions (hg38)

Description

Usage

Format

Utility function to make reference gene expression profiles

Description

Usage

Arguments

Value

Examples

Call CNVs in a pseudobulk profile using the Numbat joint HMM

Description

Usage

Arguments

Value

Examples

example reference cell annotation

Description

Usage

Format

Annotate genes on allele dataframe

Description

Usage

Arguments

Value

example pseudobulk dataframe

Description

Usage

Format

chromosome sizes (hg19)

Description

Usage

Format

chromosome sizes (hg38)

Description

Usage

Format

Plot CNV heatmap

Description

Usage

Arguments

Value

Examples

example gene expression count matrix

Description

Usage

Format

example reference count matrix

Description

Usage

Format

Call clonal LOH using SNP density. Rcommended for cell lines or tumor samples with no normal cells.

Description

Usage

Arguments

Value

Examples

example allele count dataframe

Description

Usage

Format

genome gap regions (hg19)

Description

Usage

Format

genome gap regions (hg38)

Description

Usage

Format

Aggregate single-cell data into combined bulk expression and allele profile

Description

Usage

Arguments

Value

Method `new()`

Method `plot_phylo_heatmap()`

Method `plot_exp_roll()`

Method `plot_mut_history()`

Method `plot_sc_tree()`

Method `plot_consensus()`

Method `plot_clone_profile()`

Method `cutree()`

Method `clone()`