pca_3d: Create 3d PCA plots

In tidyMicro: A Pipeline for Microbiome Analysis and Visualization

Description

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

Usage

pca_3d(
  micro_set,
  table,
  time_var,
  subject,
  y = clr,
  dist_method = "euclidean",
  type = "PCoA",
  plot_scores = FALSE,
  pch = 16,
  cex.axis = 1,
  cex.lab = 1,
  cex = 1,
  main = NULL,
  subtitle = NULL,
  scalewt = TRUE,
  print.legend = TRUE,
  legend.title = "Time Points",
  legend.position = "right"
)

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
y	Value to calculate principle components or coordinates on. Default is centered log ratio (recommended)
dist_method	Dissimilartiy method to be calculated by vegdist. Euclidean by default
type	"PCA" for principle components or "PCoA" to calculated dissimilarity matrix using vegdist
plot_scores	Plot the scores instead of the principle components
pch	Plotting "character", i.e. symbol to use.
cex.axis	Options for scatterplot3d
cex.lab	Options for scatterplot3d
cex	Options for scatterplot3d
main	Plot title
subtitle	Plot subtitle
scalewt	Logical; center and scale OTU table, recommended
print.legend	Logical; print plot legend
legend.title	Title for plot legend. Ignored if print.legend = FALSE
legend.position	'x' argument in legend

Details

Requires that you have separate columns for subject ID and time point. Data must be complete across time points. The function will automatically filter out incomplete cases with a warning message.

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

Author(s)

Charlie Carpenter, Kayla Williamson

References

vegdist

Examples

data(bpd_phy); data(bpd_cla); data(bpd_ord); data(bpd_fam); data(bpd_clin)
otu_tabs = list(Phylum = bpd_phy, Class = bpd_cla,
Order = bpd_ord, Family = bpd_fam)

set <- tidy_micro(otu_tabs = otu_tabs, clinical = bpd_clin)

set %>% pca_3d(table = "Family", time_var = day, subject = study_id, legend.title = "Day")

tidyMicro documentation built on Jan. 13, 2021, 6:18 a.m.