clusterSample {TCC} | R Documentation

## Perform hierarchical clustering for samples from expression data

### 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)

[Package TCC version 1.28.0 Index]