title: treestructure analysis output: html_document params: date: !r Sys.Date() treefn: sim.nwk minCladeSize: 10 minOverlap: -Inf nsim: 1000 level: 0.01 ncpu: 1 verbosity: 1 debugLevel: 0 output: treestructure_output ...
Available options:
tre: A tree of type ape::phylo. Must be rooted and binary. minCladeSize: All clusters within partition must have at least this many tips. minOverlap: Threshold time overlap required to find splits in a clade nsim: Number of simulations for computing null distribution of test statistics level: Significance level for finding new split within a set of tips ncpu: If >1 will compute statistics in parallel using multiple CPUs verbosity: If > 0 will print information about progress of the algorithm debugLevel: If > 0 will produce additional data in return value
print( t( as.data.frame( params, row.names='Parameter value' )) )
suppressPackageStartupMessages ( library( treestructure ) ) tr <- read.tree( params$treefn ) ts <- trestruct( tr , minCladeSize = as.numeric( params$minCladeSize ) , minOverlap = as.numeric( params$minOverlap ) , nsim = as.numeric( params$nsim ) , level = as.numeric( params$level ) , ncpu = as.numeric( params$ncpu ) , verbosity = as.numeric( params$verbosity ) , debugLevel = as.numeric( params$debugLevel ) )
print(ts)
dir.create( params$output ) for ( k in 1:length( ts$clusterSets ) ){ x <- which( as.numeric(ts$clustering)==k) tr1 <- keep.tip( tr, x ) write.tree( tr1, file = paste( sep='/' , params$output, paste0('cluster' , k , '.nwk')) ) } for ( k in 1:length( ts$partitionSets ) ){ x <- which( as.numeric(ts$partition)==k) tr1 <- keep.tip( tr, x ) write.tree( tr1, file = paste( sep='/' , params$output, paste0('partition' , k , '.nwk')) ) } saveRDS(ts, paste(sep='/', params$output, 'treestructure.rds' ))
suppressPackageStartupMessages( plot(ts ) )
Cluster & partition assigment:
print( as.data.frame( ts ))
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