R/sim.R
In girke-lab/ChemmineR-release: Cheminformatics Toolkit for R
cstrsplitSym=NA
.onLoad <- function(libname,pkgname) {
#message("libname: ",libname)
#message("pkgname: ",pkgname)
cstrsplitSym <<- getNativeSymbolInfo("cstrsplit")
}
.db.header.size <- 16
# supported elements
.elements <- list(
R=0,
H=1,
He=2,
Li=3,
Be=4,
B=5,
C=6,
N=7,
O=8,
F=9,
Ne=10,
Na=11,
Mg=12,
Al=13,
Si=14,
P=15,
S=16,
Cl=17,
Ar=18,
K=19,
Ca=20,
Sc=21,
Ti=22,
V=23,
Cr=24,
Mn=25,
Fe=26,
Co=27,
Ni=28,
Cu=29,
Zn=30,
Ga=31,
Ge=32,
As=33,
Se=34,
Br=35,
Kr=36,
Rb=37,
Sr=38,
Y=39,
Zr=40,
Nb=41,
Mo=42,
Tc=43,
Ru=44,
Rh=45,
Pd=46,
Ag=47,
Cd=48,
In=49,
Sn=50,
Sb=51,
Te=52,
I=53,
Xe=54,
Cs=55,
Ba=56,
La=57,
Ce=58,
Pr=59,
Nd=60,
Pm=61,
Sm=62,
Eu=63,
Gd=64,
Tb=65,
Dy=66,
Ho=67,
Er=68,
Tm=69,
Yb=70,
Lu=71,
Hf=72,
Ta=73,
W=74,
Re=75,
Os=76,
Ir=77,
Pt=78,
Au=79,
Hg=80,
Tl=81,
Pb=82,
Bi=83,
Po=84,
At=85,
Rn=86,
Fr=87,
Ra=88,
Ac=89,
Th=90,
Pa=91,
U=92,
Np=93,
Pu=94,
Am=95,
Cm=96,
Bk=97,
Cf=98,
Es=99,
Fm=100,
Md=101,
No=102,
Lr=103,
Rf=104,
Db=105,
Sg=106,
Bh=107,
Hs=108,
Mt=109,
Ds=110,
Rg=111)
# similarity model
# input: descriptors for two compounds. both are vectors
# output: similarity
.cmp.similarity <- function(a, b, mode=1, worst=0,
dbcon.a=NULL, db.intsize.a=4,
dbcon.b=NULL, db.intsize.b=4)
{
## ThG: added for compatability with new S4 classes APset/AP
if(class(a)=="APset") { a <- ap(a)[[1]] }
if(class(b)=="APset") { b <- ap(b)[[1]] }
if(class(a)=="AP") { a <- ap(a) }
if(class(b)=="AP") { b <- ap(b) }
## ThG: end of lines
# check argument, make sure they are vectors and not lists
if (class(a) == 'character' && length(a) == 3 && a[[1]] == 'filedb:')
a <- .load.file.backed.desc(a, dbcon.a, db.intsize.a)
if (class(b) == 'character' && length(b) == 3 && b[[1]] == 'filedb:')
b <- .load.file.backed.desc(b, dbcon.b, db.intsize.b)
if (is.null(b) || is.null(a))
return(0)
if (class(b) != "numeric" || class(a) != "numeric") {
stop("Both arguments must be AP/APset objects or descriptors in the form of vectors.\n",
"Did you use \"[]\" instead of \"[[]]\" to index the descriptor ",
"database?")
}
if (mode == 1 && worst != 0) {
# estimate upper bound
i <- length(a)
j <- length(b)
u_bond <- min(i, j) / max(i, j)
if (u_bond < worst)
return(0)
}
if (mode == 0) {
# if no mode is given, guess a mode based on compound size
# difference
if (length(a) < .25 * length(b) || length(a) > 4 * length(b))
mode <- 3
else
mode <- 1
}
if (mode == 1)
return (length(intersect(a, b)) / length(union(a, b)))
else if (mode == 2)
return (length(intersect(a, b)) / min(length(a), length(b)))
else if (mode == 3) {
s <- length(intersect(a, b)) / min(length(a), length(b))
return (s^3)
}
}
cmp.similarity <- function(a, b, mode=1, worst=0)
{
.cmp.similarity(a, b, mode=mode, worst=worst)
}
cmp.parse1 <- function(filename) sdf2ap(read.SDFset(filename))[[1]]
cmp.parse <- function(filename) apset2descdb(sdf2ap(read.SDFset(filename)))
# search db for similar compounds. `db' gives the database returned by
# `cmp.parse' or `sdfs_to_desc'. `query' gives the descriptor of one compound,
# either returned by `cmp.parse1/sdf_to_desc' or a reference to one instance in
# db such as db$descdb[[123]] two types of cutoff: score cutoff (<=1) or count
# cutoff (>1)
cmp.search <- function(db, query, type=1, cutoff=0.5, return.score=FALSE, quiet=FALSE,
mode=1, visualize=FALSE, visualize.browse=TRUE, visualize.query=NULL)
{
## ThG: added for compatability with new S4 classes APset/AP
## Note: type argument was also added (has no impact on old list object).
dbtype <- as.character(class(db))
if(dbtype=="APset") { db <- apset2descdb(db) }
if(class(query)=="APset") query <- ap(query[[1]])
if(class(query)=="AP") query <- ap(query)
## ThG: end of lines
if (.is.file.backed.desc(query))
query <- .load.file.backed.desc(query)
dbcon <- NULL
intsize <- 4
if (db$type == 'file-backed') {
for (i in 1:length(db$descdb)) {
if (.is.file.backed.desc(db$descdb[[i]])) {
dbcon <- file(paste(db$descdb[[i]][[2]], '.cdb', sep=''), 'rb')
seek(dbcon, 16)
intsize <- readBin(dbcon, integer(), n=1, size=1)
break
}
}
}
scores <- array()
ids <- array()
pos <- 1 # tail of entries
size <- length(db$cids)
perstep <- round(size / 25)
for (i in 1:size) {
if (!quiet && i %% perstep == 0) {
steps <- i / perstep
.progress_bar(paste(min(steps*4, 100), "%", collapse=""))
}
cmp <- db$descdb[[i]]
if (db$type == 'file-backed' && .is.file.backed.desc(db$descdb[[i]]))
cmp <- .load.file.backed.desc(cmp, dbcon, intsize)
if (cutoff <= 1)
score <- .cmp.similarity(cmp, query, mode=mode, worst=cutoff)
else if (pos - 1 < cutoff)
score <- .cmp.similarity(cmp, query, mode=mode, worst=0)
else
score <- .cmp.similarity(cmp, query, mode=mode,
worst=scores[[pos - 1]])
if (cutoff <= 1) {
if (score >= cutoff) {
scores[[pos]] <- score
ids[[pos]] <- i
pos <- pos + 1
}
} else {
# using naive insertion sort
if (pos - 1 < cutoff)
pos <- pos + 1
else if (scores[[pos - 1]] >= score)
next
pos_ <- pos - 2
while (pos_ > 0 && scores[[pos_]] < score) {
scores[[pos_ + 1]] <- scores[[pos_]]
ids[[pos_ + 1]] <- ids[[pos_]]
pos_ <- pos_ - 1
}
pos_ <- pos_ + 1
scores[[pos_]] <- score
ids[[pos_]] <- i
}
}
if (!is.null(dbcon)) close(dbcon)
if (!quiet) cat("\n")
if (cutoff <= 1) {
order <- sort(scores, index=TRUE)[["ix"]]
order <- order[length(order):1]
} else
order <- 1:length(scores)
## ThG: modified/added for compatability with new S4 classes APset/AP
if (return.score & dbtype=="list") {
return(data.frame(ids=ids[order], scores=scores[order]))
}
if (!return.score & dbtype=="list") {
return(ids[order])
}
if (dbtype=="APset") {
if(type==1) {
return(ids[order])
}
if(type==2) {
index <- scores[order]
names(index) <- db$cids[ids[order]]
return(index)
}
if(type==3) {
return(data.frame(index=ids[order], cid=db$cids[ids[order]], scores=scores[order]))
}
}
## ThG: end of lines
}
# segment SDFs. given indices of selected compounds, return the concatenation
# of SDFs for them
sdf.subset <- function(db, cmps)
{
if (!grep('://', db$source) && !file.exists(db$source)) {
stop("cannot find the source SDF file for this database\n")
}
output <- ""
for (i in cmps)
{
sdf <- .extract_single_sdf(db, i)
output <- paste(output, sdf, sep="")
}
return(output)
