compute_control: Generate a negative control.

In angelesarzalluz/scfilters: A correlation-based method for quality filtering of single-cell RNAseq data

Description

Computes correlations for all the randomized windows against a window of choice.

Usage

compute_control(randomized_windows, dataset, window_number, cor_method)

Arguments

randomized_windows	A list containing as many elements as randomized top windows have been computed.
dataset	A data frame containing the binned data.
window_number	An integer indicating the bin for which the control is being computed.
cor_method	A string indicating the type of correlation to use.

Details

compute_control only correlates the set of randomized windows to one of the windows into which the data set was divided. Therefore, it needs to be called once for each window.

The correlation vector for each random is generated by iterating the genes in it and correlating them to those in the selected window. As a result, there will be as many correlation values in the vector as genes in the top window. At the same time, the output will have as many elements as randomized versions of the top window have been computed. Consequently, both top window size and number of randomizations impact the computation speed of the process.

The correlation method argument is passed on to the cor function, in the stats package, and therefore, the same options as this function provides are available. However, it is adviseable to use pearson correlation, since it presents the most advantageous balance of result quality and computational efficiency.

Value

A list containing the negative control: a vector of correlations corresponding to correlating each randomized window to the chosen window in the data.

angelesarzalluz/scfilters documentation built on May 10, 2019, 11:46 a.m.