Many experts believe that ageing can be delayed, this is one of the main goals of the the Institute of Healthy Ageing at University College London. I will present the results of my lifespan-extension research where we integrated publicly available genes databases in order to identify ageing related genes. I will show what challenges we met and what we have learned about the process of ageing. Ageing is one of the fundamental mysteries in biology and many scientists are starting to study this fascinating process. I am part of the research group led by Dr Eugene Schuster at UCL Institute of Healthy Ageing. We experiment with Drosophila and Caenorhabditis elegans by modifying their genes in order to create long-lived mutants. The results of our experiments are quantified using high-throughput microarray analysis. Finally we apply information technology in order to understand how the ageing process works. I will show how we mine microarrays data in order to find the connections between thousands of genes and how we identify candidates for ageing genes. We are interested in building a better understanding of genes functions by harnessing the large quantity of experimental microarray data in the public databases. Our hope is that after understanding the ageing process in simpler organisms we will be able to apply this knowledge in humans. Cross-referencing expressions levels in thousands of genes and hundreds of experiments turned out to be a computationally challenging problem but Hadoop and Amazon cloud came to our rescue. In this talk I will present a case study based on our use of R with Amazon Elastic MapReduce and will give background on our bioinformatics challenges. These slides were presented at ApacheCon Europe 2012: http://www.apachecon.eu/schedule/presentation/3/

1. Extending lifespan
with R and Hadoop
Radek Maciaszek
Founder of DataMine Lab, CTO
Ad4Game, studying towards
PhD in Bioinformatics at UCL

2. Agenda
● Project background
● Parallel computing in R
● Hadoop + R
● Future work (Storm)
● Results and summary
2

3. Project background
● Lifespan extension - project at UCL during MSc in
Bioinformatics
● Bioinformatics – computer science in biology (DNA,
Proteins, Drug discovery, etc.)
● Institute of Healthy Ageing at UCL – lifespan is a king.
Dozens of scientists, dedicated journals.
●
Ageing is a complex process or is it? C. Elegans (2x by
a single gene DAF-2, 10x).
● Goal of the project: find genes responsible for ageing
3
Caenorhabditis Elegans

4. Primer in Bioinformatics
● Central dogma of molecular biology
● Cell (OS+3D), Gene (Program), TF (head on HDD)
● How to find ageing genes (such as DAF-2)?
4
Images: Wikipedia

5. RNA microarray
DAF-2 pathway in C. elegans
Source: Partridge & Gems, 2002 Source: Staal et al, 2003 5

6. Goal: raw data → network
Genes Network
● Pairwise comparisons of
10k x 10k genes +
clustering
100 x 100 x 50 x 10
(~10k genes)
6

7. Why R?
● Incredibly powerful for data science with
big data
● Functional, scripting programming
language with many packages.
● Popular in mathematics, bioinformatics,
finance, social science and more.
● TechCrunch lists R as trendy technology for
BigData.
● Designed by statisticians for statisticians
7

8. R example
K-Means clustering
require(graphics)
x <- rbind(matrix(rnorm(100, sd = 0.3),
ncol = 2),
matrix(rnorm(100, mean = 1,
sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
(cl <- kmeans(x, 2))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:2,
pch = 8, cex=2)
8

9. R limitations & Hadoop
● 10k x 10k (100MM) Fisher exact
correlations is slow
● Memory allocation is a common problem
● Single-threaded
● Hadoop integration:
– Hadoop Streaming
– Rhipe: http://ml.stat.purdue.edu/rhipe/
– Segue: http://code.google.com/p/segue/
9

10. Scaling R
● Explicit
– snow, parallel, foreach
● Implicit
– multicore (2.14.0)
● Hadoop
– RHIPE, rmr, Segue, RHadoop
● Storage
– rhbase, rredis, Rcassandra, rhdfs
10

11. R and Hadoop
● Streaming API (low level)
mapper.R
#!/usr/bin/env Rscript
in <- file(“stdin”, “r”)
while (TRUE) {
lineStr <- readLines(in, n=1)
line <- unlist(strsplit(line, “,”))
ret = expensiveCalculations(line)
cat(data, “n”, sep=””)
}
close(in)
jar hadoop-streaming-*.jar –input data.csv –output data.out –mapper mapper.R
11

12. RHIPE
● Can use with your Hadoop cluster
● Write mappers/reduces using R only
map <- expression({
z <- f <- table(unlist(strsplit(unlist(
rhmr(map=map,reduce=reduce, map.values)," ")))
inout=c("text","sequence") n <- names(f)
,ifolder=filename p <- as.numeric(f)
,ofolder=sprintf("%s- sapply(seq_along(n),function(r)
out",filename)) rhcollect(n[r],p[r]))
})
job.result <-
rhstatus(rhex(z,async=TRUE), reduce <- expression(
mon.sec=2) pre={ total <- 0},
reduce = { total <-
total+sum(unlist(reduce.values)) },
post = { rhcollect(reduce.key,total) }
)
12
Example from Rhipe Wiki

13. 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()
● Parallelization in 2 lines of code!
● Allowed us to speed up calculations down
to 2h with the use of 16 servers
13

14. Segue workflow (emrlapply)
14

15. lapply()
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.
15

16. Segue in a cluster
> 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)
}
> # pearson.cor <- lapply(probes, AnalysePearsonCorelation)
Moving to the cloud in 3 lines of code!
16

17. Segue in a cluster
> 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)
}
> # 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)
17

18. R + HBase
library(rhbase)
hb.init(serialize="raw")
#create new table
hb.new.table("mytable", "x","y","z",opts=list(y=list(compression='GZ')))
#insert some values into the table
hb.insert("mytable",list( list(1,c("x","y","z"),list("apple","berry","cherry"))))
rows<-hb.scan.ex("mytable",filterstring="ValueFilter(=,'substring:ber')")
rows$get()
https://github.com/RevolutionAnalytics/RHadoop/wiki/rhbase
18

19. Discovering genes
Topomaps of clustered genes
This work was based on:A Gene Expression Map for
Caenorhabditis elegans, Stuart K. Kim, et al., Science 293,
2087 (2001)
19

20. Genes clusters
Clusters based on Fisher
exactpairwise genes comparisons
Green lines represent random probes
Red lines represent up-regulated probes
Blue lines are down-regulated probes
(in daf-2 vs daf-2;daf-16 experiment) 20

21. Genes networks
Network created with Cytoscape, platform
for complex network analysis:
http://www.cytoscape.org/
21

22. Future work - real time R
● Hadoop has high throughput but for small
tasks is slow. It is not good for continuous
calculations.
● A possible solution is to use Storm
● Storm multilang can be used with any
language, including R
22

23. Storm R
Storm may be easily integrated with
third party languages and databases:
● Java
● Python
● Ruby
● Redis
● Hbase
● Cassandra
Image source: Storm github wiki
23

24. Storm R
source("storm.R")
initialize <- function()
{
emitBolt(list("bolt initializing"))
}
process <- function(tup)
{
word <- tup$tuple
rand <- runif(1)
if (rand < 0.75) {
emitBolt(list(word + "lalala"))
} else {
log(word + " randomly skipped!")
}
}
boltRun(process, initialize)
https://github.com/rathko/storm 24

25. Summary
● It’s easy to scale R using Hadoop.
● R is not only great for statistics, it is a versatile
programming language.
● Is ageing a disease? Are we all going to live very long
lives?
25

26. Questions?
● References:
http://hadoop.apache.org/
http://hbase.apache.org/
http://code.google.com/p/segue/
http://www.datadr.org/
https://github.com/RevolutionAnalytics/
https://github.com/rathko/storm
26