}
## ThG: function is obsolete for APset class which works with standard R subsetting utilities.
db.subset <- function(db, cmps)
{
return(list(descdb=db$descdb[cmps], cids=db$cids[cmps],
sdfsegs=db$sdfsegs[cmps], source=db$source))
}
# helper function for subset_sdf. This function takes an index, and return the
# SDF correponding compound at that index
.extract_single_sdf <- function(db, index)
{
if (index > length(db$sdfsegs))
stop("Index out of range in .extract_single_sdf")
skip <- db$sdfsegs[[index]]
con <- file(db$source, open="r")
if (skip > 0)
#scan(con, "raw", skip=skip-1, nlines=1, quiet=TRUE)
readLines(con, skip)
sdf <- .read.one.sdf(con, collapse=FALSE)
# replace line 1 with compound index if no name is available
if (length(grep("[a-zA-z0-9]", sdf[[1]])) == 0)
sdf[[1]] <- paste("ChemmineR_Unnamed_Compound_", index, sep="")
# close connection
close(con)
return(paste(sdf, sep="", collapse="\n"))
}
# explain an atom pair descriptor
db.explain <- function(desc)
{
## ThG: added for compatability with new S4 classes APset/AP ##
if(class(desc)=="APset") desc <- ap(desc[[1]])
if(class(desc)=="AP") desc <- ap(desc)
## ThG: end of lines ##
if ('character' %in% class(desc)) {
if (.is.file.backed.desc(desc))
desc <- .load.file.backed.desc(desc)
else
stop("The descriptor(s) to be explained is not valid.")
}
if (length(desc) == 1)
return(.explain(desc))
else {
result <- c()
for (i in 1:length(desc))
result <- c(result, .explain(desc[[i]]))
return(result)
}
}
.explain <- function(desc)
{
# numbers of appearance
occurence <- desc %% 2^7 + 1
desc <- desc %/% 2^7
# extract left atom, distance, and right atom
left <- desc %/% 2^20
desc <- desc %% 2^20
dist <- desc %/% 2^13
desc <- desc %% 2^13
right <- desc
# atom properties
left <- .interpret_atom(left)
right <- .interpret_atom(right)
# output
return (paste(left$symbol, " [", left$neighbors, " neighbor(s),",
left$pi_electrons, " pi electrons]<---",
dist,
"--->", right$symbol, " [", right$neighbors,
" neighbor(s),", right$pi_electrons, " pi electrons]",
collapse="", sep=""))
}
# interpret an atom descriptor
# see also: .gen_atom_desc
.interpret_atom <- function(atom_desc)
{
# atom
atom_symbol <- names(.elements[.elements == atom_desc %/% 2^6])
# neighbors
neighbors <- atom_desc %% 2^6 %/% 2^3
# pi electron
pi_electrons <- atom_desc %% 2^3
return(list(symbol=atom_symbol, neighbors=neighbors,
pi_electrons=pi_electrons))
}
# convert a factor to a vector to simplify Union and Intersection operations.
# It basically eliminate repeated names by adding suffix. For example, if `a'
# appears twice in the factor, it will be converted to `a1' and `a2'.
# this is an internal function and is not intended to be used by users
.factor_to_vector <- function(fac)
{
vec <- vector(mode='numeric')
index <- 1
freq <- table(fac)
keys <- names(freq)
for (i in 1:length(keys)) {
key <- keys[[i]]
if (freq[[key]] == 1) {
vec[[index]] <- as.numeric(key) * 2^7
index <- index + 1
} else {
for (j in 1:freq[[key]]) {
vec[[index]] <- as.numeric(key) * 2^7 + j - 1
index <- index + 1
}
}
}
return(vec)
}
.uniquifyAtomPairs <- function(desc) {
.Call("uniquifyAtomPairs",desc)
}
.progress_bar_int_cnt <- 0
.progress_bar <- function(label=NULL) {
progress <- c("|", "/", "-", "\\")
unlockBinding(".progress_bar_int_cnt", environment(.progress_bar))
.progress_bar_int_cnt <<- .progress_bar_int_cnt %% 4 + 1
if (is.null(label)) {
cat("\r", progress[[.progress_bar_int_cnt]])
} else {
cat("\r \r")
cat(progress[[.progress_bar_int_cnt]], label)
}
}
.postToHost <- function(host, path, data.to.send, port=80)
{
if(missing(path)) path <- "/"
ua <- "User-Agent: Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.8.1.1)"
boundary <- paste(
as.integer(runif(20, min=1, max=10)), collapse=""
)
header <- NULL
header <- c(header, paste("POST ", path, " HTTP/1.1", sep=""))
header <- c(header, paste("Host: ", host, sep=""))
header <- c(header, "Connection: close")
header <- c(header, ua)
header <- c(header, "Accept: */*")
header <- c(header,
paste("Content-type: multipart/form-data; boundary=",
boundary, sep="")
)
boundary <- paste('--', boundary, sep='')
mcontent <- NULL # keeps the content.
size <- 0
for (i in 1:length(data.to.send)) {
value <- data.to.send[[i]]
key <- names(data.to.send)[i]
content <- paste(
boundary, "\n",
"Content-Disposition: form-data; name=\"",
as.character(key), "\"", "\n",
"\n",
as.character(value), "\n",
sep=""
)
mcontent <- c(mcontent, charToRaw(content))
}
size <- length(mcontent) + length(strsplit(boundary, "")[[1]]) + 4
header <- c(header, paste("Content-length: ", size, "\n\n", sep=""))
postdata <- c(
charToRaw(paste(header, collapse="\n")),
mcontent,
charToRaw(paste(boundary,"--\n\n",sep=""))
)
rm(header,mcontent)
scon <- socketConnection(host=host,port=port,open="a+b",blocking=TRUE)
writeBin(postdata, scon, size=1)
output <- character(0)
repeat{
ss <- rawToChar(readBin(scon, "raw", 2048))
output <- paste(output,ss,sep="")
if(regexpr("\r\n0\r\n\r\n",ss)>-1) break()
if(ss == "") break()
}
close(scon)
return(output)
}
.read.one.sdf <- function(filename, collapse=TRUE)
{
if (length(filename) != 1)
stop('reference sdf must be a vector of only one entry!')
if (!("connection" %in% class(filename)) &&
!("character" %in% class(filename)))
stop('reference sdf must be an SDF file or one character string of sdf')
if ("connection" %in% class(filename))
con <- filename
# is filename really a filename a the SDF content? check existence of \n
else if (length(grep("\n", filename)) == 0 &&
length(grep("\r", filename)) == 0)
con <- file(filename, open="r")
else
con <- textConnection(filename)
result <- c()
while (TRUE) {
line <- readLines(con=con, n=1)
if (length(line) == 0 || "$$$$" %in% line) {
if (length(result) == 0)
stop("empty SDF!")
