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Trying to squeeze sense out of chemical data

Archive for the ‘R’ tag

Accessing High Content Data from R

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Over the last few months I’ve been getting involved in the informatics & data mining aspects of high content screening. While I haven’t gotten into image analysis itself (there’s a ton of good code and tools already out there), I’ve been focusing on managing image data and meta-data and asking interesting questions of the voluminuous, high-dimensional data that is generated by these techniques.

One of our platforms is ImageXpress from Molecular Devices, which stores images in a file-based image store and meta data and numerical image features in an Oracle database. While they do provide an API to interact with the database it’s a Windows only DLL. But since much of modeling requires I access the data from R, I needed a more flexible solution.

So, I’ve put together an R package that allows one to access numeric image data (i.e., descriptors) and images themselves. It depends on the ROracle package (which in turns requires an Oracle client installation).

Currently the functionality is relatively limited, focusing on my common tasks. Thus for example, given assay plate barcodes, we can retrieve the assay ids that the plate is associated with and then for a given assay, obtain the cell-level image parameter data (or optionally, aggregate it to well-level data). This task is easily parallelizable – in fact when processing a high content RNAi screen, I make use of snow to speed up the data access and processing of 50 plates.

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library(ncgchcs)
con <- get.connection(user='foo', passwd='bar', sid='baz')
plate.barcode <- 'XYZ1023'
plate.id <- get.plates(con, plate.barcode)

## multiple analyses could be run on the same plate - we need
## to get the correct one (MX uses 'assay' to refer to an analysis run)
## so we first get details of analyses without retrieving the actual data
details <- get.assay.by.barcode(con, barcode=plate.barcode, dry=TRUE)
details <- subset(ret, PLATE_ID == plate.id & SETTINGS_NAME == assay.name)
assay.id <- details$ASSAY_ID

## finally, get the analysis data, using median to aggregate cell-level data
hcs.data <-  get.assay(con, assay.id, aggregate.func=median, verbose=FALSE, na.rm=TRUE)

Alternatively, given a plate id (this is the internal MetaXpress plate id) and a well location, one can obtain the path to the relevant image(s). With the images in hand, you could use EBImage to perform image processing entirely in R.

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library(ncgchcs)
## will want to set IMG.STORE.LOC to point to your image store
con <- get.connection(user='foo', passwd='bar', sid='baz')
plate.barcode <- 'XYZ1023'
plate.id <- get.plates(con, plate.barcode)
get.image.path(con, plate.id, 4, 4) ## get images for all sites & wavelengths

Currently, you cannot get the internal plate id based on the user assigned plate name (which is usually different from the barcode). Also the documentation is non-existant, so you need to explore the package to learn the functions. If there’s interest I’ll put in Rd pages down the line. As a side note, we also have a Java interface to the MetaXpress database that is being used to drive a REST interface to make our imaging data accessible via the web.

Of course, this is all specific to the ImageXpress platform – we have others such as InCell and Acumen. To have a comprehensive solution for all our imaging, I’m looking at the OME infrastructure as a means of, at the very least, have a unified interface to the images and their meta data.

Written by Rajarshi Guha

May 27th, 2011 at 5:01 am

Posted in software,Uncategorized

Tagged with , , ,

Similarity Matrices in Parallel

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Today I got an email asking whether it’d be possible to speed up a fingerprint similarity matrix calculation in R. Now, pairwise similarity matrix calculations (whether they’re for molecules or sequences or anything else) are by definition quadratic in nature. So performing these calculations for large collections aren’t always feasible – in many cases, it’s worthwhile to rethink the problem.

But for those situations where you do need to evaluate it, a simple way to parallelize the calculation is to evaluate the similarity of each molecule with all the rest in parallel. This means each process/thread must have access to the entire set of fingerprints. So again, for very large collections, this is not always practical. However, for small collections parallel evaluation can lead to speed ups.

