pca_3d: Create 3d PCA plots

In CharlieCarpenter/tidy.micro: A Pipeline For Microbiome Analysis And Visualization

Description

Create three dimensional PCA plots from longitudinal microbiome data or multiple omics data sets.

Usage

pca_3d(
  micro_set,
  table,
  time_var,
  subject,
  modes = "AC",
  dist_method = "euclidean",
  type = "PCoA",
  plot_scores = F,
  n_compA,
  n_compB,
  n_compC,
  cex.axis = 1,
  cex.lab = 1,
  main = NULL,
  subtitle = NULL,
  scalewt = TRUE
)

Arguments

micro_set	A tidy_micro data set
table	OTU table of interest
time_var	The time point variable column name in your tidi_MIBI set
subject	The subject variable column name in your tidi_MIBI set
modes	Components of the data to focus on: time, subjects, bacteria, etc.
dist_method	Dissimilartiy method to be calculated by vegdist. Euclidean by default
type	Either "PCA" or "PCoA"
plot_scores	Plot the scores instead of the principle components
n_compA	The number of components along first axis. See details
n_compB	The number of components along second axis. See details
n_compC	The number of components along third axis. See details
cex.axis	Options for scatterplot3d
cex.lab	Options for scatterplot3d
main	Plot title
subtitle	Plot subtitle
scalewt	Logical; center and scale OTU table, recommended

Details

Requires that you have columns for subject name and time point. Data must be complete across time points. The function will filter out inconsistent subjects

When type = "PCoA" the component matrices must be specified prior to the optimization. This is handled automatically.

If n_compA, n_compB, and n_compC aren't specified they will default to the number of complete subjects, the number of taxa, and the number of time points, respectively. This slows down performance slightly, but will not change the results.

Author(s)

Charlie Carpenter, Kayla Williamson

References

help(vegdist)

Examples

data(phy); data(cla); data(ord); data(fam); data(met)

otu_tabs = list(Phylum = phy, Class = cla, Order = ord, Family = fam)
set <- tidy_micro(otu_tabs = otu_tabs, meta = met)

set %>% pca_3d(table = "Family", time_var = day, subject = study_id)

CharlieCarpenter/tidy.micro documentation built on Jan. 19, 2020, 6:28 p.m.