Expression Methods

featurePlot

R Documentation

Plot association between abundance of a feature and predictor

Description

Plot association between abundance of a feature and predictor, modified if paired and covars are available

Usage

featurePlot(
  data,
  predictor,
  paired = NULL,
  covars = NULL,
  feature = NULL,
  relative = TRUE,
  logScale = FALSE,
  delta = 0.001,
  covar.quant = c(0, 1/3, 2/3, 1)
)

Arguments

`data`	Either a matrix with counts/abundances, OR a `phyloseq` object. If a matrix/data.frame is provided rows should be taxa/genes/proteins and columns samples, and there should be rownames
`predictor`	The predictor of interest. Either a Factor or Numeric, OR if `data` is a `phyloseq` object the name of the variable in `sample_data(data)` in quotation.
`paired`	For paired/blocked experimental designs. Either a Factor with Subject/Block ID for running paired/blocked analysis, OR if `data` is a `phyloseq` object the name of the variable in `sample_data` in quotation.
`covars`	Either a named list with covariates, OR if data is a phyloseq object a character vector with names of the variables in `sample_data(data)`
`feature`	Name of feature to plot. Should be in rownames of `data` (or `taxa_names(data)` if `data` is a `phyloseq` object)
`relative`	Logical. If TRUE (default) abundances are made relative
`logScale`	Logical. Should abundances be log10-scaled? After normalization if relative is TRUE. Default FALSE
`delta`	Pseudocount for log10 normalization
`covar.quant`	Quantiles for cutting quantitative `covars`

Details

Boxplot for categorical variables, points and smooth line for quantitative variable. If a paired variable is supplied, it is always plotted as points with lines grouped by the paired variable If covars are supplied data is split in facets. Quantitative covars are cut in intervals according to the quantiles given in covar.quant

Value

A ggplot

Examples

# Create random count_table and predictor
set.seed(5)
mat <- matrix(rnbinom(500, size = 0.1, mu = 500), nrow = 50, ncol = 10)
pred <- c(rep("Control", 5), rep("Treatment", 5))
rownames(mat) <- 1:50

featurePlot(mat, pred, feature = "5")

Russel88/DAtest documentation built on March 24, 2022, 3:50 p.m.