noisyr (version 1.0.0)

calculate_expression_similarity_counts: Calcualate the expression levels and expression levels similarity matrices using the count matrix

Description

This function generates an average similarity (correlation/inverse distance) coefficient for every sliding window, for each sample in the expression matrix. That is done by comparing the distribution of genes in each window across samples.

Usage

calculate_expression_similarity_counts(
  expression.matrix,
  similarity.measure = "correlation_pearson",
  n.elements.per.window = NULL,
  n.step = NULL,
  n.step.fraction = 0.05,
  ...
)

Arguments

expression.matrix

the expression matrix, can be normalized or not

similarity.measure

one of the correlation or distance metrics to be used, defaults to pearson correlation; list of all methods in get_methods_correlation_distance

n.elements.per.window

number of elements to have in a window, default 10% of the number of rows

n.step

step size to slide across, default 1% of n.elements.per.window

n.step.fraction

an alternative way to specify the step size, as a fraction of the window length; default is 5%

...

arguments passed on to other methods

Value

A list with three elements: the first element is the expression matrix, as supplied; the other two are the expression levels matrix and expression levels similarity matrix; they have the same # of columns as the expression matrix, and n.elements.per.window * n.step rows.

See Also

calculate_expression_similarity_transcript

Examples

# NOT RUN {
calculate_expression_similarity_counts(
    expression.matrix = matrix(1:100, ncol = 5),
    similarity.measure = "correlation_pearson",
    n.elements.per.window = 3)
# }