result <- c(result, '$$$$\n')
if (collapse)
return (paste(result, collapse="\n"))
else
return (result)
} else
result <- c(result, line)
}
}
.data.frame.to.str <- function(x)
{
con <- textConnection("string", "w")
write.table(format(x), con, row.names=F, quote=F)
close(con)
return(paste(string, collapse='\n'))
}
.is.file.backed.desc <- function(desc)
{
"character" %in% class(desc) && length(desc) == 3 && desc[[1]] == 'filedb:'
}
.load.file.backed.desc <- function(desc, dbcon=NULL, intsize=4)
{
dbname <- desc[[2]]
pos <- desc[[3]]
closeme <- FALSE
if (is.null(dbcon)) {
db_a <- paste(dbname, '.cdb', sep='')
if (!file.exists(db_a)) stop("cannot find database file ", db_a)
dbcon <- file(db_a, 'rb')
closeme <- TRUE
seek(dbcon, .db.header.size)
intsize <- readBin(dbcon, integer(), n=1, size=1)
}
seek(dbcon, as.integer(pos))
size <- readBin(dbcon, integer(), n=1, size=intsize)
desc <- readBin(dbcon, integer(), n=size, size=intsize)
if (closeme) close(dbcon)
.factor_to_vector(as.factor(desc))
}
.haveOB <- function()
{
if(suppressWarnings(require('ChemmineOB', quietly=T))) {
return(TRUE)
}else{
return(FALSE)
}
}
.ensureOB <- function(mesg = paste("ChemmineOB is required to make use of this function.",
"This package can be installed from BioConductor with the ",
"command 'biocLite(\"ChemmineOB\"). ",
"It is not currently available for windows however.",
"See http://bioconductor.org/packages/devel/bioc/html/ChemmineOB.html",
"for more information"))
{
if(!.haveOB())
stop(mesg)
}
##############################################
## PubChem Fingerprint Similarity Searching ##
##############################################
## Convert base 64 encoded PubChem fingerprints to binary matrix or string vector
## Definition of PubChem's substructure dictionary-based fingerprints:
## ftp://ftp.ncbi.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
## For debugging use command: as.integer(rev(intToBits("19")[1:6]))
## Load PubChem's substructure dictionary from data dir of library
data(pubchemFPencoding); pubchemFPencoding <- pubchemFPencoding
fp2bit <- function(x, type=3, fptag="PUBCHEM_CACTVS_SUBSKEYS") {
## Covert base 64 strings to matrix
if(class(x)=="SDFset") {
blockmatrix <- datablock2ma(datablocklist=datablock(x))
fp <- blockmatrix[, fptag]
}
if(class(x)=="matrix") {
blockmatrix <- x
fp <- blockmatrix[, fptag]
}
if(class(x)=="character") {
fp <- x
}
fpma <- unlist(strsplit(fp, ""))
fpma <- matrix(fpma, length(fp), nchar(fp[1]), byrow=TRUE)
fpma <- fpma[, 1:154] # remove padding signs '='
## base 64 decoding (base 64 alphabet from http://www.faqs.org/rfcs/rfc3548.html)
base64 <- c(A=0,B=1,C=2,D=3,E=4,F=5,G=6,H=7,I=8,J=9,K=10,L=11,M=12,N=13,O=14,P=15,
Q=16,R=17,S=18,T=19,U=20,V=21,W=22,X=23,Y=24,Z=25,a=26,b=27,c=28,d=29,
e=30,f=31,g=32,h=33,i=34,j=35,k=36,l=37,m=38,n=39,o=40,p=41,q=42,r=43,
s=44,t=45,u=46,v=47,w=48,x=49,y=50,z=51,"0"=52,"1"=53,"2"=54,"3"=55,
"4"=56,"5"=57,"6"=58,"7"=59,"8"=60,"9"=61,"+"=62,"/"=63)
fpbitma <- as.integer(intToBits(base64[as.vector(t(fpma))]))
fpbitma <- matrix(fpbitma, length(fpma[,1])*154, 32, byrow=TRUE)[,6:1]
fpbitma <- matrix(t(fpbitma), length(fpma[,1]), 6*154, byrow=TRUE)[,33:913]
pubchemFP <- pubchemFPencoding[,2]
names(pubchemFP) <- pubchemFPencoding[,1]
colnames(fpbitma) <- pubchemFP
rownames(fpbitma) <- names(fp)
if(type==1) {
return(apply(fpbitma, 1, paste, collapse=""))
}
if(type==2) {
return(fpbitma)
}
if(type==3) {
#return(as(fpbitma, "FPset"))
return(new("FPset",fpma=fpbitma,type="pubchem"))
}
}
## Fingerprint comparison and similarity search function
fpSimOrig <- function(x, y, sorted=TRUE, method="Tanimoto", addone=1, cutoff=0, top="all", alpha=1, beta=1, ...) {
## Predefined similarity methods
if(class(method)=="character") {
if(method=="Tanimoto" | method=="tanimoto") method <- function(a,b,c,d) (c+addone)/(a+b+c+addone)
}
if(class(method)=="character") {
if(method=="Euclidean" | method=="euclidean") method <- function(a,b,c,d) sqrt((c+d+addone)/(a+b+c+d+addone))
}
if(class(method)=="character") {
if(method=="Tversky" | method=="tversky") method <- function(a,b,c,d) (c+addone)/(alpha*a + beta*b+c+addone)
}
if(class(method)=="character") {
if(method=="Dice" | method=="dice") method <- function(a,b,c,d) (2*c+addone)/(a+c+b+c+addone)
}
## Check for valid inputs
if(!any(c(is.vector(x), class(x)=="FP", class(x)=="FPset" & length(x)==1))) stop("x needs to be object of class FP, FPset of length one, or vector")
if(!any(c(is.vector(y), is.matrix(y), class(y)=="FP", class(y)=="FPset"))) stop("y needs to be object of class FP/FPset, vector or matrix")
## Convert FP/FPset inputs into vector/matrix format
if(class(x)=="FP") x <- as.numeric(x)
if(class(x)=="FPset") x <- as.numeric(x[[1]])
if(class(y)=="FP") y <- as.numeric(y)
if(class(y)=="FPset") y <- as.matrix(y)
## Computate similarities
if(class(y)=="matrix") {
c <- colSums((t(y) + x) == 2)
d <- colSums((t(y) + x) == 0)
b <- rowSums(y) - c
} else {
c <- sum(x + y == 2)
d <- sum(x + y == 0)
b <- sum(y) - c
}
a <- sum(x) - c
if(sorted==TRUE) {
tmp <- rev(sort(method(a,b,c,d)))
if(top!="all") tmp <- tmp[1:top]
if(top!=1) {
return(tmp[tmp>=cutoff])
} else {
return(tmp) # returns always a value when top=1; required for compatibility with cmp.cluster
}
}
if(sorted==FALSE) {
tmp <- method(a,b,c,d)
if(top!=1) {
return(tmp[tmp>=cutoff])
} else {
return(tmp) # returns always a value when top=1; required for compatibility with cmp.cluster
}
}
}
fpSim <- function(x, y, sorted=TRUE, method="Tanimoto",
addone=1, cutoff=0, top="all", alpha=1, beta=1,
parameters=NULL,scoreType="similarity") {
if(class(method)=="character") {
if(method=="Tanimoto" | method=="tanimoto")
method <- 0
else if(method=="Euclidean" | method=="euclidean")
method <- 1
else if(method=="Tversky" | method=="tversky")
method <- 2
else if(method=="Dice" | method=="dice")
method <- 3
else
stop("invalid method found: ",method)
}else
return(fpSimOrig(x,y,sorted=sorted,method=method,addone=addone,cutoff=cutoff,top=top,
alpha=alpha, beta=beta))
#stop("invalid method type found: ",class(method))
if(!any(c(is.vector(x), class(x)=="FP", class(x)=="FPset" & length(x)==1)))
stop("x needs to be object of class FP, FPset of length one, or vector")
if(!any(c(is.vector(y), is.matrix(y), class(y)=="FP", class(y)=="FPset")))
stop("y needs to be object of class FP/FPset, vector or matrix")
## Convert FP/FPset inputs into vector/matrix format
if(class(x)=="FP")
x <- as.numeric(x)
else if(class(x)=="FPset")
x <- as.numeric(x[[1]])
if(class(y)=="FP") {
dbSize = 1
y <- as.numeric(y)
}
else if(class(y)=="FPset"){
dbSize=length(y)
y <- as.matrix(y)
}
else if(is.vector(y))
dbSize=1
else if(is.matrix(y))
dbSize=nrow(y)
if(is.vector(y)) y <- t(as.matrix(y))
result=.Call("similarity",x,y,method,addone,alpha,beta)
names(result) = rownames(y)
if(!is.null(parameters)){
numBitsSet = sum(x)+1
#N = parameters$count[numBitsSet]
#if(N==0){ # no stats collected for this number of bits so use global values
if(parameters$count[numBitsSet]==0){ # no stats collected for this number of bits so use global values
warning("no parameters avaliable for fingerprints with ",numBitsSet-1," bits set, using global parameters")
numBitsSet=nrow(parameters) #global stats are last element of parameters
#N = parameters$count[numBitsSet]
}
#message("using stats for ",numBitsSet-1," bits")
#print(parameters[numBitsSet,])
avg = parameters$avg[numBitsSet]
varience = parameters$varience[numBitsSet]
alpha = parameters$alpha[numBitsSet]
beta = parameters$beta[numBitsSet]
evalues = dbSize*(1-pbeta(result,alpha,beta))
# scores <<- data.frame(similarity=result, # ThG: replaced by next line on Dec 31, 2015 since build of R Markdown vignette returned error with '<<-'.
