scatterVDJ: Create a scatterplot for diversity evenness and abundance

In kstreet13/VDJdive: Analysis Tools for 10X V(D)J Data

Create a scatterplot for diversity evenness and abundance

Description

scatterVDJ creates a scatterplot that shows the abundance of the sample on the x-axis and the evenness on the y-axis.

Usage

scatterVDJ(d, ...)

## S4 method for signature 'matrix'
scatterVDJ(d, sampleGroups = NULL, title = NULL, legend = FALSE)

Arguments

d	A matrix created with calculateDiversity. The matrix must include nClonotypes and normentropy.
...	additional arguments.
sampleGroups	A matrix or data.frame that identifies the groups that each sample belongs to. The matrix must contain two columns. The first column lists the individual samples and should be called "Sample". The second column should list the group that each sample belongs to (e.g. Normal and Tumor) and be called "Group". If no sampleGroups dataset is provided, all of the samples will be plotted in the same color.
title	Character vector with an optional title.
legend	If TRUE, a legend will be included with the plot. If FALSE, no legend is included in the plot.

Value

Returns a ggplot plot with a scatterplot that shows the abundance for each sample on the x-axis and the evenness for each sample on the y-axis. Richness can be expressed as the total number of unique clonotypes in the sample or as the breakaway diversity measure (Willis and Bunge 2015), which estimates the total number of unique clonotypes in the population. Evenness is measured as the normalized entropy, which is a measure of how evenly cells are distributed across the different clonotypes. Evenness is a measure between 0 and 1 that is independent of the number of cells in a sample. Diversity measures such as Shannon entropy contain information about both the evenness and the abundance of a sample, but because both characteristics are combined into one number, comparison between samples or groups of samples is difficult. Other measures, such as the breakaway measure of diversity, only express the abundance of the sample and not the evenness. The scatterplot shows how evenness and abundance differs between each sample and between each group of samples.

Examples

data('contigs')
x <- clonoStats(contigs)
d <- calculateDiversity(x)
sampleGroups <- data.frame(Sample = c("sample1", "sample2"), 
                           Group = c("Cancer", "Normal"))
scatterVDJ(d, sampleGroups = NULL, 
       title = "Evenness-abundance plot", legend = TRUE)

kstreet13/VDJdive documentation built on May 31, 2024, 1:26 p.m.