find_outlier_samples: Identification of Potential Outlier Samples in Gene...

In IOBR/IOBR: Immune Oncology Biological Research

Identification of Potential Outlier Samples in Gene Expression Data

Description

The "find_outlier_samples" function is designed to analyze gene expression data and identify potential outlier samples based on connectivity analysis. By utilizing the "WGCNA" package, this function calculates the normalized adjacency and connectivity z-scores for each sample. It further generates a connectivity plot, highlighting samples with connectivity z-scores greater than the specified y-intercept value. This function also allows for the option to plot hierarchical clustering and save the output files in a designated project folder. The returned result is a list of potential outlier samples, providing valuable insights for further analysis and data interpretation.

Usage

find_outlier_samples(
  eset,
  yinter = -3,
  project = "find_outlier_eset",
  plot_hculst = FALSE,
  show_plot = TRUE,
  index = NULL
)

Arguments

eset	A gene expression matrix data. It is the input data on which the function will operate.
yinter	A numeric value representing the y-intercept for the horizontal line on the connectivity plot. It is used to identify potential outliers in the data.
project	A string indicating the project name associated with the analysis. It is used to create a folder for saving the output files.
plot_hculst	A logical value indicating whether to plot the hierarchical clustering of samples. If set to TRUE, the hierarchical clustering plot will be generated.
show_plot	A logical value indicating whether to display the connectivity plot. If set to TRUE, the connectivity plot will be shown.
index	default is null.

Value

A vector of character strings representing the names of potential outlier samples identified based on the connectivity analysis. These samples have connectivity z-scores greater than the absolute value of yinter.

Author(s)

Dongqiang Zeng

Examples

# loading expression data
data("eset_tme_stad", package = "IOBR")
outs <- find_outlier_samples(eset = eset_tme_stad)
print(outs)

IOBR/IOBR documentation built on May 5, 2024, 2:34 p.m.