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Plot CNV results

Source: R/CNVPlot.R
CNVPlot.Rd

Plot CNV results

Usage

CNVPlot(
  object,
  plot_type = c("heatmap", "dim", "spatial", "bar", "tree"),
  tool_name = "CNV",
  method = NULL,
  group.by = NULL,
  reduction = NULL,
  value = NULL,
  cluster_tree_by = c("cluster", "cell"),
  palette = "Chinese",
  palcolor = NULL,
  heatmap_palette = "RdBu",
  heatmap_palcolor = NULL,
  title = NULL,
  subtitle = NULL,
  theme_use = "theme_scop",
  theme_args = list(),
  heatmap_limits = NULL,
  srt = NULL,
  ...
)

Source

The example uses expression and cell annotations from GSE70630.

Arguments

object

A Seurat object.

plot_type

Plot type: "heatmap", "dim", "spatial", "bar", or "tree". Heatmaps show cells in rows and features in columns. Use cluster_columns for cells or cluster_rows for features; both default to FALSE.

tool_name

Name of the stored CNV results.

method

CNV method. NULL uses the active method.

group.by

A metadata column identifying cell groups.

reduction

Reduction to plot. NULL uses DefaultReduction.

value

Metadata column for "dim", "spatial", or "bar".

cluster_tree_by

Build the tree from cells or CNV clusters.

palette, palcolor

Colors for discrete groups.

heatmap_palette, heatmap_palcolor

Colors for heatmap scores. Defaults to muted blue, white, and red.

title, subtitle

Plot title and ggplot subtitle.

theme_use, theme_args

Theme name or function, plus extra theme arguments.

heatmap_limits

Color limits. NULL uses c(0.75, 1.25) for inferCNV and c(-1, 1) otherwise. "auto" uses the 95th percentile of absolute deviations from the method's midpoint (1 or 0), with the method limits as fallback. A numeric pair sets the limits and must span the midpoint.

srt

Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

...

Arguments passed to other functions.

Value

A ComplexHeatmap::Heatmap for "heatmap", an hclust for "tree", or a ggplot, patchwork, or list of ggplot objects otherwise.

See also

RunCNV()

Examples

base_url <- paste0(
  "https://raw.githubusercontent.com/broadinstitute/infercnv/",
  "1def6c930335d00ddf251474abb0a31a1c74b0da/inst/extdata/"
)
files <- c("oligodendroglioma_expression_downsampled.counts.matrix.gz",
           "oligodendroglioma_annotations_downsampled.txt")
counts_file <- tempfile(fileext = ".gz")
labels_file <- tempfile()
utils::download.file(paste0(base_url, files[1]), counts_file, mode = "wb")
utils::download.file(paste0(base_url, files[2]), labels_file, mode = "wb")
counts <- as.matrix(utils::read.delim(counts_file, row.names = 1, check.names = FALSE))
labels <- utils::read.delim(labels_file, header = FALSE, row.names = 1)
cohort <- labels[colnames(counts), 1]
colnames(counts) <- paste0("cell_", colnames(counts))
srt <- SeuratObject::CreateSeuratObject(Matrix::Matrix(counts, sparse = TRUE))
srt$group <- factor(
  ifelse(startsWith(cohort, "malignant_"), sub("^malignant_", "Tumor ", cohort), "Reference"),
  levels = c("Reference", "Tumor 93", "Tumor 97", "Tumor MGH36", "Tumor MGH53")
)
set.seed(42)
srt <- RunCNV(srt, method = "copykat", reference.by = "group", reference = "Reference")
#> ℹ [2026-11-12 04:59:30] Run copykat CNV backend with 10338 features and 184 cells
#> ℹ [2026-11-12 04:59:30] Attached copykat to the search path so its lazy data objects are resolvable by the backend
#> [1] "running copykat v1.2.5"
#> [1] "step1: read and filter data ..."
#> [1] "10338 genes, 184 cells in raw data"
#> [1] "9450 genes past LOW.DR filtering"
#> [1] "step 2: annotations gene coordinates ..."
#> [1] "start annotation ..."
#> [1] "step 3: smoothing data with dlm ..."
#> [1] "step 4: measuring baselines ..."
#> [1] "42 known normal cells found in dataset"
#> [1] "run with known normal..."
#> [1] "baseline is from known input"
#> [1] "step 5: segmentation..."
#> [1] "step 6: convert to genomic bins..."
#> [1] "step 7: re-adjust baseline ..."
#> [1] "step 8: final prediction ..."
#> [1] "step 9: saving results..."
#> [1] "step 10: ploting heatmap ..."
#> Time difference of 1.151121 mins
#> ✔ [2026-11-12 05:00:39] copykat CNV results stored in `srt@tools[[CNV]]`
CNVPlot(srt, group.by = "group", heatmap_limits = "auto")