Usage
dcAlgoPredictGenome(input.file, RData.HIS = c(NULL, "Feature2GOBP.sf",
"Feature2GOMF.sf", "Feature2GOCC.sf", "Feature2HPPA.sf",
"Feature2GOBP.pfam",
"Feature2GOMF.pfam", "Feature2GOCC.pfam", "Feature2HPPA.pfam",
"Feature2GOBP.interpro", "Feature2GOMF.interpro",
"Feature2GOCC.interpro",
"Feature2HPPA.interpro"), weight.method = c("none", "copynum", "ic",
"both"), 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
input.file
an input file containing genomes and their domain
architectures (including individual domains). For example, a file
containing Hominidae genomes and their domain architectures can be
found in
http://dcgor.r-forge.r-project.org/data/Feature/Hominidae.txt. As
seen in this example, the input file must contain the header (in the
first row) and two columns: 1st column for 'Genome' (a genome like a
container), 2nd column for 'Architecture' (SCOP domain architectures,
each represented as comma-separated domains). Alternatively, the
input.file can be a matrix or data frame, assuming that input file has
been read. Note: the file should use the tab delimiter as the field
separator between columns 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)
weight.method
the method used how to weight predictions. It can
be one of "none" (no weighting; by default), "copynum" for weighting
copynumber of architectures, and "ic" for weighting information content
(ic) of the term, "both" for weighting both copynumber and ic
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 merging. The
sequential merging is done via: $\sum_{i=1}{\frac{R_{i}}{i}}$,
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