This function creates a heatmap to visualise an expression matrix
Usage
expression_heatmap(
expression.matrix.subset,
top.annotation.ids = NULL,
metadata,
type = c("Z-score", "Log2 Expression", "Expression"),
show.column.names = TRUE
)Arguments
- expression.matrix.subset
a subset of rows from the expression matrix; rows correspond to genes and columns correspond to samples
- top.annotation.ids
a vector of column indices denoting which columns of the metadata should become heatmap annotations
- 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- type
type of rescaling; one of "Expression" (defautl, does nothing), "Log2 Expression" (returns log2(x + 1) for every value), "Mean Scaled" (each row is scaled by its average), "Z-score" (each row is centered and scaled to mean = 0 and sd = 1)
- show.column.names
whether to show the column names below the heatmap; default is TRUE
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)
)
print(expression_heatmap(head(expression.matrix.preproc), NULL, metadata))