scores = data.frame(similarity=result,
zscore=(result - avg) /sqrt(varience),
evalue=evalues,
pvalue=1-exp(-evalues))
titles = 1:4
names(titles)=colnames(scores)
if(sorted){
if(titles[scoreType] <=2 ) #similarity or zscore, bigger is better
scores = scores[order(-scores[,titles[scoreType]]),]
else
scores = scores[order(scores[,titles[scoreType]]),]
if(top!="all")
scores = scores[1:top,]
}
if(!missing(cutoff)){
if(titles[scoreType] <=2 ) #similarity or zscore, bigger is better
cutScores = scores[scores[,titles[scoreType]]>= cutoff,]
else # e or p values, lower is better
cutScores = scores[scores[,titles[scoreType]]<= cutoff,]
# make sure we don't lose all results do to cutoff
if( nrow(cutScores) >= 1)
scores = cutScores
}
scores
}
else{
if(sorted) {
result = sort(result, decreasing=TRUE)
if(top!="all")
result = result[1:top]
}
cutResult = result[result >= cutoff]
if( length(cutResult) >= 1)
cutResult
else # make sure we don't lose all results do to cutoff
result
}
}
######################################
## Query ChemMine Web Tools Service ##
######################################
.serverURL <- "http://chemmine.ucr.edu/ChemmineR/"
# perform sdf to smiles conversion
sdf2smilesOB <- function(sdf) {
if(! class(sdf) == "SDFset"){
stop('reference compound must be a compound of class \"SDFset\"')
}
if(.haveOB()){
#sdfstrList=as(as(sdf,"SDFstr"),"list")
#defs = paste(Map(function(x) paste(x,collapse="\n"), sdfstrList),collapse="\n" )
defs = sdfSet2definition(sdf)
t=Reduce(rbind,strsplit(unlist(strsplit(convertFormat("SDF","SMI",defs),
"\n",fixed=TRUE)),
"\t",fixed=TRUE))
if(class(t)=="character"){ # R rearranged our matrix because there was only one result
smiles=t[1]
names(smiles)=t[2]
}else{
smiles = t[,1]
names(smiles)= t[,2]
}
as(smiles, "SMIset")
}else{
message("ChemmineOB not found, falling back to web service version. This will be much slower")
sdf2smilesWeb(sdf)
}
}
sdf2smiles <- sdf2smilesOB
sdf2smilesWeb <- function(sdfset,limit=100){
#message("class of sdfset: ",class(sdfset))
if(length(sdfset) > limit)
sdfset = sdfset[1:limit]
smiles =c()
for(i in seq(along=sdfset)){
#message("class of sdfset[[]]: ",class(sdfset[[i]]))
sdf <- sdf2str(sdfset[[i]])
sdf <- paste(sdf, collapse="\n")
response <- postForm(paste(.serverURL, "runapp?app=sdf2smiles", sep=""), sdf=sdf)[[1]]
if(grepl("^ERROR:", response)){
stop(response)
}
response <- sub("\n$", "", response) # remove trailing newline
id <- sub(".*\t(.*)$", "\\1", response) # get id
response <- sub("\t.*$", "", response) # get smiles
names(response) <- id
smiles = c(smiles,response)
}
as(smiles,"SMIset")
}
smiles2sdfOB <- function(smiles) {
if(!class(smiles) %in% c("character", "SMIset")){
stop('input must be SMILES strings stored as \"SMIset\" or \"character\" object')
}
if(class(smiles)=="SMIset") smiles <- as.character(smiles)
if(.haveOB()) {
sdf = definition2SDFset(convertFormat("SMI","SDF",paste(paste(smiles,names(smiles),sep="\t"), collapse="\n")))
cid(sdf)=sdfid(sdf)
sdf
}else{
message("ChemmineOB not found, falling back to web service version. This will be much slower")
sdf =smiles2sdfWeb(smiles)
cid(sdf)=sdfid(sdf)
sdf
}
}
smiles2sdf <- smiles2sdfOB
regenCoordsOB <- function(sdf){
applyOptions(sdf,data.frame(names="gen2D",args=""))
}
regenerateCoords <- regenCoordsOB
generate3DCoordsOB <- function(sdf){
applyOptions(sdf,data.frame(names="gen3D",args=""))
}
generate3DCoords <- generate3DCoordsOB
canonicalizeOB <- function(sdf){
applyOptions(sdf,data.frame(names="canonical",args=""))
}
canonicalize <- canonicalizeOB
canonicalNumberingOB <- function(sdf){
.ensureOB()
results=canonicalNumbering_OB(obmol(sdf))
names(results) = cid(sdf)
results
}
canonicalNumbering <- canonicalNumberingOB
applyOptions <- function(sdf,options){
.ensureOB()
if(class(sdf) == "SDFset" || class(sdf)=="SDF"){
defs = sdfSet2definition(sdf)
sdfNew = definition2SDFset(convertFormat("SDF","SDF",defs,options))
cid(sdfNew)=sdfid(sdf)
if(class(sdf)=="SDF")
sdfNew[[1]]
else
sdfNew
}else
stop("input to applyOptions must be a SDFset or SDF object. Found ",class(sdf))
}
# perform smiles to sdf conversion through ChemMine Web Tools
smiles2sdfWeb <- function(smiles,limit=100) {
if(length(smiles) > limit)
smiles = smiles[1:limit]
smileStrings = if(! is.null(names(smiles)))
paste(smiles, names(smiles), sep="\t")
else
smiles
sdfs = c()
for(smile in smileStrings){
response <- postForm(paste(.serverURL, "runapp?app=smiles2sdf", sep=""), smiles=smile)[[1]]
if(grepl("^ERROR:", response))
stop(response)
response <- strsplit(response, "\n")
response <- as(as(response, "SDFstr"), "SDFset")[[1]]
#response <- as(response, "SDFstr")
sdfs = c(sdfs,response)
}
names(sdfs)=names(smiles)
as(sdfs,"SDFset")
}
times = new.env()
times$descT = 0
times$uniqueT = 0
genAPDescriptors <- function(sdf,uniquePairs=TRUE){
# t=Sys.time()
d=.Call("genAPDescriptor",sdf)
# times$descT <- times$descT + (Sys.time() - t)
if(uniquePairs){
# t=Sys.time()
d= .uniquifyAtomPairs(d)
# itimes$uniqueT <- times$uniqueT + (Sys.time() - t)
}
d
}
propOB <- function(sdfSet){
.ensureOB()
results = prop_OB(obmol(sdfSet))
rownames(results) = cid(sdfSet)
results
}
fingerprintOB <- function(sdfSet,fingerprintName){
.ensureOB()
x = fingerprint_OB(obmol(sdfSet),fingerprintName)
if(is.vector(x)) x= t(as.matrix(x))
fpset = new("FPset",fpma=x,
type=fingerprintName)
cid(fpset) = cid(sdfSet)
fpset
}
smartsSearchOB <- function(sdfset,smartsPattern,uniqueMatches=TRUE){
.ensureOB()
smartsSearch_OB(obmol(sdfset),smartsPattern,uniqueMatches)
}
exactMassOB <- function(sdfset){
.ensureOB()
exactMass_OB(obmol(sdfset))
}
#compounds should be items that can be passed into similarity
maximallyDissimilar <- function(compounds,n,similarity = cmp.similarity) {
dist = function(a,b) 1-similarity(a,b)
if(length(compounds) <= 2)
stop("maximallyDissimilar: compounds must have more than 2 elements")
#pick first selection at random
selected = sample(1:length(compounds),1)
#compute dist between compounds[-selected] and compounds[selected]
minDistsToSelected = sapply(1:length(compounds),
function(i) dist(compounds[[i]],compounds[[selected]]))
for(k in 2:n){
#find candidate farthest from those selected
newPoint = which.max(minDistsToSelected)
selected = c(selected,newPoint)
minDistsToSelected[newPoint] = 0
#update min distances
minDistsToSelected = sapply(1:length(compounds),
function(i)
if(minDistsToSelected[i]==0) 0
else min(minDistsToSelected[i],dist(compounds[i],compounds[newPoint])))
