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Run copy-number alteration inference

Source: R/RunCNV.R
RunCNV.Rd

Run expression-based single-cell or spatial copy-number alteration backends and store the results in a consistent SCOP result bundle.

Usage

RunCNV(
  srt,
  method = c("copykat", "fastCNV", "scevan", "infercnv", "numbat"),
  assay = NULL,
  layer = "counts",
  group.by = NULL,
  reference.by = NULL,
  reference = NULL,
  genome = c("hg38", "hg19", "mm10"),
  gene_order = NULL,
  sample.by = NULL,
  allele_counts = NULL,
  reference_counts = NULL,
  output_dir = NULL,
  prefix = "CNV",
  tool_name = "CNV",
  store_matrix = TRUE,
  verbose = TRUE,
  ...
)

Arguments

srt

A Seurat object.

method

CNA/CNV backend. Supported backends are "copykat", "fastCNV", "scevan", "infercnv", and "numbat".

assay

Assay to use. NULL uses the default assay.

layer

Assay layer used as the expression matrix.

group.by

Optional metadata column forwarded to supported backends and stored as cell annotation.

reference.by

Metadata column identifying reference/normal cells. Required for "infercnv" and "fastCNV".

reference

Reference labels in reference.by.

genome

Reference genome label.

gene_order

Gene coordinate table or a path to one. The table should contain gene, chromosome, start, and end columns. If NULL, SCOP tries to resolve these columns from assay feature metadata.

sample.by

Optional sample metadata column.

allele_counts

Allele count table for "numbat". This is forwarded to numbat::run_numbat() as df_allele.

reference_counts

Reference expression profile for "numbat". This is forwarded to numbat::run_numbat() as lambdas_ref.

output_dir

Optional backend output directory.

prefix

Prefix for metadata columns.

tool_name

Name used for srt@tools.

store_matrix

Whether to store the normalized CNV matrix in srt@tools[[tool_name]].

verbose

Whether to print the message. Default is TRUE.

...

Additional parameters forwarded to the selected backend.

Value

A Seurat object with CNV metadata columns and a result bundle in srt@tools[[tool_name]].

See also

CNVPlot

Examples

if (FALSE) { # \dontrun{
# copykat uses raw counts and can infer diploid/aneuploid cells directly.
srt <- RunCNV(
  srt,
  method = "copykat",
  genome = "hg38"
)

# fastCNV and inferCNV require a normal/reference cell annotation.
srt <- RunCNV(
  srt,
  method = "fastCNV",
  reference.by = "celltype",
  reference = "Normal",
  genome = "hg38"
)

gene_order <- data.frame(
  gene = rownames(srt),
  chr = "chr1",
  start = seq_len(nrow(srt)) * 1000,
  end = seq_len(nrow(srt)) * 1000 + 999
)
srt <- RunCNV(
  srt,
  method = "infercnv",
  reference.by = "celltype",
  reference = "Normal",
  gene_order = gene_order
)

# Numbat can also be run when allele-aware preprocessing
# outputs are available.

CNVPlot(srt, plot_type = "heatmap", group.by = "CNV_prediction")
CNVPlot(srt, plot_type = "dim", value = "CNV_prediction")
} # }