genBart (version 1.0.1)

clusterData: Hierarchical clustering of normalized expression data

Description

Perform hierarchical clustering on normalized data

Usage

clusterData(norm.data, dist.method = "euclidean", agg.method = "complete")

Arguments

norm.data

list of normalized expression data returned by normalizeData

dist.method

The distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". See dist for more details.

agg.method

The agglomeration method to be used. This must be one of "single", "complete", "average", "mcquitty", "ward.D", "ward.D2", "centroid" or "median". hclust for more details.

Value

rowdend1b dendrogram from hierarchical clustering of genes on baseline samples normalized according to norm.method specified in norm.data. NULL if y1b in norm.data is NULL.

rowdend2b dendrogram from hierarchical clustering of genes on baseline samples normalized to controls according to norm.method specified in norm.data. NULL if y2b in norm.data is NULL.

rowdend1 dendrogram from hierarchical clustering of genes on all samples normalized according to norm.method specified in norm.data. NULL if y1 in norm.data is NULL.

rowdend2 dendrogram from hierarchical clustering of genes on all samples normalized to controls according to norm.method specified in norm.data. NULL if y2 in norm.data is NULL.

rowdend3 dendrogram from hierarchical clustering of genes on all samples normalized to their baseline. NULL if y3 in norm.data is NULL.

norm.method string describing the normalization method used in normalizeData

Details

This function performs hierarchical clustering on the rows of the normalized expression data contained in norm.data.

Examples

# NOT RUN {
# Example data
data(tb.expr)
data(tb.design)

# Use first 100 probes to demonstrate
dat <- tb.expr[1:100,]

# Create desInfo object
meta.data <- metaData(y = dat, design = tb.design, data.type = "microarray", 
                    columnname = "columnname", long = TRUE, sample.id = "sample_id",
                    subject.id = "monkey_id", time.var = "timepoint",
                    baseline.var = "timepoint", baseline.val = 0)

# Normalize data
data.norm <- normalizeData(meta = meta.data)

# Cluster data
dendros <- clusterData(norm.data = data.norm)
# }