The Dream 8 Challenges

The 8th iteration of the DREAM Challenges are underway.

DREAM is something like the Kaggle of computational biology with an open science bent. Participating teams apply machine learning and statistical modeling methods to biological problems, competing to achieve the best predictive accuracy.

This year's three challenges focus on reverse engineering cancer, toxicology and the kinetics of the cell.

• HPN-DREAM Breast Cancer Network Inference Challenge
  Infer the signaling networks in breast cancer cell lines
• NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge
  Predict individual response to environmental and pharmaceutical chemicals
• The Whole-Cell Parameter Estimation DREAM Challenge
  Infer the kinetic parameters underlying biological processes in whole cell models

Sage Bionetworks (my employer) has teamed up with DREAM to offer our Synapse platform as an integral part of the challenges. Synapse is targeted at providing a platform for hosting data analysis projects, much like GitHub is a platform for software development projects.

My own part in Synapse is on the Python client and a bit on the R client. I expect to get totally pummeled by bug reports once participation in the challenges really gets going.

Open data, collaborative innovation and reproducible science

The goal of Synapse is to enable scientists to combine data, source code, provenance, prose and figures to tell a story with data. The emphasis is on open data and collaboration, but full access control and governance for restricted access is built in.

In contrast to Kaggle, the DREAM Challenges are run in the spirit of open science. Winning models become part of the scientific record rather than the intellectual property of the organizers. Sharing code and building on other contestant's models is encouraged in with hopes of forming networks of collaborative innovation.

Aside from lively competition, these challenges are a great way to compare the virtues of various methods on a standardized problem. Synapse is aiming to become an environment for hosting standard open data sets and documenting reproducible methods for deriving models from them.

Winning methods will be presented at the RECOMB/ISCB Conference in Toronto this fall.

So, if you want to sharpen your data science chops on some important open biological problems, check out the DREAM8 challenges.

More on DREAM, Sage Bionetworks, and Challenges

• May the Best Model Win
• Wisdom of crowds for robust gene network inference
• Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling.
• Metcalfe's law and the biology information commons
• Synapse – a Kaggle for molecular medicine?

Big Data Book

Big Data: A Revolution That Will Transform How We Live, Work, and Think asks what happens when data goes from scarce to abundant. The book expands on The data deluge, a report appearing in the Economist in early 2010, providing a concise overview of a topic that is at the same time over-hyped and also genuinely transformative.

Authors Kenneth Cukier, Data Editor at the Economist, and Viktor Mayer-Schonberger, Oxford professor of public policy, look at big data trends through a business oriented lens.

Their book identifies two important shifts in thinking as key to dealing with big data: comfort with the messy uncertainty of probability and what the book refers to as correlations over causality. This just means that some tasks are better suited to a correlative or pattern matching approach rather than modeling from first principles. No one drives a car by thinking about physics.

In domains such as natural language and biology we'd like to know the underlying theory, but don't. At least, not yet. Where theory is insufficiently developed to directly answer pressing questions, a correlative, probabilistic approach is the best we can do. The merits of this concession to incomplete information can be debated, and was by Norvig and Chomsky. But, empirical disciplines like medicine and engineering rely on it - let alone the really messy worlds of economics and politics. It's often necessary to act in absence of mechanistic understanding.

As data goes from a scarcity to abundance, judgment and interpretation remain valuable. What changes is that you don't necessarily have to formulate a question and only then collect data that may help answer it. Another strategy becomes viable - collect a trove of data first and then interrogate it. That may bother hypothesis-driven traditionalists, but once you have a high-throughput data source - a DNA sequencer, a sensor networks or an e-commerce site - it makes sense to ask yourself what questions you can ask of that data.

Datafication

One industry or discipline after another has undergone the transition from information scarcity to super-abundance. One of the last hold-outs is medicine, where the potential impact is huge. Internet companies are accumulating vast data hordes streaming in through social networks and smartphones. The process has something of the character of a land-rush. Given the economies of scale, each category has its dominant player.

The dark side

As for obligatory dark-side of big data, the book worries about "the prospect of being penalized by for things we haven't even done yet, based on big data's ability to predict our future behavior."

While this won't keep me up at night, technology is not necessarily the liberating force that we may hope it is, nor is it necessarily a tool of Orwellian control. Certain technologies lend themselves more readily one way or the other.

