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\name{eiQuery}
\alias{eiQuery}
\title{
Perform a query on an embedded database
}
\description{
Finds similar compounds for each query.
}
\usage{
eiQuery(runId,queries,format="sdf",
dir=".",distance=getDefaultDist(descriptorType),conn=defaultConn(dir),
asSimilarity=FALSE, K=200, searchK=-1,lshData=NULL,
mainIds = readIddb(conn,file.path(dir, Main)))
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{runId}{
The id number identifying a particular set of settings for a database. This is generally
the number returned by \code{\link{eiMakeDb}}. If your coming from an older version of eiR, you should
not use this value instead of
specifying \code{r}, \code{d},\code{refIddb}, and \code{descriptorType}.
}
\item{queries}{
This can be either an SDFset, or a file containg 1 or more
query compounds.
}
\item{format}{
The format in which the queries are given. Valid values are: "sdf" when
\code{queries} is either a filename of an sdf file, or and SDFset object;
"compound_id" when \code{queries} is a list of id numbers; and "name", when \code{queries}
is a list of compound names, as returned by \code{cid(apset)}.
}
\item{dir}{
The directory where the "data" directory lives. Defaults to the
current directory.
}
\item{distance}{
The distance function to be used to compute the distance between two descriptors. A default function is
provided for "ap" and "fp" descriptors. The Tanimoto function is used by default.
}
\item{conn}{
Database connection to use.
}
\item{asSimilarity}{
If true, return similarity values instead of distance values. This only works in the given
distance function returns values between 0 and 1. This is true for the default atom pair and
finger print distance functions.
}
\item{K}{
The number of results to return.
}
\item{searchK}{
Tunable Annoy LSH parameter. A larger value will give more accurate results, but will take longer time to return.
The default value of -1 will allow the value to chosen automatically, which will set a value of numTrees * (approximate number of nearest neighbors).
See Annoy page for details. \url{https://github.com/spotify/annoy}
}
\item{lshData}{
DEPRECATED. This is no longer used.
}
\item{mainIds}{
A vector of all id numbers in the current database. This is mainly provided as an option
here to avoid having to re-read the id list multiple times when executing several queries.
If not supplied it will read it in itself.
}
}
\details{
This function identifies the database by the \code{r}, \code{d}, and
\code{refIddb} parameters. The queries can be given in a few
different formats, see the \code{queries} parameter for details.
The LSH algorithm is used to quickly identify compounds similar to the
queries.
This function must use a distance function rather than a similarity function.
However, if the distance function given returns values between 0 and 1, then
the \code{asSimilarity} parameter may be used to return similarity values rather
than distance values.
}
\value{
Returns a data frame with columns 'query', 'target', 'target_ids', and
'distance'. 'query' and 'target' are the compound names and
distance is the distance between them, as computed by
the given distance function.'target_ids' is the compound id of the target.
Query namess are repeated for each matching target found.
If \code{asSimilarity} is true then instead of a "distance"
column there will be a "similarity" column.
}
\author{
Kevin Horan
}
\seealso{
%% ~~objects to See Also as \code{\link{help}}, ~~~
\code{\link{eiInit}}
\code{\link{eiMakeDb}}
\code{\link{eiPerformanceTest}}
}
\examples{
library(snow)
r<- 50
d<- 40
#initialize
data(sdfsample)
dir=file.path(tempdir(),"query")
dir.create(dir)
eiInit(sdfsample,dir=dir,skipPriorities=TRUE)
#create compound db
runId=eiMakeDb(r,d,numSamples=20,dir=dir,
cl=makeCluster(1,type="SOCK",outfile=""))
#find compounds similar two each query
results = eiQuery(runId,sdfsample[1:2],K=15,dir=dir)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
|