The fingerprint package provides a method to directly get the similarity matrix for a set of fingerprints, but this is implemented in interpreted R so is not very fast. Given a list of fingerprints, a manual evaluation of the similarity matrix can be done using nested lapply’s:

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library(fingerprint)
sims <- lapply(fps, function(x) {
  unlist(lapply(fps, function(y) distance(x,y)))
})

For 1012 fingerprints, this takes 286s on my Macbook Pro (4GB, 2.4 GHz). Using snow, we can convert this to a parallel version, which takes 172s on two cores:

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library(fingerprint)
library(snow)
cl <- makeCluster(4, type = "SOCK")
clusterEvalQ(cl, library(fingerprint))
clusterExport(cl, "fps")
sim <- parLapply(cl, fps, function(x) {
  unlist(lapply(fps, function(y) distance(x,y)))
})

Written by Rajarshi Guha

December 2nd, 2010 at 1:03 am

Inserting 2D Depictions into R Plots

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Recent versions of rcdk allow you to insert images of chemical structures into R plots, via the view.image.2d and rasterImage functions. One problem with the latter function is that the 2D structure image must be located in plot units, rather than pixel units. Paul Murrell suggested an easy way to insert the raster image into the plot region, maintaining the  native resolution of the image:

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library(rcdk)
m <- parse.smiles("O=C(C1=CC=CC=C1)C1=CC=CC=C1")[[1]]
img <- view.image.2d(m, 200,200)
plot(10:1, pch=19)

## Position the depiction at the lower left corner
dpi <- (par("cra")/par("cin"))[1]
usr <- par("usr")
xl <- usr[1]
yb <- usr[3]
xr <- xl + xinch(200/dpi)
yt <- yb + yinch(200/dpi)

rasterImage(img, xl,yb, xr,yt)

Written by Rajarshi Guha

November 20th, 2010 at 5:09 pm

Posted in cheminformatics,software

Tagged with , ,

Updates to R Packages

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I’ve uploaded a new version of fingerprint (v 3.4) which now supports feature fingerprints – fingerprints that are represented as variable length vectors of numbers or strings. An example would be circular fingerprints. Now, when reading fingerprints you have to indicate whether you’re loading binary fingerprints or not (via the binary argument in fp.read). A new line parser function (ecfp.lf) is provided to load these types of files, though it’s trivial to write your own. Similarity can be evaluated between feature fingerprints in the usual manner, but the metrics are restricted to Tanimoto and Dice. A function is also available to convert a collection of feature fingerprints into a set of fixed length binary fingerprints (featvec.to.binaryfp) as described here.

New versions of rcdk (v 3.0.4) and rcdklibs (v 1.3.6.3) have also been uploaded to CRAN. These releases are based on todays CDK 1.4.x branch and resolve a number of bugs and add some new features

  • Correct formula generation
  • Correct handling of SD tags whose values are just white space
  • Proper generation of Murcko frameworks when molecule objects are requested
  • 3 new descriptors – FMF, acidic group count, basic group count

Written by Rajarshi Guha

October 22nd, 2010 at 1:58 am

Posted in cheminformatics

Tagged with , ,

Working with Sequences in R

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I’ve been working on some RNAi projects and part of that involved generating descriptors for sequences. It turns out that the Biostrings package is very handy and high performance. So, our database contains a catalog for an siRNA library with ~ 27,000 target DNA sequences. To get at the siRNA sequence, we need to convert the DNA to RNA and then take the complement of the RNA sequence. Obviously, you could a write a function to do the transcription step and the complement step, but the Biostrings package already handles that. So I naively tried

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seqs <- get_sequences_from_db()
seqs <- sapply(seqs, function(x) {
  as.character(complement(RNAString(DNAString(x))))
})

but for the 27,000 sequences it took longer than 5 minutes. I then came across the XStringSet class and it’s subclasses, DNAStringSet and RNAStringSet. Using this method got me the siRNA sequences in less than a second.

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seqs <- get_sequences_from_db()
seqs <- as.character(complement(RNAStringSet(DNAStringSet(seqs))))

A slightly contrived example shows the performance improvement

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x <- sapply(1:1000, function(x) {
    paste(sample(c('A', 'T', 'C', 'G'), 21, replace=TRUE), collapse='')
})
system.time(y <- as.character(complement(RNAStringSet(DNAStringSet(x)))))
system.time(y <- sapply(x, function(z) as.character(complement(RNAString(DNAString(z))) )))

Ideally, my descriptor code would also operate directly on a RNAString object, rather than requiring a character object

Written by Rajarshi Guha

October 20th, 2010 at 10:11 pm

Posted in bioinformatics,software

Tagged with , ,