funbarRF (version 1.0.2)

WarcupRDS: Warcup training dataset which is trained with funbarRF.

Description

The RDP Warcup ITS training set was retrieved from https://rdp.cme.msu.edu/classifier/classifier.jsp. The collected dataset comprises 17878 sequences belonging to 8551 species. After removing the 2262 singletons, a final dataset comprising 15616 sequences belonging to 6289 species was prepared.

Usage

data (WarcupRDS)

Details

This dataset can be used to train the Random Forest prediction model in a local server after installing the funbarRF package, which can be subsequently used for prediction of the species labels for unknown specimen. For predicting the species labels of unknown specimen, see examples section.

References

1. Deshpande V., Wang Q., Greenfield P., Charleston M., Porras-Alfaro A., Kuske C.R., Cole J.R., Midgley D.J., and Tran-Dinh N. (2016) .Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia. 108(1): 1-5.

Examples

# NOT RUN {
#Prepararing the trained model.
data (WarcupRDS) # Loading Warcup ITS training dataset.
trs <- WarcupRDS # Reading Warcup dataset into R.
tr<- trs[1:100]
en_tr <- encGPC (tr) # Encoding of Warcup dataset with gap-pair compositional features.
y1 <- as.factor (rownames(en_tr)) # preparing response vector.
x1 <- en_tr # Preparing predictors.
library(randomForest) # Install the "randomForest" package from CRAN.
ff <- randomForest (y=y1, x=x1, mtry=10, ntree=500) 
# Training with random forest technique. User has to use sufficient number of ntree.
#Preparing the test set.
data (fun_dat)
ms <- read_seq_txt (fun_dat$seq)[1:2] #test/query sequences.
res_enc <- encGPC (ms) #encoding of the query sequences with gap-pair compositionsl features.
#Prediction of species labels for the test set.
test_res <- predict (ff, res_enc, type="response") #prediction of labels for the query sequences.
print (test_res) #priniting the predicted labels.
# }