micropan (version 1.2)

panMatrix: Computing the pan-matrix for a set of gene clusters

Description

A pan-matrix has one row for each genome and one column for each gene cluster, and cell [i,j] indicates how many members genome i has in gene family j.

Usage

panMatrix(clustering)

Arguments

clustering

A vector of integers indicating the gene cluster for every sequence. Sequences with the same number belong to the same cluster. The name of each element is the tag identifying the sequence.

Value

The returned object belongs to the class Panmat, which is a small (S3) extension to a matrix. It can be treated as a matrix, but the generic functions plot.Panmat and summary.Panmat are defined for a Panmat object. The input vector clustering is attached as the attribute clustering to the Panmat object.

Details

The pan-matrix is a central data structure for pan-genomic analysis. It is a matrix with one row for each genome in the study, and one column for each gene cluster. Cell [i,j] contains an integer indicating how many members genome i has in cluster j.

The input clustering must be an integer vector with one element for each sequence in the study, typically produced by either bClust or dClust. The name of each element is a text identifying every sequence. The value of each element indicates the cluster, i.e. those sequences with identical values are in the same cluster. IMPORTANT: The name of each sequence must contain the GID-tag for each genome, i.e. they must of the form GID111_seq1, GID111_seq2,... where the GIDxxx part indicates which genome the sequence belongs to. See panPrep for details.

The rows of the pan-matrix is named by the GID-tag for every genome. The columns are just named Cluster_x where x is an integer copied from clustering.

See Also

bClust, dClust, distManhattan, distJaccard, fluidity, chao, binomixEstimate, heaps, rarefaction.

Examples

# NOT RUN {
# Loading clustering data in the micropan package
data(list=c("Mpneumoniae.blast.clustering","Mpneumoniae.domain.clustering"),package="micropan")

# Pan-matrix based on BLAST clustering
panmat.blast <- panMatrix(Mpneumoniae.blast.clustering)

# Pan-matrix based on domain sequence clustering
panmat.domains <- panMatrix(Mpneumoniae.domain.clustering)

# Plotting the first pan-matrix, and then printing its summary
plot(panmat.blast)
summary(panmat.blast)

# }