Package 'rRDP'

Calculate Classification Accuracy

Description

Calculate the classification accuracy at a given phylogenetic level.

Usage

accuracy(actual, predicted, rank)

confusionTable(actual, predicted, rank)

Arguments

actual	data.frame with the actual classification hierarchy.
predicted	data.frame with the predicted classification hierarchy.
rank	rank at which the accuracy should be evaluated.

Value

The accuracy or a confusion table.

Examples

seq <- readRNAStringSet(system.file("examples/RNA_example.fasta",
    package = "rRDP"
))

### decode the actual classification
actual <- decode_Greengenes(names(seq))

### use RDP to predict the classification
pred <- predict(rdp(), seq)

### calculate accuracy
confusionTable(actual, pred, "genus")
accuracy(actual, pred, "genus")

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Decoding and Encoding Phylogenetic Classification Annotations

Description

Functions to represent, decode and encode phylogenetic classification annotations used in FASTA files by RDP and the Greengenes project.

Usage

decode_Greengenes(annotation)

GenClass16S(
  Kingdom = NA,
  Phylum = NA,
  Class = NA,
  Order = NA,
  Family = NA,
  Genus = NA,
  Species = NA,
  Otu = NA,
  Org_name = NA,
  Id = NA
)

encode_Greengenes(classification)

decode_RDP(annotation)

encode_RDP(classification)

Arguments

annotation	Annotation from a FASTA file containing the classification information.
Kingdom	Name of the kingdom to which the organism belongs.
Phylum	Name of the phylum to which the organism belongs.
Class	Name of the class to which the organism belongs.
Order	Name of the order to which the organism belongs.
Family	Name of the family to which the organism belongs.
Genus	Name of the genus to which the organism belongs.
Species	Name of the species to which the organism belongs.
Otu	Name of the otu to which the organism belongs.
Org_name	Name of the organism.
Id	ID of the sequence.
classification	A data.frame created with GenClass16S() with the classification information.

Value

GenClass16S() and decodeX() return a data.frame. encodeX() returns a string with the corresponding annotation.

Examples

seq <- readRNAStringSet(system.file("examples/RNA_example.fasta",
    package = "rRDP"
))

### the FASTA annotation is read as names. This data has a Greengenes format
### annotation
names(seq)

classification <- decode_Greengenes(names(seq))
classification

### look at the Genus of all sequences
classification[, "Genus"]

### to train the RDP classifier, the annotations need to be in RDP format
annotation <- encode_RDP(classification)
names(seq) <- annotation
seq

### now we can train the classifier
customRDP <- trainRDP(seq)
customRDP

## clean up
removeRDP(customRDP)

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Ribosomal Database Project (RDP) Classifier for 16S rRNA

Description

Use the RDP classifier (Wang et al, 2007) to classify 16S rRNA sequences. This package contains currently RDP version 2.14 released in August 2023. The associated data package rRDPData contains models trained on the bacterial and archaeal taxonomy training set No. 19 (see Wang and Cole, 2024).

Usage

rdp(dir = NULL)

## S3 method for class 'RDPClassifier'
predict(object, newdata, confidence = 0.8, rdp_args = "", verbose = FALSE, ...)

trainRDP(x, dir = "classifier", rank = "genus", verbose = FALSE)

removeRDP(object)

Arguments

dir	directory where the classifier information is stored.
object	a RDPClassifier object.
newdata	new data to be classified as a Biostrings::DNAStringSet.
confidence	numeric; minimum confidence level for classification. Results with lower confidence are replaced by NAs. Set to 0 to disable.
rdp_args	additional RDP arguments for classification (e.g., "-minWords 5" to set the minimum number of words for each bootstrap trial.). See RDP documentation.
verbose	logical; print additional information.
...	additional arguments (currently unused).
x	an object of class Biostrings::DNAStringSet with the 16S rRNA sequences for training.
rank	Taxonomic rank at which the classification is learned.

Details

RDP is a naive Bayes classifier using 8-mers as features.

rdp() creates a default classifier trained with the data shipped with RDP. Alternatively, a directory with the data for an existing classifier (created with trainRDP()) can be supplied.

trainRDP() creates a new classifier for the data in x and stores the classifier information in dir. The data in x needs to have annotations in the following format:

"<ID> <Kingdom>;<Phylum>;<Class>;<Order>;<Family>;<Genus>"

A created classifier can be removed with removeRDP(). This will remove the directory which stores the classifier information.

The data for the default 16S rRNA classifier can be found in package rRDPData.

Value

rdp() and trainRDP() return a RDPClassifier object.

predict() returns a data.frame containing the classification results for each sequence (rows). The data.frame has an attribute called "confidence" with a matrix containing the confidence values.

References

Hahsler M, Nagar A (2020). "rRDP: Interface to the RDP Classifier." R Package, Bioconductor. doi:10.18129/B9.bioc.rRDP.

RDP classifier software: https://sourceforge.net/projects/rdp-classifier/

Qiong Wang, George M. Garrity, James M. Tiedje and James R. Cole. Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy, Appl. Environ. Microbiol. August 2007 vol. 73 no. 16 5261-5267. doi:10.1128/AEM.00062-07

Qiong W. and Cole J.R. Updated RDP taxonomy and RDP Classifier for more accurate taxonomic classification, Microbial Ecology, Announcement, 4 March 2024. doi:10.1128/mra.01063-23

Examples

### Use the default classifier
seq <- readRNAStringSet(system.file("examples/RNA_example.fasta",
    package = "rRDP"
))

## shorten names
names(seq) <- sapply(strsplit(names(seq), " "), "[", 1)
seq

## use rdp for classification (this needs package rRDPData installed)
## > BiocManager::install("rRDPData")

cl_16S <- rdp()
cl_16S

pred <- predict(cl_16S, seq)
pred

attr(pred, "confidence")

### Train a custom RDP classifier on new data
trainingSequences <- readDNAStringSet(
    system.file("examples/trainingSequences.fasta", package = "rRDP")
)

customRDP <- trainRDP(trainingSequences)
customRDP

testSequences <- readDNAStringSet(
    system.file("examples/testSequences.fasta", package = "rRDP")
)
predict(customRDP, testSequences)

## clean up
removeRDP(customRDP)

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