| panMatrix {micropan} | R Documentation |
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 named vector of integers. |
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 a named 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 ‘genome_id’ 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 ‘genome_id’ for every genome. The columns are just named ‘Cluster_x’ where ‘x’ is an integer copied from ‘clustering’.
Value
An integer matrix with a row for each genome and a column for each sequence cluster. The input vector ‘clustering’ is attached as the attribute ‘clustering’.
Author(s)
Lars Snipen and Kristian Hovde Liland.
See Also
bClust, dClust, distManhattan,
distJaccard, fluidity, chao,
binomixEstimate, heaps, rarefaction.
Examples
# Loading clustering data in this package
data(xmpl.bclst)
# Pan-matrix based on the clustering
panmat <- panMatrix(xmpl.bclst)
## Not run:
# Plotting cluster distribution
library(ggplot2)
tibble(Clusters = as.integer(table(factor(colSums(panmat > 0), levels = 1:nrow(panmat)))),
Genomes = 1:nrow(panmat)) %>%
ggplot(aes(x = Genomes, y = Clusters)) +
geom_col()
## End(Not run)