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. By default, it remotely locates at "https://github.com/hfang-bristol/RDataCentre/blob/master/dcGOR" and "http://dcgor.r-forge.r-project.org/data". For the user equipped with fast internet connection, this option can be just left as default. But it is always advisable to download these files locally. Especially when the user needs to run this function many times, there is no need to ask the function to remotely download every time (also it will unnecessarily increase the runtime). For examples, these files (as a whole or part of them) can be first downloaded into your current working directory, and then set this option as: RData.location=".". If RData to load is already part of package itself, this parameter can be ignored (since this function will try to load it via function data first). Here is the UNIX command for downloading all RData files (preserving the directory structure): wget -r -l2 -A "*.RData" -np -nH --cut-dirs=0 "http://dcgor.r-forge.r-project.org/data"

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)

Note

When 'output.file' is specified, a tab-delimited text file is written out, with the column names: 1st column the same as the input file (e.g. 'SeqID'), 2nd for 'Term' (predicted ontology terms), 3rd for 'Score' (along with predicted scores).

Examples

# 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")

Start at 2015-07-23 12:56:25

First, load the ontology 'HPPA' (2015-07-23 12:56:25) ...
'onto.HPPA' (from package 'dcGOR' version 1.0.5) has been loaded into the working environment
Second, import files for GSP (2015-07-23 12:56:25) ...
Third, propagate GSP annotations (2015-07-23 12:56:26) ...
	At level 16, there are 2 nodes, and 5 incoming neighbors.
	At level 15, there are 7 nodes, and 9 incoming neighbors.
	At level 14, there are 21 nodes, and 42 incoming neighbors.
	At level 13, there are 54 nodes, and 82 incoming neighbors.
	At level 12, there are 105 nodes, and 105 incoming neighbors.
	At level 11, there are 274 nodes, and 188 incoming neighbors.
	At level 10, there are 463 nodes, and 294 incoming neighbors.
	At level 9, there are 782 nodes, and 441 incoming neighbors.
	At level 8, there are 1004 nodes, and 538 incoming neighbors.
	At level 7, there are 1182 nodes, and 581 incoming neighbors.
	At level 6, there are 1295 nodes, and 527 incoming neighbors.
	At level 5, there are 940 nodes, and 290 incoming neighbors.
	At level 4, there are 408 nodes, and 99 incoming neighbors.
	At level 3, there are 114 nodes, and 21 incoming neighbors.
	At level 2, there are 21 nodes, and 1 incoming neighbors.
	At level 1, there are 1 nodes, and 0 incoming neighbors.
	There are 6673 terms in GSP (2015-07-23 12:57:15).
Fourth, do naive predictions for 3085 genes/proteins (2015-07-23 12:57:15) ...
	Focus on top 1000 predicted terms for each gene/protein

End at 2015-07-23 12:57:18
Runtime in total is: 53 secs

res[1:10,]

      SeqID    Term         Score   
 [1,] "10225"  "HP:0000118" "1"     
 [2,] "10806"  "HP:0000707" "0.6591"
 [3,] "11020"  "HP:0012638" "0.6053"
 [4,] "1131"   "HP:0000924" "0.5299"
 [5,] "123016" "HP:0000152" "0.5217"
 [6,] "129880" "HP:0011842" "0.5171"
 [7,] "1312"   "HP:0000234" "0.5138"
 [8,] "1376"   "HP:0000478" "0.5098"
 [9,] "139285" "HP:0012639" "0.4675"
[10,] "145173" "HP:0000271" "0.4583"


# 3) calculate Precision and Recall
res_PR <- dcAlgoPredictPR(GSP.file=GSP.file, prediction.file=res,
ontology="HPPA")

Start at 2015-07-23 12:57:18

First, load the ontology 'HPPA' (2015-07-23 12:57:18) ...
'onto.HPPA' (from package 'dcGOR' version 1.0.5) has been loaded into the working environment
Second, import files for GSP and predictions (2015-07-23 12:57:18) ...
Third, propagate GSP annotations (2015-07-23 12:57:20) ...
	At level 16, there are 2 nodes, and 5 incoming neighbors.
	At level 15, there are 7 nodes, and 9 incoming neighbors.
	At level 14, there are 21 nodes, and 42 incoming neighbors.
	At level 13, there are 54 nodes, and 82 incoming neighbors.
	At level 12, there are 105 nodes, and 105 incoming neighbors.
	At level 11, there are 274 nodes, and 188 incoming neighbors.
	At level 10, there are 463 nodes, and 294 incoming neighbors.
	At level 9, there are 782 nodes, and 441 incoming neighbors.
	At level 8, there are 1004 nodes, and 538 incoming neighbors.
	At level 7, there are 1182 nodes, and 581 incoming neighbors.
	At level 6, there are 1295 nodes, and 527 incoming neighbors.
	At level 5, there are 940 nodes, and 290 incoming neighbors.
	At level 4, there are 408 nodes, and 99 incoming neighbors.
	At level 3, there are 114 nodes, and 21 incoming neighbors.
	At level 2, there are 21 nodes, and 1 incoming neighbors.
	At level 1, there are 1 nodes, and 0 incoming neighbors.
	There are 3048 genes/proteins in GSP (2015-07-23 12:58:08).
Fourth, process input predictions (2015-07-23 12:58:08) ...
	There are 3085 genes/proteins in predictions (2015-07-23 12:58:14).
Fifth, calculate the precision and recall for each of 3048 predicted and GSP genes/proteins (2015-07-23 12:58:14).
Finally, calculate the averaged precision and recall (2015-07-23 12:58:16).
In summary, Prediction coverage: 1.00 (amongst 3048 targets in GSP), and F-measure: 0.35.

End at 2015-07-23 12:58:16
Runtime in total is: 58 secs

res_PR

          Precision     Recall
1        1.00000000 0.04034783
0.901476 1.00000000 0.04034783
0.802952 1.00000000 0.04034783
0.704428 1.00000000 0.04034783
0.605904 0.82956037 0.05303582
0.50738  0.60707841 0.09848956
0.408856 0.52151001 0.15324301
0.310332 0.44000040 0.23356765
0.211808 0.32728481 0.36977436
0.113284 0.23301021 0.53028895
0.01476  0.07161905 0.84493817


# 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")

Source code

Source man

See also