View source: R/ggrasp.create.R
ggrasp.load | R Documentation |
ggrasp.load() initializes a class GGRaSP object from a file containing either a tree, a distance matrix or a multi-fasta alignment. The returned object can subsequently be clustered using ggrasp.cluster().
ggrasp.load(file, file.format, rank.file, offset, tree.method = "complete")
file |
File containing the tree, matrix or sequence alignment used to initialize the ggrasp object. Required. |
file.format |
The format the file is in, with tree, fasta and matrix accepted. If not given the program makes a guess. |
rank.file |
File containing the ranks of genomes in a tab-delineated file with the genome in column 1 and the rank in column 2. The rank is a non-negative number. |
offset |
Numeric representing a perfect match. Default is 0. |
tree.method |
The method used to make the tree from a distance matrix. "Complete" (Default), "Average", "Single", and "nj" (Neighbor Joining) are currently available. |
Returns a class GGRaSP variable
#The following data is from Chavda et al 2016 which phylotyped Enterobacter genomes # Our example uses the data underpinning the tree shown in Figure 2 # Also included is a ranking file to prioritize closed Enterobactor genomes library(ggrasp); tree.file <- system.file("extdata", "Enter.kSNP.tree", package="ggrasp") rank.file.in <- system.file("extdata", "Enter.kSNP.ranks", package="ggrasp") Enter.tree <- ggrasp.load(tree.file, file.format = "tree", rank.file = rank.file.in); # Other options include loading by fasta file: fasta.file <- system.file("extdata", "Enter.kSNP2.fasta", package="ggrasp") rank.file.in <- system.file("extdata", "Enter.kSNP.ranks", package="ggrasp") Enter.tree <- ggrasp.load(fasta.file, file.format = "fasta", rank.file =rank.file.in) # and by distance matrix. Since this distance matrix is actually percent identity, # we will us an offset of 100 mat.file <- system.file("extdata", "Enter.ANI.mat", package="ggrasp") rank.file.in <- system.file("extdata", "Enter.kSNP.ranks", package="ggrasp") Enter.in <- ggrasp.load(mat.file, file.format = "matrix", rank.file =rank.file.in, offset = 100) # Use summary() to examine the data loaded summary(Enter.in) #Use plot() to see the tree plot(Enter.in)
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