calculate_expression_similarity_counts: Calcualate the expression levels and expression levels...

In noisyr: Noise Quantification in High Throughput Sequencing Output

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

calculate_expression_similarity_counts(
    expression.matrix = matrix(1:100, ncol = 5),
    similarity.measure = "correlation_pearson",
    n.elements.per.window = 3)

noisyr documentation built on April 16, 2021, 5:07 p.m.