dcAlgoPredict {dcGOR}R Documentation

Function to predict ontology terms given domain architectures (including individual domains)

Description

dcAlgoPredict is supposed to predict ontology terms given domain architectures (including individual domains). It involves 3 steps: 1) splitting an architecture into individual domains and all possible consecutive domain combinations (viewed as component features); 2) merging hscores among component features; 3) scaling merged hscores into predictive scores across terms.

Usage

dcAlgoPredict(data, RData.HIS = c(NA, "Feature2GOBP.sf",
"Feature2GOMF.sf",
"Feature2GOCC.sf", "Feature2HPPA.sf", "Feature2GOBP.pfam",
"Feature2GOMF.pfam", "Feature2GOCC.pfam", "Feature2HPPA.pfam",
"Feature2GOBP.interpro", "Feature2GOMF.interpro",
"Feature2GOCC.interpro",
"Feature2HPPA.interpro"), merge.method = c("sum", "max", "sequential"),
scale.method = c("log", "linear", "none"), feature.mode = c("supra",
"individual", "comb"), slim.level = NULL, max.num = NULL,
parallel = TRUE, multicores = NULL, verbose = T,
RData.HIS.customised = NULL,
RData.location =
"https://github.com/hfang-bristol/RDataCentre/blob/master/dcGOR")

Arguments

data

an input data vector containing domain architectures. An architecture is represented in the form of comma-separated domains

RData.HIS

RData to load. This RData conveys two bits of information: 1) feature (domain) type; 2) ontology. It stores the hypergeometric scores (hscore) between features (individual domains or consecutive domain combinations) and ontology terms. The RData name tells which domain type and which ontology to use. It can be: SCOP sf domains/combinations (including "Feature2GOBP.sf", "Feature2GOMF.sf", "Feature2GOCC.sf", "Feature2HPPA.sf"), Pfam domains/combinations (including "Feature2GOBP.pfam", "Feature2GOMF.pfam", "Feature2GOCC.pfam", "Feature2HPPA.pfam"), InterPro domains (including "Feature2GOBP.interpro", "Feature2GOMF.interpro", "Feature2GOCC.interpro", "Feature2HPPA.interpro"). If NA, then the user has to input a customised RData-formatted file (see RData.HIS.customised below)

merge.method

the method used to merge predictions for each component feature (individual domains and their combinations derived from domain architecture). It can be one of "sum" for summing up, "max" for the maximum, and "sequential" for the sequential weighting. The sequential weighting is done via: ∑_{i=1}{\frac{R_{i}}{i}}, where R_{i} is the i^{th} ranked highest hscore

scale.method

the method used to scale the predictive scores. It can be: "none" for no scaling, "linear" for being linearily scaled into the range between 0 and 1, "log" for the same as "linear" but being first log-transformed before being scaled. The scaling between 0 and 1 is done via: \frac{S - S_{min}}{S_{max} - S_{min}}, where S_{min} and S_{max} are the minimum and maximum values for S

feature.mode

the mode of how to define the features thereof. It can be: "supra" for combinations of one or two successive domains (including individual domains; considering the order), "individual" for individual domains only, and "comb" for all possible combinations (including individual domains; ignoring the order)

slim.level

whether only slim terms are returned. By defaut, it is NULL and all predicted terms will be reported. If it is specified as a vector containing any values from 1 to 4, then only slim terms at these levels will be reported. Here is the meaning of these values: '1' for very general terms, '2' for general terms, '3' for specific terms, and '4' for very specific terms

max.num

whether only top terms per sequence are returned. By defaut, it is NULL and no constraint is imposed. If an integer is specified, then all predicted terms (with scores in a decreasing order) beyond this number will be discarded. Notably, this parameter works after the preceding parameter slim.level

parallel

logical to indicate whether parallel computation with multicores is used. By default, it sets to true, but not necessarily does so. Partly because parallel backends available will be system-specific (now only Linux or Mac OS). Also, it will depend on whether these two packages "foreach" and "doMC" have been installed. It can be installed via: source("http://bioconductor.org/biocLite.R"); biocLite(c("foreach","doMC")). If not yet installed, this option will be disabled

multicores

an integer to specify how many cores will be registered as the multicore parallel backend to the 'foreach' package. If NULL, it will use a half of cores available in a user's computer. This option only works when parallel computation is enabled

verbose

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

RData.HIS.customised

a file name for RData-formatted file containing an object of S3 class 'HIS'. By default, it is NULL. It is only needed when the user wants to perform customised analysis. See dcAlgoPropagate on how this object is created

RData.location

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

Value

a named list of architectures, each containing predictive scores

Note

none

See Also

dcRDataLoader, dcSplitArch, dcConverter, dcAlgoPropagate, dcAlgoPredictMain, dcAlgoPredictGenome

Examples

## Not run: 
# 1) randomly generate 5 domains and/or domain architectures
x <- dcRDataLoader(RData="Feature2GOMF.sf")
data <- sample(names(x$hscore), 5)

# 2) get predictive scores of all predicted terms for this domain architecture
## using 'sequential' method (by default)
pscore <- dcAlgoPredict(data=data, RData.HIS="Feature2GOMF.sf",
parallel=FALSE)
## using 'max' method
pscore_max <- dcAlgoPredict(data=data, RData.HIS="Feature2GOMF.sf",
merge.method="max", parallel=FALSE)
## using 'sum' method
pscore_sum <- dcAlgoPredict(data=data, RData.HIS="Feature2GOMF.sf",
merge.method="sum", parallel=FALSE)

# 3) advanced usage
## a) focus on those terms at the 2nd level (general)
pscore <- dcAlgoPredict(data=data, RData.HIS="Feature2GOMF.sf",
slim.level=2, parallel=FALSE)
## b) visualise predictive scores in the ontology hierarchy
### load the ontology
g <- dcRDataLoader("onto.GOMF", verbose=FALSE)
ig <- dcConverter(g, from='Onto', to='igraph', verbose=FALSE)
### do visualisation for the 1st architecture
data <- pscore[[1]]
subg <- dnet::dDAGinduce(ig, nodes_query=names(data),
path.mode="shortest_paths")
dnet::visDAG(g=subg, data=data, node.info="term_id")

## End(Not run)

[Package dcGOR version 1.0.6 Index]