Run CIBERSORT deconvolution

Estimate immune cell proportions from a bulk expression matrix using the optional GitHub Moonerss/CIBERSORT package or the package C++ backend. The C++ backend is the default and has no external R-package dependency. sig_matrix = "LM22" downloads the LM22 signature matrix from mengxu98/datasets and caches it locally.

Usage

RunCIBERSORT(
  object = NULL,
  count_matrix = NULL,
  sig_matrix = "LM22",
  bulk_assay = "counts",
  perm = 100,
  QN = TRUE,
  absolute = FALSE,
  backend = c("cpp", "r"),
  cores = NULL,
  seed = 123L,
  verbose = TRUE,
  ...
)

Arguments

object

Optional SummarizedExperiment object or expression matrix. When a SummarizedExperiment is provided, results are stored in metadata(object)[["Deconvolution"]].

count_matrix

Optional expression matrix with genes in rows and samples in columns. Used when object is not provided as a matrix.

sig_matrix

Signature matrix, local file path, or "LM22".

bulk_assay

Assay name in object used as the bulk counts matrix.

perm

Number of CIBERSORT permutations.

QN

Whether CIBERSORT should use quantile normalization.

absolute

Passed to CIBERSORT when supported by the installed package. The C++ backend currently returns relative fractions.

backend

CIBERSORT backend. "cpp" uses the package LIBSVM implementation. "r" is an optional reference backend from Moonerss/CIBERSORT.

cores

Number of CPU cores used by the C++ backend. NULL uses up to 4 local cores. n_threads passed through ... is accepted as a backward-compatible alias when cores = NULL.

seed

Random seed used by the C++ permutation backend.

verbose

Whether to print the message. Default is TRUE.

...

Additional parameters forwarded to the internal deconvolution backend.

Value

A deconvolution result bundle for matrix input, or the modified SummarizedExperiment object for SummarizedExperiment input.

Examples

data(islet_bulk)

if (FALSE) {
  # Run CIBERSORT
  islet_bulk <- RunCIBERSORT(
    object = islet_bulk,
    sig_matrix = "LM22",
    bulk_assay = "counts",
    perm = 100,
    QN = TRUE
  )

  # Immune abundance stacked bar plot
  p1 <- ImmuneAbundancePlot(
    object = islet_bulk,
    plot_type = "bar",
    group.by = "condition"
  )
  p1

  # Immune cell correlation heatmap
  p2 <- ImmuneAbundancePlot(
    object = islet_bulk,
    plot_type = "cor"
  )
  p2

  # Gene-immune correlation butterfly plot
  p3 <- GeneImmuneCorPlot(
    object = islet_bulk,
    features = rownames(SummarizedExperiment::assay(islet_bulk, "counts"))[1:3]
  )
  p3
}