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Create signature matrix using CIBERSORTx

Source: R/create_signature_matrix.R
create_signature_matrix.Rd

Generates a signature matrix from reference profiles and cell type classes using the CIBERSORTx Docker image. This signature matrix contains cell type-specific marker proteins that will be used for deconvolution.

Usage

create_signature_matrix(
  refsample = NULL,
  phenoclasses = NULL,
  g_min = 200,
  g_max = 400,
  q_value = 0.01,
  replicates = 5,
  sampling = 0.5,
  fraction = 0.75,
  filter = FALSE,
  verbose = FALSE,
  QN = FALSE,
  single_cell = FALSE,
  use_sudo = FALSE,
  ...
)

Arguments

refsample

A numeric matrix containing reference profiles with genes as row names and samples as columns. This should be preprocessed data from pure cell populations.

phenoclasses

A numeric matrix, data frame, or tibble containing cell type classification (0/1/2). If provided as a matrix or base data frame, the cell types are assumed to be in the row names. If provided as a tibble, the cell type identifiers should be included as a column. This is typically created using the create_phenoclasses() function.

g_min

Minimum number of genes per cell type in the signature matrix. Default is 200.

g_max

Maximum number of genes per cell type in the signature matrix. Default is 400.

q_value

Q-value threshold for differential expression. Default is 0.01.

replicates

Number of replicates to use for building the reference file (only relevant when single_cell=TRUE). Default is 5.

sampling

Fraction of available gene expression profiles selected by random sampling (only relevant when single_cell=TRUE). Default is 0.5.

fraction

Fraction of cells of the same identity showing evidence of expression (only relevant when single_cell=TRUE). Default is 0.75.

filter

Logical indicating whether to remove non-hematopoietic genes. Default is FALSE.

verbose

Logical indicating whether to print detailed output. Default is FALSE.

QN

Logical indicating whether to run quantile normalization. Default is FALSE.

single_cell

Logical indicating whether to create signature from scRNA-Seq data. Default is FALSE.

use_sudo

Logical indicating whether to use sudo for Docker commands. Default is FALSE.

...

Additional arguments passed to the function.

Value

A numeric matrix with genes as rows and cell types as columns, representing the expression profile of each cell type. This signature matrix can be used as input for deconvolution algorithms to estimate cell type proportions in mixed samples.

Details

This function uses the CIBERSORTx Docker image to construct a signature matrix based on the input reference profiles and cell type classifications. The CIBERSORTx token and email must be set as environment variables either in the project directory's .Renviron file or in the user's home directory .Renviron file.

See also

create_phenoclasses for creating the phenoclasses input and deconvolute for using the signature matrix in deconvolution.

Examples

if (FALSE) { # \dontrun{
# Load preprocessed pure samples data
pure_samples <- readRDS(system.file("extdata", "pure_samples_matrix.rds",
                                    package = "proteoDeconv"))

# Create phenoclasses for the samples
mapping_rules <- list(
  "CD8+ T cells" = "CD8",
  "Monocytes" = "Mono"
)
phenoclasses <- create_phenoclasses(
  data = pure_samples,
  mapping_rules = mapping_rules
)

# Create signature matrix
signature_matrix <- create_signature_matrix(
  refsample = pure_samples,
  phenoclasses = phenoclasses,
  g_min = 200,
  g_max = 400,
  q_value = 0.01
)
} # }