dcGOR (version 1.0.6)

dcNaivePredict: Function to perform naive prediction from input known annotations

Description

dcNaivePredict is supposed to perform naive prediction from input known annotations. For each gene/protein, a term to be predicted are simply the frequency of that term appearing in the known annotations.

Usage

dcNaivePredict(data, GSP.file, output.file = NULL, ontology = c(NA,
"GOBP",
"GOMF", "GOCC", "DO", "HPPA", "HPMI", "HPON", "MP", "EC", "KW", "UP"),
max.num = 1000, verbose = T, RData.ontology.customised = NULL,
RData.location =
"https://github.com/hfang-bristol/RDataCentre/blob/master/dcGOR")

Arguments

data

an input vector containing genes/proteins to be predicted

GSP.file

a Glod Standard Positive (GSP) file containing known annotations between proteins/genes and ontology terms. For example, a file containing annotations between human genes and HP terms can be found in http://dcgor.r-forge.r-project.org/data/Algo/HP_anno.txt. As seen in this example, the input file must contain the header (in the first row) and two columns: 1st column for 'SeqID' (actually these IDs can be anything), 2nd column for 'termID' (HP terms). Alternatively, the GSP.file can be a matrix or data frame, assuming that GSP file has been read. Note: the file should use the tab delimiter as the field separator between columns

output.file

an output file containing predicted results. If not NULL, a tab-delimited text file will be also written out; otherwise, there is no output file (by default)

ontology

the ontology identity. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPON" for Human Phenotype ONset and clinical course, "MP" for Mammalian Phenotype, "EC" for Enzyme Commission, "KW" for UniProtKB KeyWords, "UP" for UniProtKB UniPathway. For details on the eligibility for pairs of input domain and ontology, please refer to the online Documentations at http://supfam.org/dcGOR/docs.html. If NA, then the user has to input a customised RData-formatted file (see RData.ontology.customised below)

max.num

an integer to specify how many terms will be predicted for each gene/protein

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to TRUE for display

RData.ontology.customised

a file name for RData-formatted file containing an object of S4 class 'Onto' (i.g. ontology). By default, it is NULL. It is only needed when the user wants to perform customised analysis using their own ontology. See dcBuildOnto for how to creat this object

RData.location

the characters to tell the location of built-in RData files. See dcRDataLoader for details

Value

a data frame containing three columns: 1st column the same as the input file (e.g. 'SeqID'), 2nd for 'Term' (predicted ontology terms), 3rd for 'Score' (along with predicted scores)

See Also

dcRDataLoader, dcAlgoPropagate

Examples

## Not run: 
# # 1) prepare genes to be predicted
# input.file <-
# "http://dcgor.r-forge.r-project.org/data/Algo/HP_anno.txt"
# #input.file <- "http://dcgor.r-forge.r-project.org/data/Algo/SCOP_architecture.txt"
# input <- utils::read.delim(input.file, header=TRUE, sep="\t",
# colClasses="character")
# data <- unique(input[,1])
# 
# # 2) do naive prediction
# GSP.file <- "http://dcgor.r-forge.r-project.org/data/Algo/HP_anno.txt"
# res <- dcNaivePredict(data=data, GSP.file=GSP.file, ontology="HPPA")
# res[1:10,]
# 
# # 3) calculate Precision and Recall
# res_PR <- dcAlgoPredictPR(GSP.file=GSP.file, prediction.file=res,
# ontology="HPPA")
# res_PR
# 
# # 4) plot PR-curve
# plot(res_PR[,2], res_PR[,1], xlim=c(0,1), ylim=c(0,1), type="b",
# xlab="Recall", ylab="Precision")
# ## End(Not run)