}
compounds[selected]
}
sdf2OBMol<- function(sdfSet){
.ensureOB()
defs = paste(Map(function(x) paste(x,collapse="\n"),
as(as(sdfSet,"SDFstr"),"list")),"\n",
sep="",collapse="" )
forEachMol("SDF",defs,identity,c)
}
girke-lab/ChemmineR-release documentation built on May 17, 2019, 5:26 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
cstrsplitSym=NA
.onLoad <- function(libname,pkgname) {
#message("libname: ",libname)
#message("pkgname: ",pkgname)
cstrsplitSym <<- getNativeSymbolInfo("cstrsplit")
}
.db.header.size <- 16
# supported elements
.elements <- list(
R=0,
H=1,
He=2,
Li=3,
Be=4,
B=5,
C=6,
N=7,
O=8,
F=9,
Ne=10,
Na=11,
Mg=12,
Al=13,
Si=14,
P=15,
S=16,
Cl=17,
Ar=18,
K=19,
Ca=20,
Sc=21,
Ti=22,
V=23,
Cr=24,
Mn=25,
Fe=26,
Co=27,
Ni=28,
Cu=29,
Zn=30,
Ga=31,
Ge=32,
As=33,
Se=34,
Br=35,
Kr=36,
Rb=37,
Sr=38,
Y=39,
Zr=40,
Nb=41,
Mo=42,
Tc=43,
Ru=44,
Rh=45,
Pd=46,
Ag=47,
Cd=48,
In=49,
Sn=50,
Sb=51,
Te=52,
I=53,
Xe=54,
Cs=55,
Ba=56,
La=57,
Ce=58,
Pr=59,
Nd=60,
Pm=61,
Sm=62,
Eu=63,
Gd=64,
Tb=65,
Dy=66,
Ho=67,
Er=68,
Tm=69,
Yb=70,
Lu=71,
Hf=72,
Ta=73,
W=74,
Re=75,
Os=76,
Ir=77,
Pt=78,
Au=79,
Hg=80,
Tl=81,
Pb=82,
Bi=83,
Po=84,
At=85,
Rn=86,
Fr=87,
Ra=88,
Ac=89,
Th=90,
Pa=91,
U=92,
Np=93,
Pu=94,
Am=95,
Cm=96,
Bk=97,
Cf=98,
Es=99,
Fm=100,
Md=101,
No=102,
Lr=103,
Rf=104,
Db=105,
Sg=106,
Bh=107,
Hs=108,
Mt=109,
Ds=110,
Rg=111)
# similarity model
# input: descriptors for two compounds. both are vectors
# output: similarity
.cmp.similarity <- function(a, b, mode=1, worst=0,
dbcon.a=NULL, db.intsize.a=4,
dbcon.b=NULL, db.intsize.b=4)
{
## ThG: added for compatability with new S4 classes APset/AP
if(class(a)=="APset") { a <- ap(a)[[1]] }
if(class(b)=="APset") { b <- ap(b)[[1]] }
if(class(a)=="AP") { a <- ap(a) }
if(class(b)=="AP") { b <- ap(b) }
## ThG: end of lines
# check argument, make sure they are vectors and not lists
if (class(a) == 'character' && length(a) == 3 && a[[1]] == 'filedb:')
a <- .load.file.backed.desc(a, dbcon.a, db.intsize.a)
if (class(b) == 'character' && length(b) == 3 && b[[1]] == 'filedb:')
b <- .load.file.backed.desc(b, dbcon.b, db.intsize.b)
if (is.null(b) || is.null(a))
return(0)
if (class(b) != "numeric" || class(a) != "numeric") {
stop("Both arguments must be AP/APset objects or descriptors in the form of vectors.\n",
"Did you use \"[]\" instead of \"[[]]\" to index the descriptor ",
"database?")
}
if (mode == 1 && worst != 0) {
# estimate upper bound
i <- length(a)
j <- length(b)
u_bond <- min(i, j) / max(i, j)
if (u_bond < worst)
return(0)
}
if (mode == 0) {
# if no mode is given, guess a mode based on compound size
# difference
if (length(a) < .25 * length(b) || length(a) > 4 * length(b))
mode <- 3
else
mode <- 1
}
if (mode == 1)
return (length(intersect(a, b)) / length(union(a, b)))
else if (mode == 2)
return (length(intersect(a, b)) / min(length(a), length(b)))
else if (mode == 3) {
s <- length(intersect(a, b)) / min(length(a), length(b))
return (s^3)
}
}
cmp.similarity <- function(a, b, mode=1, worst=0)
{
.cmp.similarity(a, b, mode=mode, worst=worst)
}
cmp.parse1 <- function(filename) sdf2ap(read.SDFset(filename))[[1]]
cmp.parse <- function(filename) apset2descdb(sdf2ap(read.SDFset(filename)))
# search db for similar compounds. `db' gives the database returned by
# `cmp.parse' or `sdfs_to_desc'. `query' gives the descriptor of one compound,
# either returned by `cmp.parse1/sdf_to_desc' or a reference to one instance in
# db such as db$descdb[[123]] two types of cutoff: score cutoff (<=1) or count
# cutoff (>1)
cmp.search <- function(db, query, type=1, cutoff=0.5, return.score=FALSE, quiet=FALSE,
mode=1, visualize=FALSE, visualize.browse=TRUE, visualize.query=NULL)
{
## ThG: added for compatability with new S4 classes APset/AP
## Note: type argument was also added (has no impact on old list object).
dbtype <- as.character(class(db))
if(dbtype=="APset") { db <- apset2descdb(db) }
if(class(query)=="APset") query <- ap(query[[1]])
if(class(query)=="AP") query <- ap(query)
## ThG: end of lines
if (.is.file.backed.desc(query))
query <- .load.file.backed.desc(query)
dbcon <- NULL
intsize <- 4
if (db$type == 'file-backed') {
for (i in 1:length(db$descdb)) {
if (.is.file.backed.desc(db$descdb[[i]])) {
dbcon <- file(paste(db$descdb[[i]][[2]], '.cdb', sep=''), 'rb')
seek(dbcon, 16)
intsize <- readBin(dbcon, integer(), n=1, size=1)
break
}
}
}
scores <- array()
ids <- array()
pos <- 1 # tail of entries
size <- length(db$cids)
perstep <- round(size / 25)
for (i in 1:size) {
if (!quiet && i %% perstep == 0) {
steps <- i / perstep
.progress_bar(paste(min(steps*4, 100), "%", collapse=""))
}
cmp <- db$descdb[[i]]
if (db$type == 'file-backed' && .is.file.backed.desc(db$descdb[[i]]))
cmp <- .load.file.backed.desc(cmp, dbcon, intsize)
if (cutoff <= 1)
score <- .cmp.similarity(cmp, query, mode=mode, worst=cutoff)
else if (pos - 1 < cutoff)
score <- .cmp.similarity(cmp, query, mode=mode, worst=0)
else
score <- .cmp.similarity(cmp, query, mode=mode,
worst=scores[[pos - 1]])
if (cutoff <= 1) {
if (score >= cutoff) {
scores[[pos]] <- score
ids[[pos]] <- i
pos <- pos + 1
}
} else {
# using naive insertion sort
if (pos - 1 < cutoff)
pos <- pos + 1
else if (scores[[pos - 1]] >= score)
next
pos_ <- pos - 2
while (pos_ > 0 && scores[[pos_]] < score) {
scores[[pos_ + 1]] <- scores[[pos_]]
ids[[pos_ + 1]] <- ids[[pos_]]
pos_ <- pos_ - 1
}
pos_ <- pos_ + 1
scores[[pos_]] <- score
ids[[pos_]] <- i
}
}
if (!is.null(dbcon)) close(dbcon)
if (!quiet) cat("\n")
if (cutoff <= 1) {
order <- sort(scores, index=TRUE)[["ix"]]
order <- order[length(order):1]
} else
order <- 1:length(scores)
## ThG: modified/added for compatability with new S4 classes APset/AP
if (return.score & dbtype=="list") {
return(data.frame(ids=ids[order], scores=scores[order]))
}
if (!return.score & dbtype=="list") {
return(ids[order])
}
if (dbtype=="APset") {
if(type==1) {
return(ids[order])
}
if(type==2) {
index <- scores[order]
names(index) <- db$cids[ids[order]]
return(index)
}
if(type==3) {
return(data.frame(index=ids[order], cid=db$cids[ids[order]], scores=scores[order]))
}
}
## ThG: end of lines
}
# segment SDFs. given indices of selected compounds, return the concatenation
# of SDFs for them
sdf.subset <- function(db, cmps)
{
if (!grep('://', db$source) && !file.exists(db$source)) {
stop("cannot find the source SDF file for this database\n")
}
output <- ""
for (i in cmps)
{
sdf <- .extract_single_sdf(db, i)
output <- paste(output, sdf, sep="")
}
return(output)
