micro_pca: Calculate and plot principle components

In tidyMicro: A Pipeline for Microbiome Analysis and Visualization

Description

Principle components are calculated on the centerted log ratio tranformation of the OTU table using the prcomp function from the stats package. Scaling the OTU table to a unit variance is the default option, and recommended, but this can be changed using scaled = F.

Usage

micro_pca(
  micro_set,
  table = NULL,
  dist = NULL,
  grp_var,
  y = clr,
  scale = TRUE,
  axes_arrows = F,
  ellipse = FALSE,
  ellipse.prob = 0.68,
  main = NULL,
  subtitle = NULL,
  legend_title = NULL
)

Arguments

micro_set	A tidy_micro data set
table	OTU table of interest
dist	A distance matrix, such as a beta diversity. If supplied a PCoA plot will be returned
grp_var	Categorical grouping variable
y	Value to calculate principle components or coordinates on. Default is centered log ratio (recommended)
scale	Logical. Indicating whether the variables should be scaled to have unit variance before the analysis takes place
axes_arrows	Logical. Plot component axes arrows
ellipse	Logical. Plot normal data ellipses by groups
ellipse.prob	Numeric.
main	Plot title
subtitle	Plot subtitle
legend_title	Legend title

Details

PCA calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. Calculations are accomplished through the prcomp function, and the plot is created through internal code based on the ggbiplot function https://github.com/vqv/ggbiplot.

Value

A ggplot you can add geoms to if you'd like

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Mardia, K. V., J. T. Kent, and J. M. Bibby (1979) Multivariate Analysis, London: Academic Press.

Venables, W. N. and B. D. Ripley (2002) Modern Applied Statistics with S, Springer-Verlag.

Vincent Q. Vu (2011). ggbiplot: A ggplot2 based biplot. https://github.com/vqv/ggbiplot

Examples

data(mrsa_gen); data(mrsa_clin)

set <- tidy_micro(otu_tabs = mrsa_gen, tab_names = "Genus", clinical = mrsa_clin)

## PCA Plot
set %>% micro_pca(table = "Genus", grp_var = Aureus_Positive)

## PCoA Plot (Recommended for p > n)

bray_beta <- set %>% beta_div(table = "Genus")
micro_pca(set, dist = bray_beta, grp_var = Aureus_Positive, ellipse = TRUE)

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