Taxa {DECIPHER} | R Documentation |
Taxonomic classification is the process of assigning an organism a label that is part of a taxonomic hierarchy (e.g., Phylum, Class, Order, Family, Genus). Here, labels are assigned based on an organism's DNA or RNA sequence at a rank level determined by the classification's confidence. Class Taxa
provides objects and functions for storing and viewing training and testing objects used in taxonomic classification.
## S3 method for class 'Taxa' plot(x, y = NULL, showRanks = TRUE, n = NULL, ...) ## S3 method for class 'Taxa' print(x, ...) ## S3 method for class 'Taxa' x[i, j, threshold]
x |
An object of class |
y |
An (optional) object of class |
showRanks |
Logical specifying whether to show all rank levels when plotting an object of class |
n |
Numeric vector giving the frequency of each classification if |
... |
Other optional parameters. |
i |
Numeric or character vector of indices to extract from objects of class |
j |
Numeric or character vector of rank levels to extract from objects of class |
threshold |
Numeric specifying the confidence |
Objects of class Taxa
are stored as lists, and can have either subclass Train
or Test
. The function LearnTaxa
returns an object of subclass Train
, while the function IdTaxa
can return an object of class Test
.
Train
ing objects are built from a set of reference sequences with known taxonomic classifications. List elements contain information required by IdTaxa
for assigning a classification to test sequences.
Test
ing objects can be generated by IdTaxa
from a Train
ing object and a set of test sequences. Each list element contains the taxon, confidence, and (optionally) rank name of the taxonomic assignment.
The information stored in Taxa
can be visualized with the plot
function or displayed with print
. Only objects of subclass Train
can be subsetted without losing their class.
Erik Wright eswright@pitt.edu
data("TrainingSet_16S") plot(TrainingSet_16S) # import test sequences fas <- system.file("extdata", "Bacteria_175seqs.fas", package="DECIPHER") dna <- readDNAStringSet(fas) # remove any gaps in the sequences dna <- RemoveGaps(dna) # classify the test sequences ids <- IdTaxa(dna, TrainingSet_16S, strand="top") ids plot(ids) # plot all rank levels plot(ids[, 1:4]) # plot the first rank levels plot(ids[j=c("rootrank", "class", "genus")]) # plot specific rank levels plot(ids[threshold=70]) # plot high confidence classifications