clusterSample: Perform hierarchical clustering for samples from expression...

In jqsunac/TCC: TCC: Differential expression analysis for tag count data with robust normalization strategies

Description

This function performs hierarchical clustering for samples (tissues or columns) from expression data.

Usage

clusterSample(data, dist.method = "spearman", hclust.method = "average",
              unique.pattern = TRUE)

Arguments

data	numeric matrix or data frame containing expression data (count data or microarray data), where each row indicates the gene (or transcript or probeset ID), each column indicates the sample (or library), and each cell indicates the expression value (i.e., number of counts or signal intensity) of the gene in the sample.
dist.method	character string specifying a type for correlation coefficient ("spearman" or "pearson") used as distance. The default is "spearman". The hierarchical clustering is performed using the distance (i.e., 1 - "spearman" correlation coefficient, by default).
hclust.method	character string specifying an agglomeration method used in hclust function: "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". The default is "average".
unique.pattern	logical. If FALSE, the input expression data are directly applied for clustering. If TRUE (default), the input data only having unique expression patterns are applied.)

Value

An object of class hclust which describes the tree produced by the clustering process. See hclust for details.

Examples

# Perform sample clustering with default options.
data(hypoData)
hc <- clusterSample(hypoData)
plot(hc)

# Obtain the same result using the 'unique.pattern = FALSE' option.
data(hypoData)
keep <- as.logical(rowSums(hypoData) > 0)
data <- unique(hypoData[keep, ])
hc <- clusterSample(data, unique.pattern = FALSE)
plot(hc)

jqsunac/TCC documentation built on March 20, 2021, 4:23 a.m.