The document discusses a project on lifespan extension utilizing R and Hadoop for bioinformatics research at UCL. It highlights the use of R for data science in gene analysis, the challenges of scalability, and the integration of Hadoop for handling large datasets. The future scope includes exploring real-time processing with Storm to enhance computational efficiency.

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
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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
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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)?
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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)
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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
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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)
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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/
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10. Scaling R
● Explicit
– snow, parallel, foreach
● Implicit
– multicore (2.14.0)
● Hadoop
– RHIPE, rmr, Segue, RHadoop
● Storage
– rhbase, rredis, Rcassandra, rhdfs
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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
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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) }
)
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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
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14. Segue workflow (emrlapply)
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15. lapply()
m <- list(a = 1:10, b = exp(-3:3))
lapply(m, mean)$a
[1] 5.5
$b
[1] 4.535125
lapply(X, FUN)
returns a list of the same length as X, each element of which is
the result of applying FUN to the corresponding element of X.
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16. Segue in a cluster
> AnalysePearsonCorelation <- function(probe) {
A.vector <- experiments.matrix[probe,]
p.values <- c()
for(probe.name in rownames(experiments.matrix)) {
B.vector <- experiments.matrix[probe.name,]
p.values <- c(p.values, cor.test(A.vector, B.vector)$p.value)
}
return (p.values)
}
> # pearson.cor <- lapply(probes, AnalysePearsonCorelation)
Moving to the cloud in 3 lines of code!
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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)$p.value)
}
return (p.values)
}
> # pearson.cor <- lapply(probes, AnalysePearsonCorelation)
> myCluster <- createCluster(numInstances=5,
masterBidPrice="0.68”, slaveBidPrice="0.68”,
masterInstanceType=”c1.xlarge”,
slaveInstanceType=”c1.xlarge”, copy.image=TRUE)
> pearson.cor <- emrlapply(myCluster, probes,
AnalysePearsonCorelation)
> stopCluster(myCluster)
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18. R + HBase
library(rhbase)
hb.init(serialize="raw")
#create new table
hb.new.table("mytable", "x","y","z",opts=list(y=list(compression='GZ')))
#insert some values into the table
hb.insert("mytable",list( list(1,c("x","y","z"),list("apple","berry","cherry"))))
rows<-hb.scan.ex("mytable",filterstring="ValueFilter(=,'substring:ber')")
rows$get()
https://github.com/RevolutionAnalytics/RHadoop/wiki/rhbase
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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)
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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/
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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
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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
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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?
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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
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