# 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

 ```1 2 3 4 5 6 7 8``` ```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.

`calculate_expression_similarity_transcript`
 ```1 2 3 4``` ```calculate_expression_similarity_counts( expression.matrix = matrix(1:100, ncol = 5), similarity.measure = "correlation_pearson", n.elements.per.window = 3) ```