linda.plot {MicrobiomeStat}

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)