nanostringPCA: Plot PCA

In calebclass/NanoTube: An Easy Pipeline for NanoString nCounter Data Analysis

Plot PCA

Description

Conduct principal components analysis and plot the results, using either ggplot2 or plotly.

Usage

nanostringPCA(
  ns,
  pc1 = 1,
  pc2 = 2,
  interactive.plot = FALSE,
  exclude.zeros = TRUE,
  codeclass.retain = "endogenous"
)

Arguments

ns	Processed NanoString data
pc1	Principal component to plot on x-axis (default 1)
pc2	Principal component to plot on y-axis (default 2)
interactive.plot	Plot using plotly? Default FALSE (in which case ggplot2 is used)
exclude.zeros	Exclude genes that are not detected in all samples (default TRUE)
codeclass.retain	The CodeClasses to retain for principal components analysis.Generally we're interested in endogenous genes, so we keep "endogenous" only by default. Others can be included by entering a character vector for this option. Alternatively, all targets can be retained by setting this option to ".".

Value

A list containing:

pca	The PCA object
plt	The PCA plot

Examples

example_data <- system.file("extdata", "GSE117751_RAW", package = "NanoTube")
sample_data <- system.file("extdata", "GSE117751_sample_data.csv", 
                           package = "NanoTube")

# Process and normalize data first
dat <- processNanostringData(example_data, 
                             sampleTab = sample_data, 
                             groupCol = "Sample_Diagnosis",
                             normalization = "nSolver", 
                             bgType = "t.test", bgPVal = 0.01)
                               
# Interactive PCA using plotly                             
nanostringPCA(dat, interactive.plot = TRUE)$plt

# Static plot using ggplot2, for the 3rd and 4th PC's.
nanostringPCA(dat, pc1 = 3, pc2 = 4, interactive.plot = FALSE)$plt

calebclass/NanoTube documentation built on Nov. 21, 2023, 12:31 p.m.