This document discusses using R analytics in the cloud. It provides an introduction to bioinformatics and analyzing gene expression data from C. elegans to study aging. It explains that R is popular for bioinformatics but limited to single machines. Hadoop and tools like Segue allow scaling R to the cloud. Segue creates AWS clusters and implements lapply for distributed computing. An example analyzes gene correlation at scale using Segue on AWS. The goal is to discover genes responsible for aging through clustered gene expression maps.

1. R Analytics
in the Cloud

2. Introduction
 Radek Maciaszek
 DataMine Lab (www.dataminelab.com) - Data mining,
business intelligence and data warehouse
consultancy.
 MSc in Bioinformatics at Birkbeck, University of
London.
 Project at UCL Institute of Healthy Ageing under
supervision of Dr Eugene Schuster.
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3. Primer in Bioinformatics
 Bioinformatics - applying computer
science to biology (DNA, Proteins,
Drug discovery, etc)
 Ageing strategy – solve it in simple
organism and apply findings to more
complex organisms (i.e. humans).
 Goal: find genes responsible for ageing
Caenorhabditis Elegans
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4. Central dogma of molecular biology
Genes are encoded
by the DNA. Microarray
(100 x 100)
• Database of 50 curated experiments.
• 10k genes compare to each other
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5. Why R?
 Very popular in bioinformatics
 Functional, scripting programming
language
 Swiss-army knife for statistician
 Designed by statisticians for
statisticians
 Lots of ready to use packages (CRAN)
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6. R limitations & Hadoop
 Data needs to fit in the memory
 Single-threaded
 Hadoop integration:
 Hadoop Streaming
 Rhipe: http://ml.stat.purdue.edu/rhipe/
 Segue: http://code.google.com/p/segue/
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7. Segue
 Works with Amazon Elastic MapReduce.
 Creates a cluster for you.
 Designed for Big Computations (rather than
Big Data)
 Implements a cloud version of lapply()
function.
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8. Segue workflow (emrlapply)
List (local)
List (remote)
Amazon AWS 8

9. R very quick example
m <- list(a = 1:10, b = exp(-3:3))
lapply(m, mean)
$a
[1] 5.5
$b
[1] 4.535125
lapply(X, FUN) returns a list of the same length as X,
each element of which is the result of applying FUN to
the corresponding element of X.
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10. Segue – large scale example
> AnalysePearsonCorelation <- function(probe) {
A.vector <- experiments.matrix[probe,]
p.values <- c()
for(probe.name in rownames(experiments.matrix)) {
B.vector <- experiments.matrix[probe.name,]
p.values <- c(p.values, cor.test(A.vector, B.vector)$p.value)
}
return (p.values)
}
RNA Probes
> pearson.cor <- lapply(probes, AnalysePearsonCorelation)
Moving to the cloud in 3 lines of code!
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11. Segue – large scale example
> AnalysePearsonCorelation <- function(probe) {
A.vector <- experiments.matrix[probe,]
p.values <- c()
for(probe.name in rownames(experiments.matrix)) {
B.vector <- experiments.matrix[probe.name,]
p.values <- c(p.values, cor.test(A.vector, B.vector)$p.value)
}
return (p.values)
}
RNA Probes
> # pearson.cor <- lapply(probes, AnalysePearsonCorelation)
> myCluster <- createCluster(numInstances=5, masterBidPrice="0.68”,
slaveBidPrice="0.68”, masterInstanceType=”c1.xlarge”,
slaveInstanceType=”c1.xlarge”, copy.image=TRUE)
> pearson.cor <- emrlapply(myCluster, probes, AnalysePearsonCorelation)
> stopCluster(myCluster) 11

12. Discovering genes
Topomaps of clustered genes
This work was based on a similar approach to:
A Gene Expression Map for Caenorhabditis elegans, Stuart K. Kim, et al., 12
Science 293, 2087 (2001)

13. Conclusions
 R is great for statistics.
 It’s easy to scale up R using Segue.
 We are all going to live very long.
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14. Thanks!
 Questions?
 References:
http://code.google.com/r/radek-segue/
http://www.dataminelab.com
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