}
## ThG: function is obsolete for APset class which works with standard R subsetting utilities.
db.subset <- function(db, cmps)
{
return(list(descdb=db$descdb[cmps], cids=db$cids[cmps],
sdfsegs=db$sdfsegs[cmps], source=db$source))
}
# helper function for subset_sdf. This function takes an index, and return the
# SDF correponding compound at that index
.extract_single_sdf <- function(db, index)
{
if (index > length(db$sdfsegs))
stop("Index out of range in .extract_single_sdf")
skip <- db$sdfsegs[[index]]
con <- file(db$source, open="r")
if (skip > 0)
#scan(con, "raw", skip=skip-1, nlines=1, quiet=TRUE)
readLines(con, skip)
sdf <- .read.one.sdf(con, collapse=FALSE)
# replace line 1 with compound index if no name is available
if (length(grep("[a-zA-z0-9]", sdf[[1]])) == 0)
sdf[[1]] <- paste("ChemmineR_Unnamed_Compound_", index, sep="")
# close connection
close(con)
return(paste(sdf, sep="", collapse="\n"))
}
# explain an atom pair descriptor
db.explain <- function(desc)
{
## ThG: added for compatability with new S4 classes APset/AP ##
if(class(desc)=="APset") desc <- ap(desc[[1]])
if(class(desc)=="AP") desc <- ap(desc)
## ThG: end of lines ##
if ('character' %in% class(desc)) {
if (.is.file.backed.desc(desc))
desc <- .load.file.backed.desc(desc)
else
stop("The descriptor(s) to be explained is not valid.")
}
if (length(desc) == 1)
return(.explain(desc))
else {
result <- c()
for (i in 1:length(desc))
result <- c(result, .explain(desc[[i]]))
return(result)
}
}
.explain <- function(desc)
{
# numbers of appearance
occurence <- desc %% 2^7 + 1
desc <- desc %/% 2^7
# extract left atom, distance, and right atom
left <- desc %/% 2^20
desc <- desc %% 2^20
dist <- desc %/% 2^13
desc <- desc %% 2^13
right <- desc
# atom properties
left <- .interpret_atom(left)
right <- .interpret_atom(right)
# output
return (paste(left$symbol, " [", left$neighbors, " neighbor(s),",
left$pi_electrons, " pi electrons]<---",
dist,
"--->", right$symbol, " [", right$neighbors,
" neighbor(s),", right$pi_electrons, " pi electrons]",
collapse="", sep=""))
}
# interpret an atom descriptor
# see also: .gen_atom_desc
.interpret_atom <- function(atom_desc)
{
# atom
atom_symbol <- names(.elements[.elements == atom_desc %/% 2^6])
# neighbors
neighbors <- atom_desc %% 2^6 %/% 2^3
# pi electron
pi_electrons <- atom_desc %% 2^3
return(list(symbol=atom_symbol, neighbors=neighbors,
pi_electrons=pi_electrons))
}
# convert a factor to a vector to simplify Union and Intersection operations.
# It basically eliminate repeated names by adding suffix. For example, if `a'
# appears twice in the factor, it will be converted to `a1' and `a2'.
# this is an internal function and is not intended to be used by users
.factor_to_vector <- function(fac)
{
vec <- vector(mode='numeric')
index <- 1
freq <- table(fac)
keys <- names(freq)
for (i in 1:length(keys)) {
key <- keys[[i]]
if (freq[[key]] == 1) {
vec[[index]] <- as.numeric(key) * 2^7
index <- index + 1
} else {
for (j in 1:freq[[key]]) {
vec[[index]] <- as.numeric(key) * 2^7 + j - 1
index <- index + 1
}
}
}
return(vec)
}
.uniquifyAtomPairs <- function(desc) {
.Call("uniquifyAtomPairs",desc)
}
.progress_bar_int_cnt <- 0
.progress_bar <- function(label=NULL) {
progress <- c("|", "/", "-", "\\")
unlockBinding(".progress_bar_int_cnt", environment(.progress_bar))
.progress_bar_int_cnt <<- .progress_bar_int_cnt %% 4 + 1
if (is.null(label)) {
cat("\r", progress[[.progress_bar_int_cnt]])
} else {
cat("\r \r")
cat(progress[[.progress_bar_int_cnt]], label)
}
}
.postToHost <- function(host, path, data.to.send, port=80)
{
if(missing(path)) path <- "/"
ua <- "User-Agent: Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.8.1.1)"
boundary <- paste(
as.integer(runif(20, min=1, max=10)), collapse=""
)
header <- NULL
header <- c(header, paste("POST ", path, " HTTP/1.1", sep=""))
header <- c(header, paste("Host: ", host, sep=""))
header <- c(header, "Connection: close")
header <- c(header, ua)
header <- c(header, "Accept: */*")
header <- c(header,
paste("Content-type: multipart/form-data; boundary=",
boundary, sep="")
)
boundary <- paste('--', boundary, sep='')
mcontent <- NULL # keeps the content.
size <- 0
for (i in 1:length(data.to.send)) {
value <- data.to.send[[i]]
key <- names(data.to.send)[i]
content <- paste(
boundary, "\n",
"Content-Disposition: form-data; name=\"",
as.character(key), "\"", "\n",
"\n",
as.character(value), "\n",
sep=""
)
mcontent <- c(mcontent, charToRaw(content))
}
size <- length(mcontent) + length(strsplit(boundary, "")[[1]]) + 4
header <- c(header, paste("Content-length: ", size, "\n\n", sep=""))
postdata <- c(
charToRaw(paste(header, collapse="\n")),
mcontent,
charToRaw(paste(boundary,"--\n\n",sep=""))
)
rm(header,mcontent)
scon <- socketConnection(host=host,port=port,open="a+b",blocking=TRUE)
writeBin(postdata, scon, size=1)
output <- character(0)
repeat{
ss <- rawToChar(readBin(scon, "raw", 2048))
output <- paste(output,ss,sep="")
if(regexpr("\r\n0\r\n\r\n",ss)>-1) break()
if(ss == "") break()
}
close(scon)
return(output)
}
.read.one.sdf <- function(filename, collapse=TRUE)
{
if (length(filename) != 1)
stop('reference sdf must be a vector of only one entry!')
if (!("connection" %in% class(filename)) &&
!("character" %in% class(filename)))
stop('reference sdf must be an SDF file or one character string of sdf')
if ("connection" %in% class(filename))
con <- filename
# is filename really a filename a the SDF content? check existence of \n
else if (length(grep("\n", filename)) == 0 &&
length(grep("\r", filename)) == 0)
con <- file(filename, open="r")
else
con <- textConnection(filename)
result <- c()
while (TRUE) {
line <- readLines(con=con, n=1)
if (length(line) == 0 || "$$$$" %in% line) {
if (length(result) == 0)
stop("empty SDF!")
