linda.plot: Plot LinDA Results

In MicrobiomeStat: Statistical Methods for Microbiome Compositional Data

Plot LinDA Results

Description

The function produces the effect size plot of the differential features and volcano plot based on the output from linda.

Usage

linda.plot(
  linda.obj,
  variables.plot,
  titles = NULL,
  alpha = 0.05,
  lfc.cut = 1,
  legend = FALSE,
  directory = NULL,
  width = 11,
  height = 8
)

Arguments

linda.obj	return from function linda.
variables.plot	vector; variables whose results are to be plotted. For example, suppose the return value variables is equal to ('x1', 'x2', 'x3b', 'x3c', 'x1:x2'), then one could set variables.plot = c('x3b', 'x1:x2').
titles	vector; titles of the effect size plot and volcano plot for each variable in variables.plot. Default is NULL. If NULL, the titles will be set as variables.plot.
alpha	a numerical value between 0 and 1; cutoff for padj.
lfc.cut	a positive numerical value; cutoff for log2FoldChange.
legend	TRUE or FALSE; whether to show the legends of the effect size plot and volcano plot.
directory	character; the directory to save the figures, e.g., getwd(). Default is NULL. If NULL, figures will not be saved.
width	the width of the graphics region in inches. See R function pdf.
height	the height of the graphics region in inches. See R function pdf.

Value

A list of ggplot2 objects.

plot.lfc	a list of effect size plots. Each plot corresponds to one variable in variables.plot.
plot.volcano	a list of volcano plots. Each plot corresponds to one variable in variables.plot.

Author(s)

Huijuan Zhou, Jun Chen, Xianyang Zhang

References

Zhou, H., He, K., Chen, J., Zhang, X. (2022). LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome biology, 23(1), 95.

Examples

data(smokers)
ind <- smokers$meta$AIRWAYSITE == 'Throat' & smokers$meta$SIDEOFBODY == 'Left'
otu.tab <- as.data.frame(smokers$otu[, ind])
depth <- colSums(otu.tab)
meta <- cbind.data.frame(Smoke = factor(smokers$meta$SMOKER[ind]),
                         Sex = factor(smokers$meta$SEX[ind]))
                         
ind  <- depth >= 1000
linda.obj <- linda(otu.tab[, ind], meta[ind, ], formula = '~Smoke+Sex',
           feature.dat.type = 'count', 
           prev.filter = 0.1, is.winsor = TRUE, outlier.pct = 0.03,
           p.adj.method = "BH", alpha = 0.1)
           
linda.plot(linda.obj, c('Smokey', 'Sexmale'),
           titles = c('Smoke: n v.s. y', 'Sex: female v.s. male'), 
           alpha = 0.1, lfc.cut = 1, legend = TRUE, directory = NULL,
            width = 11, height = 8)

MicrobiomeStat documentation built on May 29, 2024, 11:38 a.m.