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pca_in_one

Source: vignettes/pca_in_one.Rmd
pca_in_one.Rmd

The main function of this method is to perform principal component analysis (PCA) and output the results of the transformation along with some simple plots.

Feature Value Matrix

data is the feature value matrix in data frame format, where rows represent samples and columns represent feature values. It should only contain numeric feature values. Additionally, row names need to be provided to facilitate merging with the sample data frame for plotting purposes.

#   Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1          5.1         3.5          1.4         0.2
# 2          4.9         3.0          1.4         0.2
# 3          4.7         3.2          1.3         0.2
# 4          4.6         3.1          1.5         0.2
# 5          5.0         3.6          1.4         0.2
# 6          5.4         3.9          1.7         0.4

Sample Information Table

sample is the sample information matrix, where the first column contains the sample names. The default column name is sample, and there are no specific requirements for the names of the other columns.

#   sample species
# 1      1  setosa
# 2      2  setosa
# 3      3  setosa
# 4      4  setosa
# 5      5  setosa
# 6      6  setosa

Run PCA 

# devtools::install_github("lixiang117423/biohelpers")

library(tidyverse)
library(biohelpers)

data <- iris[, 1:4]

sample <- iris$Species %>%
  as.data.frame() %>%
  rownames_to_column(var = "sample") %>%
  set_names(c("sample", "species"))

pca_in_one(data, sample) -> result.pca

The output is a list containing:

  • result.pca: The output from FactoMineR::PCA, which can be called directly.
  • plot.pca: The plotting results, defaulting to PC1 and PC2. The output is a ggplot object, which can be fine-tuned using ggplot2.
  • point.data: The data used for plotting, which users can call for their own plots or export for use in other software.
  • eigenvalue.pca: The explained variance of the principal components, starting from PC1 by default.