geneUsage.distance: Dissimilarity/distance indices for gene usage data
In bcRep: Advanced Analysis of B Cell Receptor Repertoire Data

Description Usage Arguments Details Value Author(s) References See Also Examples

This function calculates Bray-Curtis, Jaccard or cosine indices for gene usage data of different samples.

1 2	geneUsage.distance(geneUsage.tab=NULL, names=NULL, method=c("bc","jaccard", "cosine"), cutoff=0)

`geneUsage.tab`	gene usage table with genes as columns and samples as rows
`names`	Names of samples (default: Sample1...n)
`method`	Distance/dissimilarity index to be used for calculation. On of Bray-Curtis (bc), Jaccard (jaccard) or cosine (cosine)
`cutoff`	Cutoff for gene proportions (default: 0; see details)

This function calculates dissmiliarity indices based on gene usage data of different samples (columns = genes, rows = samples). Bray-Curtis, Jaccard or cosine indices can be chosen.

For explanation of Bray-Curtis and Jaccard index see vegdist.

For explanation of cosine index see dist.

When using Jaccard index, a cutoff for gene proportions can be given. Proportions will be transformed into absence/presence data (<= cutoff; > cutoff) and afterwards Jaccard indices are calculated.

Output is a matrix containing dissimilarity/distance indices between samples, based on gene usages.

Julia Bischof

Bray, J. R. and J. T. Curtis. 1957. An ordination of upland forest communities of southern Wisconsin. Ecological Monographs 27:325-349.

Levandowsky, Michael; Winter, David (1971), "Distance between sets", Nature 234 (5): 34-35

Graham L. Giller (2012). "The Statistical Properties of Random Bitstreams and the Sampling Distribution of Cosine Similarity". Giller Investments Research Notes (20121024/1)

Jari Oksanen, F. Guillaume Blanchet, et al. (2015). vegan: Community Ecology Package. R package version 2.3-1. https://CRAN.R-project.org/package=vegan

David Meyer and Christian Buchta (2015). proxy: Distance and Similarity Measures. R package version 0.4-15. https://CRAN.R-project.org/package=proxy

dist.PCoA, plotDistPCoA, sequences.distance

1 2	data(vgenes) # VH gene proportions of 10 samples (rows) and 30 VH genes (columns) disttab<-geneUsage.distance(geneUsage.tab = vgenes, method = "bc")