result <- c(result, '$$$$\n')
if (collapse)
return (paste(result, collapse="\n"))
else
return (result)
} else
result <- c(result, line)
}
}
.data.frame.to.str <- function(x)
{
con <- textConnection("string", "w")
write.table(format(x), con, row.names=F, quote=F)
close(con)
return(paste(string, collapse='\n'))
}
.is.file.backed.desc <- function(desc)
{
"character" %in% class(desc) && length(desc) == 3 && desc[[1]] == 'filedb:'
}
.load.file.backed.desc <- function(desc, dbcon=NULL, intsize=4)
{
dbname <- desc[[2]]
pos <- desc[[3]]
closeme <- FALSE
if (is.null(dbcon)) {
db_a <- paste(dbname, '.cdb', sep='')
if (!file.exists(db_a)) stop("cannot find database file ", db_a)
dbcon <- file(db_a, 'rb')
closeme <- TRUE
seek(dbcon, .db.header.size)
intsize <- readBin(dbcon, integer(), n=1, size=1)
}
seek(dbcon, as.integer(pos))
size <- readBin(dbcon, integer(), n=1, size=intsize)
desc <- readBin(dbcon, integer(), n=size, size=intsize)
if (closeme) close(dbcon)
.factor_to_vector(as.factor(desc))
}
.haveOB <- function()
{
if(suppressWarnings(require('ChemmineOB', quietly=T))) {
return(TRUE)
}else{
return(FALSE)
}
}
.ensureOB <- function(mesg = paste("ChemmineOB is required to make use of this function.",
"This package can be installed from BioConductor with the ",
"command 'biocLite(\"ChemmineOB\"). ",
"It is not currently available for windows however.",
"See http://bioconductor.org/packages/devel/bioc/html/ChemmineOB.html",
"for more information"))
{
if(!.haveOB())
stop(mesg)
}
##############################################
## PubChem Fingerprint Similarity Searching ##
##############################################
## Convert base 64 encoded PubChem fingerprints to binary matrix or string vector
## Definition of PubChem's substructure dictionary-based fingerprints:
## ftp://ftp.ncbi.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
## For debugging use command: as.integer(rev(intToBits("19")[1:6]))
## Load PubChem's substructure dictionary from data dir of library
data(pubchemFPencoding); pubchemFPencoding <- pubchemFPencoding
fp2bit <- function(x, type=3, fptag="PUBCHEM_CACTVS_SUBSKEYS") {
## Covert base 64 strings to matrix
if(class(x)=="SDFset") {
blockmatrix <- datablock2ma(datablocklist=datablock(x))
fp <- blockmatrix[, fptag]
}
if(class(x)=="matrix") {
blockmatrix <- x
fp <- blockmatrix[, fptag]
}
if(class(x)=="character") {
fp <- x
}
fpma <- unlist(strsplit(fp, ""))
fpma <- matrix(fpma, length(fp), nchar(fp[1]), byrow=TRUE)
fpma <- fpma[, 1:154] # remove padding signs '='
## base 64 decoding (base 64 alphabet from http://www.faqs.org/rfcs/rfc3548.html)
base64 <- c(A=0,B=1,C=2,D=3,E=4,F=5,G=6,H=7,I=8,J=9,K=10,L=11,M=12,N=13,O=14,P=15,
Q=16,R=17,S=18,T=19,U=20,V=21,W=22,X=23,Y=24,Z=25,a=26,b=27,c=28,d=29,
e=30,f=31,g=32,h=33,i=34,j=35,k=36,l=37,m=38,n=39,o=40,p=41,q=42,r=43,
s=44,t=45,u=46,v=47,w=48,x=49,y=50,z=51,"0"=52,"1"=53,"2"=54,"3"=55,
"4"=56,"5"=57,"6"=58,"7"=59,"8"=60,"9"=61,"+"=62,"/"=63)
fpbitma <- as.integer(intToBits(base64[as.vector(t(fpma))]))
fpbitma <- matrix(fpbitma, length(fpma[,1])*154, 32, byrow=TRUE)[,6:1]
fpbitma <- matrix(t(fpbitma), length(fpma[,1]), 6*154, byrow=TRUE)[,33:913]
pubchemFP <- pubchemFPencoding[,2]
names(pubchemFP) <- pubchemFPencoding[,1]
colnames(fpbitma) <- pubchemFP
rownames(fpbitma) <- names(fp)
if(type==1) {
return(apply(fpbitma, 1, paste, collapse=""))
}
if(type==2) {
return(fpbitma)
}
if(type==3) {
#return(as(fpbitma, "FPset"))
return(new("FPset",fpma=fpbitma,type="pubchem"))
}
}
## Fingerprint comparison and similarity search function
fpSimOrig <- function(x, y, sorted=TRUE, method="Tanimoto", addone=1, cutoff=0, top="all", alpha=1, beta=1, ...) {
## Predefined similarity methods
if(class(method)=="character") {
if(method=="Tanimoto" | method=="tanimoto") method <- function(a,b,c,d) (c+addone)/(a+b+c+addone)
}
if(class(method)=="character") {
if(method=="Euclidean" | method=="euclidean") method <- function(a,b,c,d) sqrt((c+d+addone)/(a+b+c+d+addone))
}
if(class(method)=="character") {
if(method=="Tversky" | method=="tversky") method <- function(a,b,c,d) (c+addone)/(alpha*a + beta*b+c+addone)
}
if(class(method)=="character") {
if(method=="Dice" | method=="dice") method <- function(a,b,c,d) (2*c+addone)/(a+c+b+c+addone)
}
## Check for valid inputs
if(!any(c(is.vector(x), class(x)=="FP", class(x)=="FPset" & length(x)==1))) stop("x needs to be object of class FP, FPset of length one, or vector")
if(!any(c(is.vector(y), is.matrix(y), class(y)=="FP", class(y)=="FPset"))) stop("y needs to be object of class FP/FPset, vector or matrix")
## Convert FP/FPset inputs into vector/matrix format
if(class(x)=="FP") x <- as.numeric(x)
if(class(x)=="FPset") x <- as.numeric(x[[1]])
if(class(y)=="FP") y <- as.numeric(y)
if(class(y)=="FPset") y <- as.matrix(y)
## Computate similarities
if(class(y)=="matrix") {
c <- colSums((t(y) + x) == 2)
d <- colSums((t(y) + x) == 0)
b <- rowSums(y) - c
} else {
c <- sum(x + y == 2)
d <- sum(x + y == 0)
b <- sum(y) - c
}
a <- sum(x) - c
if(sorted==TRUE) {
tmp <- rev(sort(method(a,b,c,d)))
if(top!="all") tmp <- tmp[1:top]
if(top!=1) {
return(tmp[tmp>=cutoff])
} else {
return(tmp) # returns always a value when top=1; required for compatibility with cmp.cluster
}
}
if(sorted==FALSE) {
tmp <- method(a,b,c,d)
if(top!=1) {
return(tmp[tmp>=cutoff])
} else {
return(tmp) # returns always a value when top=1; required for compatibility with cmp.cluster
}
}
}
fpSim <- function(x, y, sorted=TRUE, method="Tanimoto",
addone=1, cutoff=0, top="all", alpha=1, beta=1,
parameters=NULL,scoreType="similarity") {
if(class(method)=="character") {
if(method=="Tanimoto" | method=="tanimoto")
method <- 0
else if(method=="Euclidean" | method=="euclidean")
method <- 1
else if(method=="Tversky" | method=="tversky")
method <- 2
else if(method=="Dice" | method=="dice")
method <- 3
else
stop("invalid method found: ",method)
}else
return(fpSimOrig(x,y,sorted=sorted,method=method,addone=addone,cutoff=cutoff,top=top,
alpha=alpha, beta=beta))
#stop("invalid method type found: ",class(method))
if(!any(c(is.vector(x), class(x)=="FP", class(x)=="FPset" & length(x)==1)))
stop("x needs to be object of class FP, FPset of length one, or vector")
if(!any(c(is.vector(y), is.matrix(y), class(y)=="FP", class(y)=="FPset")))
stop("y needs to be object of class FP/FPset, vector or matrix")
## Convert FP/FPset inputs into vector/matrix format
if(class(x)=="FP")
x <- as.numeric(x)
else if(class(x)=="FPset")
x <- as.numeric(x[[1]])
if(class(y)=="FP") {
dbSize = 1
y <- as.numeric(y)
}
else if(class(y)=="FPset"){
dbSize=length(y)
y <- as.matrix(y)
}
else if(is.vector(y))
dbSize=1
else if(is.matrix(y))
dbSize=nrow(y)
if(is.vector(y)) y <- t(as.matrix(y))
result=.Call("similarity",x,y,method,addone,alpha,beta)
names(result) = rownames(y)
if(!is.null(parameters)){
numBitsSet = sum(x)+1
#N = parameters$count[numBitsSet]
#if(N==0){ # no stats collected for this number of bits so use global values
if(parameters$count[numBitsSet]==0){ # no stats collected for this number of bits so use global values
warning("no parameters avaliable for fingerprints with ",numBitsSet-1," bits set, using global parameters")
numBitsSet=nrow(parameters) #global stats are last element of parameters
#N = parameters$count[numBitsSet]
}
#message("using stats for ",numBitsSet-1," bits")
#print(parameters[numBitsSet,])
avg = parameters$avg[numBitsSet]
varience = parameters$varience[numBitsSet]
alpha = parameters$alpha[numBitsSet]
beta = parameters$beta[numBitsSet]
evalues = dbSize*(1-pbeta(result,alpha,beta))
# scores <<- data.frame(similarity=result, # ThG: replaced by next line on Dec 31, 2015 since build of R Markdown vignette returned error with '<<-'.
