I'd like to introduce you one of the faces of the project: Alex, the genomics rat idol.
Arguably, Alex is the most popular rat on the internet. For the justification of this strong statement, read on.
Alex was born in May 2000. It's well known that a rat's cuteness reaches maximum at about 3-4 weeks. After this critical time, a pet store rat is less likely to be purchased and may be asked to act as snake food. In Alex's case, she was perilously close to her deadline. Luckily for her, we paid a ransom of $6.99 to the Noah's Ark pet shop in Vancouver. She was on her last cute leg.
From May 2000 Alex spent most of her time hoarding food pellets and riding on shoulders.
Alex liked to bite. And rats only bite hard — they don't nibble. Her contention for this unattractive behaviour was the uncanny similarity between a finger and a pellet of food.
Other than unpredictable bouts of biting (by far the most exciting aspect of her personality), Alex lacked other distinguishing characteristics.
Alex died of a seizure in late 2002. She was buried outside of the Museum of Anthropology. A ratty pair of underwear served as a burial shroud.
And I hope you got that last pun.
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Despite my best efforts at meaningful work, this web page continues to be the most popular of all my online offerings, making for a somewhat embarrassing achievement.
Alex's images consistently show up first in Google's web search for 'rat', 'rat image' and image search for 'rat'.
Finally, Alex appears as the first entry in Google images for 'rat'.
Alex's Public Appearances
More recently, she's appeared on the cover of Ethnologie Francaise (Jan-Mar 2009 issue).
The topic of the issue was the relationship between animals and humans. It is fitting therefore to recount here the relationship I shared with Alex during her sojourn with us.
I was commissioned by Scientific American to create an information graphic that showed how our genomes are more similar to those of the chimp and bonobo than to the gorilla.
I had about 5 x 5 inches of print space to work with. For 4 genomes? No problem. Bring out the Hilbert curve!
To accompany the piece, I will be posting to the Scientific American blog about the process of creating the figure. And to emphasize that the genome is not a blueprint!
Celebrate Pi Approximation Day (July 22nd) with the art of arm waving. This year I take the first 10,000 most accurate approximations (m/n, m=1..10,000) and look at their accuracy.
Our 10th Points of Significance column! Continuing with our previous discussion about comparative experiments, we introduce ANOVA and blocking. Although this column appears to introduce two new concepts (ANOVA and blocking), you've seen both before, though under a different guise.
If you know the t-test you've already applied analysis of variance (ANOVA), though you probably didn't realize it. In ANOVA we ask whether the variation within our samples is compatible with the variation between our samples (sample means). If the samples don't all have the same mean then we expect the latter to be larger. The ANOVA test statistic (F) assigns significance to the ratio of these two quantities. When we only have two-samples and apply the t-test, t2 = F.
ANOVA naturally incorporates and partitions sources of variation—the effects of variables on the system are determined based on the amount of variation they contribute to the total variation in the data. If this contribution is large, we say that the variation can be "explained" by the variable and infer an effect.
We discuss how data collection can be organized using a randomized complete block design to account for sources of uncertainty in the experiment. This process is called blocking because we are blocking the variation from a known source of uncertainty from interfering with our measurements. You've already seen blocking in the paired t-test example, in which the subject (or experimental unit) was the block.
We've worked hard to bring you 20 pages of statistics primers (though it feels more like 200!). The column is taking a month off in August, as we shrink our error bars.
Krzywinski, M. & Altman, N. (2014) Points of Significance: Analysis of Variance (ANOVA) and Blocking Nature Methods 11:699-700.
Krzywinski, M. & Altman, N. (2014) Points of Significance: Designing Comparative Experiments Nature Methods 11:597-598.
Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I — t-tests Nature Methods 11:215-216.
Krzywinski, M. & Altman, N. (2013) Points of Significance: Significance, P values and t-tests Nature Methods 10:1041-1042.