plotOTU: Basic plot function of the raw or normalized data.

In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Description

This function plots the abundance of a particular OTU by class. The function uses the estimated posterior probabilities to make technical zeros transparent.

Usage

plotOTU(
  obj,
  otu,
  classIndex,
  log = TRUE,
  norm = TRUE,
  jitter.factor = 1,
  pch = 21,
  labs = TRUE,
  xlab = NULL,
  ylab = NULL,
  jitter = TRUE,
  ...
)

Arguments

obj	A MRexperiment object with count data.
otu	The row number/OTU to plot.
classIndex	A list of the samples in their respective groups.
log	Whether or not to log2 transform the counts - if MRexperiment object.
norm	Whether or not to normalize the counts - if MRexperiment object.
jitter.factor	Factor value for jitter.
pch	Standard pch value for the plot command.
labs	Whether to include group labels or not. (TRUE/FALSE)
xlab	xlabel for the plot.
ylab	ylabel for the plot.
jitter	Boolean to jitter the count data or not.
...	Additional plot arguments.

Value

Plotted values

See Also

cumNorm

Examples

data(mouseData)
classIndex=list(controls=which(pData(mouseData)$diet=="BK"))
classIndex$cases=which(pData(mouseData)$diet=="Western")
# you can specify whether or not to normalize, and to what level
plotOTU(mouseData,otu=9083,classIndex,norm=FALSE,main="9083 feature abundances")

metagenomeSeq documentation built on Nov. 8, 2020, 5:34 p.m.