A more pedestrian "threat to human agency" comes from marketers and political consultants equipped with data feeds, powerful machines and brainy statisticians, and able to manipulate people into consuming useless junk and acting against their own interests. Given detailed individual profiles and constant feedback on the effectiveness of targeted marketing, the puppet masters are likely to keep getting better at pulling our strings.

Data governance

To safeguard privacy and ethical uses of data, the book advocates a self-policing professional association of data scientists similar to those existing for accountants, lawyers and brokers. Personally, I'm a skeptical that these organizations have much in the way of teeth. An informed public debate might be a healthier option.

Bolder thinking in this area comes from Harvard's cyber-law group. Their VRM (for vendor relationship management) project proposes a protocol by which individuals and vendors can specify the terms under which they are willing to share data. Software can automate the negotiation and present options where terms are compatible. Precedents for these ideas can be found in finance where bonds and options are standardized contracts and can thus be fluidly traded.

The new oil?

Land, labor and capital are the classic factors of production - the primary inputs to the economy - or so they teach in Econ 101. Technology certainly belongs on that list and data does as well. The original robber barons sat in castles overlooking the Rhine or the Danube taxing the flow of goods and travelers around Europe. The term was later applied to giant industrial firms built on other flows: oil, rail and steel. Companies like Google, Facebook and Amazon extract wealth from a new flow: that of data. It's time to think of data as another factor of economic production, a raw material out of which something can be made.

Links, links, links

• Comparing Big Data with the Signal and the Noise
• The Rise of Big DataHow It's Changing the Way We Think About the World By Kenneth Neil Cukier and Viktor Mayer-Schoenberger published in Foreign Affairs May/June 2013
• How Big Data Changes Our Relationship With Information by Irving Wladawsky-Berger in the WSJ, July 5, 2013
• The Age of Big Data by Steve Lohr published in the NY Times in February of 2012
• Jer Thorpe says, Big Data Is Not the New Oil
• Niccolo Tempini finds that rather than showing how the impact of data-driven innovations will advance the march of humankind, the authors merely present a thin collection of happy-ending business stories.
• Norvig vs. Chomsky and the Fight for the Future of AI
• Doc Searls on the Intention Economy
• Patients Know Best
• Some colleagues are working on data governance for bio-medical research: US-EU Scientific Research Collaborations: Sage Bionetworks’ Experience Navigating the Complex Regulatory Landscape and Consent to Research
• Is ‘Google Flu Trends’ Prescient Or Wrong?

Shiny talk by Joe Cheng

Shiny is a framework work for creating web applications with R. Joe Cheng of RStudio, Inc. presented on Shiny last evening in Zillow's offices 30 stories up in the former WaMu Center. Luckily, the talk was interesting enough to compete with the view of Elliot bay aglow with late evening sunlight streaming through breaks in the clouds over the Olympics.

Shiny is very slick, achieving interactive and pleasant looking web UIs with node.js, websockets and bootstrap under the hood. It's designed on a reactive programming model (like bacon and ember) that eliminates a lot of the boiler-plate code associated with listeners or observers in UI coding.

Shiny comes in two parts, the shiny R package for developing Shiny apps and Shiny server for deploying them. The RStudio company intends to create a paid tier consisting of an enterprise server and a paid hosting service, Glimmer, which is free for now.

Among several demos were a plot of TV Show Rankings over time and a neat integration with Google's Geochart library to map World Bank health nutrition and population statistics. There are also some examples of combining D3 with Shiny (G3Plot).

Possibly, the coolest demo was a tutorial on reactive programming in the form of an R console in a browser. Chunks of code could drag-and-drop around as in "live document" systems like IPython notebooks or Chris Granger's Clojure IDE Light Table.

Links

• Slides and source code are on github in Joe's seattle-meetup repo.
• To learn more, check out the Shiny Tutorial

HiveBio: a DIY biology lab for Seattle

Seattle is one of the few cities with a big biotech industry lacking a community lab space. Katriona Guthrie-Honea and Bergen McMurray are going to fix that by creating a DIY bioscience lab. The Seattle HiveBio Community Lab will be a community supported Do-It-Yourself (DIY) biology hacker-space or maker-space.

Katriona Guthrie-Honea is a student at Ingraham High and an intern at Fred Hutch. Bergen McMurray is a neuroscience student and an alumna of the Allen Brain Institute and Jigsaw Renaissance, a maker-space in Seattle's International District.

