plotCohesiveness: Plot of cohesiveness results for a specific category of...

In fjcamlab/cohesiveness: Non-parametric feature selection for large omic datasets

Description

This function returns several plots describing the results obtained by cohesiveness.

Usage

plotCohesiveness(mx, cl, id, coh,
                    category = NA, color = "darkred")

Arguments

mx	a numeric matrix containing the omic dataset, where rows are features and columns are samples.
cl	a character vector describing which samples are belonging to each category.
id	a character indicating the ID of the feature to represent.
coh	results provided by plotCohesiveness R function.
category	a character naming which category of samples must be highlighted.
color	color of highlighted category of samples.

Details

R function to perform a cohesiveness analysis.

Value

This function returns several plots describing the results.

Author(s)

FJ Campos-Laborie ([e-mail])

See Also

plotCohesiveness

Examples

## Example of omic dataset based on normal distribution data
mx <- t(sapply(seq_len(1000), function(x) rnorm(n = 200, mean = 5, sd = 1)))

## Formatting the example matrix
rownames(mx) <- paste("g", seq_len(1000), sep = "")
colnames(mx) <- paste("s", seq_len(200), sep = "")

## Naming a vector of sample categories
classes <- rep(LETTERS[1:8], c(20, 40, 15, 40, 65, 5, 5, 10))
names(classes) <- colnames(mx)

## Running cohesiveness
resultsCohesiveness <- cohesiveness(mx, classes)

## Example of plot of cohesiveness for 'g1'
plotCohesiveness(mx, cl = classes,
                 coh = resultsCohesiveness, id = "g1")

fjcamlab/cohesiveness documentation built on May 6, 2019, 12:09 p.m.