Perform GRN inference

This function performs Gene Regulatory Network inference on a subset of the expression matrix, for a set of potential targets

Usage

infer_GRN(
  expression.matrix,
  metadata,
  anno,
  seed = 13,
  targets,
  condition,
  samples,
  inference_method
)

Arguments

expression.matrix

the expression matrix; rows correspond to genes and columns correspond to samples; usually preprocessed by preprocessExpressionMatrix; a list (of the same length as modality) can be provided if #' length(modality) > 1

metadata

a data frame containing metadata for the samples contained in the expression.matrix; must contain at minimum two columns: the first column must contain the column names of the expression.matrix, while the last column is assumed to contain the experimental conditions that will be tested for differential expression; a list (of the same length as modality) can be provided if #' length(modality) > 1

anno

annotation data frame containing a match between the row names of the expression.matrix (usually ENSEMBL IDs) and the gene names that should be rendered within the app and in output files; this object is created by generateShinyApp using the org.db specified

seed

the random seed to be set when running GRN inference, to ensure reproducibility of outputs

targets

the target genes of interest around which the GRN is built; must be row names of the expression matrix

condition

name of the metadata column to select samples from

samples

names of the sample groups to select; must appear in metadata[[condition]]

inference_method

method used for GRN inference; only supported method is currently GENIE3.

Value

The adjacency matrix of the inferred network

Examples

expression.matrix.preproc <- as.matrix(read.csv(
  system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"), 
  row.names = 1
))[1:500, ]

metadata <- data.frame(
  srr = colnames(expression.matrix.preproc), 
  timepoint = rep(c("0h", "12h", "36h"), each = 2)
)

anno <- AnnotationDbi::select(
  getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
  keys = rownames(expression.matrix.preproc),
  keytype = 'ENSEMBL',
  columns = 'SYMBOL'
) %>%
  dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
  dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
#> 'select()' returned 1:many mapping between keys and columns

res <- infer_GRN(
  expression.matrix = expression.matrix.preproc,
  metadata = metadata,
  anno = anno,
  seed = 13,
  targets = c("Hecw2", "Akr1cl"),
  condition = "timepoint",
  samples = "0h",
  inference_method = "GENIE3"
)