Worrying about an "innovational stagnation period" because not enough people are learning and playing with biotech, Guthrie-Honea wants to provide a place where people of all ages can do just that.

Synthetic Biology was founded on the idea of bringing an engineering mindset to biotechnology, with one result being BioBricks, the beginnings of a set of modular components. The iGEM competitions drive education and open community around synthetic biology.

But, one could argue that a standard engineer wouldn't make a centrifuge out of a salad spinner or a ceiling fan. To do that, what you need is a hacker.

I love the idea of bringing the hacker mentality to life sciences. Just like we should all take the lids off our computers and root our phones, we should be hacking the yeast in our beer like mad scientist Belgian monks.

Anticipating a May opening, Guthrie-Honea and McMurray are seeking funding from Microryza, which is like a Kickstarter for science, and a great idea in itself.

Do you love the idea, too? Want to help? Just like Kickstarter, Microryza is a crowdfunding platform. Check out their project and kick in a few bucks.

Playing with earthquake data

This is a little example inspired by Jeff Leek's Data analysis class. Jeff pointed us towards some data from the US Geological Survey on earthquakes. I believe these are the earthquakes for the last 7 days.

According to Nate Silver, analysis of earthquake data has made fools of many. But, that won't stop us. Let's see what we can do with it.

fileUrl <- "http://earthquake.usgs.gov/earthquakes/catalogs/eqs7day-M1.txt"
download.file(fileUrl, destfile = "./data/earthquake_7day.csv", method = "curl")
eq <- read.csv("./data/earthquake_7day.csv")

Let's be good citizens and record the provinance of our data.

attributes(eq)["date.downloaded"] <- date()
attributes(eq)["url"] <- fileUrl
save(eq, file = "earthquakes_7day.RData")

attributes(eq)$date.downloaded
## [1] "Mon Feb  4 20:03:48 2013"

Let's see what they give us.

colnames(eq)

##  [1] "Src"       "Eqid"      "Version"   "Datetime"  "Lat"      
##  [6] "Lon"       "Magnitude" "Depth"     "NST"       "Region"

Something looks a little funny about the distribution of magnitudes. What's up with that bump at magnitude 5? I'm guessing that some detectors are more sensitive than others and the less sensitive ones bottom out at around 5.

hist(eq$Magnitude, main = "Histogram of Earthquake Magnitude", col = "#33669988", 
    border = "#000066")

I'm guessing that the better detectors are on the west coast of the US.

westcoast <- c("Northern California", "Central California", "Southern California",  "Greater Los Angeles area, California", "Santa Monica Bay, California", "Long Valley area, California", "San Francisco Bay area, California", "Lassen Peak area, California", "offshore Northern California", "Washington", "Seattle-Tacoma urban area, Washington", "Puget Sound region, Washington", "Olympic Peninsula, Washington", "Mount St. Helens area, Washington", "San Pablo Bay, California", "Portland urban area, Oregon", "off the coast of Oregon")
hist(eq[eq$Region %in% westcoast, "Magnitude"], main = "Histogram of Earthquake Magnitude - West Coast US", 
    col = "#33669988", border = "#000066")

Now that looks a little smoother. Let's try R's mapping ability. Check out the ring of fire.

library(maps)
map()
points(x = eq$Lon, y = eq$Lat, col = rgb(1, 0, 0, alpha = 0.3), cex = eq$Magnitude * 
    0.5, pch = 19)

Zoom in on the west coast.

map("state", xlim = range(c(-130, -110)), ylim = range(c(25, 50)))
points(x = eq$Lon, y = eq$Lat, col = rgb(1, 0, 0, alpha = 0.3), cex = eq$Magnitude * 
    0.5, pch = 19)

Lucky for us, almost all the recent quakes out here were tiny and the biggest was way off the coast.

summary(eq[eq$Region %in% westcoast, "Magnitude"])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     1.0     1.2     1.4     1.5     1.7     5.3

There are supposed to be active fault lines here in Seattle and you can see a few little ones. Down by Mt. St. Helens, they get a few moderately bigger ones. The only quakes I've felt recently were the contractors doing demolition for remodeling my basement.

The knitr output can also be seen on RPubs: Playing with Earthquake Data and on GitHub cbare/earthquakes.rmd.