scores = data.frame(similarity=result,
zscore=(result - avg) /sqrt(varience),
evalue=evalues,
pvalue=1-exp(-evalues))
titles = 1:4
names(titles)=colnames(scores)
if(sorted){
if(titles[scoreType] <=2 ) #similarity or zscore, bigger is better
scores = scores[order(-scores[,titles[scoreType]]),]
else
scores = scores[order(scores[,titles[scoreType]]),]
if(top!="all")
scores = scores[1:top,]
}
if(!missing(cutoff)){
if(titles[scoreType] <=2 ) #similarity or zscore, bigger is better
cutScores = scores[scores[,titles[scoreType]]>= cutoff,]
else # e or p values, lower is better
cutScores = scores[scores[,titles[scoreType]]<= cutoff,]
# make sure we don't lose all results do to cutoff
if( nrow(cutScores) >= 1)
scores = cutScores
}
scores
}
else{
if(sorted) {
result = sort(result, decreasing=TRUE)
if(top!="all")
result = result[1:top]
}
cutResult = result[result >= cutoff]
if( length(cutResult) >= 1)
cutResult
else # make sure we don't lose all results do to cutoff
result
}
}
######################################
## Query ChemMine Web Tools Service ##
######################################
.serverURL <- "http://chemmine.ucr.edu/ChemmineR/"
# perform sdf to smiles conversion
sdf2smilesOB <- function(sdf) {
if(! class(sdf) == "SDFset"){
stop('reference compound must be a compound of class \"SDFset\"')
}
if(.haveOB()){
#sdfstrList=as(as(sdf,"SDFstr"),"list")
#defs = paste(Map(function(x) paste(x,collapse="\n"), sdfstrList),collapse="\n" )
defs = sdfSet2definition(sdf)
t=Reduce(rbind,strsplit(unlist(strsplit(convertFormat("SDF","SMI",defs),
"\n",fixed=TRUE)),
"\t",fixed=TRUE))
if(class(t)=="character"){ # R rearranged our matrix because there was only one result
smiles=t[1]
names(smiles)=t[2]
}else{
smiles = t[,1]
names(smiles)= t[,2]
}
as(smiles, "SMIset")
}else{
message("ChemmineOB not found, falling back to web service version. This will be much slower")
sdf2smilesWeb(sdf)
}
}
sdf2smiles <- sdf2smilesOB
sdf2smilesWeb <- function(sdfset,limit=100){
#message("class of sdfset: ",class(sdfset))
if(length(sdfset) > limit)
sdfset = sdfset[1:limit]
smiles =c()
for(i in seq(along=sdfset)){
#message("class of sdfset[[]]: ",class(sdfset[[i]]))
sdf <- sdf2str(sdfset[[i]])
sdf <- paste(sdf, collapse="\n")
response <- postForm(paste(.serverURL, "runapp?app=sdf2smiles", sep=""), sdf=sdf)[[1]]
if(grepl("^ERROR:", response)){
stop(response)
}
response <- sub("\n$", "", response) # remove trailing newline
id <- sub(".*\t(.*)$", "\\1", response) # get id
response <- sub("\t.*$", "", response) # get smiles
names(response) <- id
smiles = c(smiles,response)
}
as(smiles,"SMIset")
}
smiles2sdfOB <- function(smiles) {
if(!class(smiles) %in% c("character", "SMIset")){
stop('input must be SMILES strings stored as \"SMIset\" or \"character\" object')
}
if(class(smiles)=="SMIset") smiles <- as.character(smiles)
if(.haveOB()) {
sdf = definition2SDFset(convertFormat("SMI","SDF",paste(paste(smiles,names(smiles),sep="\t"), collapse="\n")))
cid(sdf)=sdfid(sdf)
sdf
}else{
message("ChemmineOB not found, falling back to web service version. This will be much slower")
sdf =smiles2sdfWeb(smiles)
cid(sdf)=sdfid(sdf)
sdf
}
}
smiles2sdf <- smiles2sdfOB
regenCoordsOB <- function(sdf){
applyOptions(sdf,data.frame(names="gen2D",args=""))
}
regenerateCoords <- regenCoordsOB
generate3DCoordsOB <- function(sdf){
applyOptions(sdf,data.frame(names="gen3D",args=""))
}
generate3DCoords <- generate3DCoordsOB
canonicalizeOB <- function(sdf){
applyOptions(sdf,data.frame(names="canonical",args=""))
}
canonicalize <- canonicalizeOB
canonicalNumberingOB <- function(sdf){
.ensureOB()
results=canonicalNumbering_OB(obmol(sdf))
names(results) = cid(sdf)
results
}
canonicalNumbering <- canonicalNumberingOB
applyOptions <- function(sdf,options){
.ensureOB()
if(class(sdf) == "SDFset" || class(sdf)=="SDF"){
defs = sdfSet2definition(sdf)
sdfNew = definition2SDFset(convertFormat("SDF","SDF",defs,options))
cid(sdfNew)=sdfid(sdf)
if(class(sdf)=="SDF")
sdfNew[[1]]
else
sdfNew
}else
stop("input to applyOptions must be a SDFset or SDF object. Found ",class(sdf))
}
# perform smiles to sdf conversion through ChemMine Web Tools
smiles2sdfWeb <- function(smiles,limit=100) {
if(length(smiles) > limit)
smiles = smiles[1:limit]
smileStrings = if(! is.null(names(smiles)))
paste(smiles, names(smiles), sep="\t")
else
smiles
sdfs = c()
for(smile in smileStrings){
response <- postForm(paste(.serverURL, "runapp?app=smiles2sdf", sep=""), smiles=smile)[[1]]
if(grepl("^ERROR:", response))
stop(response)
response <- strsplit(response, "\n")
response <- as(as(response, "SDFstr"), "SDFset")[[1]]
#response <- as(response, "SDFstr")
sdfs = c(sdfs,response)
}
names(sdfs)=names(smiles)
as(sdfs,"SDFset")
}
times = new.env()
times$descT = 0
times$uniqueT = 0
genAPDescriptors <- function(sdf,uniquePairs=TRUE){
# t=Sys.time()
d=.Call("genAPDescriptor",sdf)
# times$descT <- times$descT + (Sys.time() - t)
if(uniquePairs){
# t=Sys.time()
d= .uniquifyAtomPairs(d)
# itimes$uniqueT <- times$uniqueT + (Sys.time() - t)
}
d
}
propOB <- function(sdfSet){
.ensureOB()
results = prop_OB(obmol(sdfSet))
rownames(results) = cid(sdfSet)
results
}
fingerprintOB <- function(sdfSet,fingerprintName){
.ensureOB()
x = fingerprint_OB(obmol(sdfSet),fingerprintName)
if(is.vector(x)) x= t(as.matrix(x))
fpset = new("FPset",fpma=x,
type=fingerprintName)
cid(fpset) = cid(sdfSet)
fpset
}
smartsSearchOB <- function(sdfset,smartsPattern,uniqueMatches=TRUE){
.ensureOB()
smartsSearch_OB(obmol(sdfset),smartsPattern,uniqueMatches)
}
exactMassOB <- function(sdfset){
.ensureOB()
exactMass_OB(obmol(sdfset))
}
#compounds should be items that can be passed into similarity
maximallyDissimilar <- function(compounds,n,similarity = cmp.similarity) {
dist = function(a,b) 1-similarity(a,b)
if(length(compounds) <= 2)
stop("maximallyDissimilar: compounds must have more than 2 elements")
#pick first selection at random
selected = sample(1:length(compounds),1)
#compute dist between compounds[-selected] and compounds[selected]
minDistsToSelected = sapply(1:length(compounds),
function(i) dist(compounds[[i]],compounds[[selected]]))
for(k in 2:n){
#find candidate farthest from those selected
newPoint = which.max(minDistsToSelected)
selected = c(selected,newPoint)
minDistsToSelected[newPoint] = 0
#update min distances
minDistsToSelected = sapply(1:length(compounds),
function(i)
if(minDistsToSelected[i]==0) 0
else min(minDistsToSelected[i],dist(compounds[i],compounds[newPoint])))
}
compounds[selected]
}
sdf2OBMol<- function(sdfSet){
.ensureOB()
defs = paste(Map(function(x) paste(x,collapse="\n"),
as(as(sdfSet,"SDFstr"),"list")),"\n",
sep="",collapse="" )
forEachMol("SDF",defs,identity,c)
}
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