Tango is a sad thought that is danced.think & dance

# making poetry out of spam is fun

Bioinformatics and Genome Analysis Course. Izmir International Biomedicine and Genome Institute, Izmir, Turkey. May 2–14, 2016

# Dummer — Like Nothing Else

The Hummer font is a slightly modified Antique Olive Nord. The Like Nothing Else tag line is Trade Gothic. Both have character widths increased to 110-120% and individually adjusted kerning. Get the Illustrator CS5 file for both logos.

Hummer logo. (EPS, PNG)
Dummer logo. (EPS, PNG)

This project might give you the impression that I don't like Hummers. You'd be right.

It could be worse. But not by much. (zoom)
It could be worse. But not by much. (zoom)
It could be worse. But not by much. (zoom)

## update

The Maurauder. Over 25,000 lb — five times what an H3 weighs. Enough said.

There is always someone with a bigger one. (Manufacturer's page.)

## Dummer - Like Nothing Else

Hummers are a cultural equivalent of a toxic warning label and have the same effect on me as bug spray on mosquitoes.

I am not the first one to satirize this automotive aberration, so there's some hope.

Dummer. Like Nothing Else. (New York Times — Laugh Lines)

GM's advertisement images require no modification for the satire, which makes it all that much better.

Dumb and Dumber. (New York Times — Laugh Lines)

I could have just as well used the Lincoln Navigator or Cadillac Escalade, but they don't embody the superlative like the Hummer.

The Hummer brand proved itself to be aesthetically, rationally and economically unsustainable and collapsed after a failed attempt to sell it to China. There continues to be a robust market for used Hummers. Let the farce continue.

## I'm hated

It delights me that this project produced my first hate mail.

I want to meet Doug and give him a hug for adding another dimension to this project.

## I'm loved

The images got picked up by the New York Times laughlines blog, which drew a couple of fan mails.

But neither made me feel as good as Doug's email.

## Dummer Images

Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)
Dummer. Like nothing else. (zoom)

# Gene Volume Control

Thu 11-06-2015

I was commissioned by Scientific American to create an information graphic based on Figure 9 in the landmark Nature Integrative analysis of 111 reference human epigenomes paper.

The original figure details the relationships between more than 100 sequenced epigenomes and genetic traits, including disease like Crohn's and Alzheimer's. These relationships were shown as a heatmap in which the epigenome-trait cell depicted the P value associated with tissue-specific H3K4me1 epigenetic modification in regions of the genome associated with the trait.

Figure 9 from Integrative analysis of 111 reference human epigenomes (Nature (2015) 518 317–330). (details)

As much as I distrust network diagrams, in this case this was the right way to show the data. The network was meticulously laid out by hand to draw attention to the layered groups of diseases of traits.

Network diagram redesign of the heatmap for a select set of traits. Only relationships with –log P > 3.9 are displayed. Appears on Graphic Science page in June 2015 issue of Scientific American. (details)

This was my second information graphic for the Graphic Science page. Last year, I illustrated the extent of differences in the gene sequence of humans, Denisovans, chimps and gorillas.

# Sampling distributions and the bootstrap

Thu 11-06-2015

The bootstrap is a computational method that simulates new sample from observed data. These simulated samples can be used to determine how estimates from replicate experiments might be distributed and answer questions about precision and bias.

Nature Methods Points of Significance column: Sampling distributions and the bootstrap. (read)

We discuss both parametric and non-parametric bootstrap. In the former, observed data are fit to a model and then new samples are drawn using the model. In the latter, no model assumption is made and simulated samples are drawn with replacement from the observed data.

Kulesa, A., Krzywinski, M., Blainey, P. & Altman, N (2015) Points of Significance: Sampling distributions and the bootstrap Nature Methods 12:477-478.

Krzywinski, M. & Altman, N. (2013) Points of Significance: Importance of being uncertain. Nature Methods 10:809-810.

# Bayesian statistics

Thu 30-04-2015

Building on last month's column about Bayes' Theorem, we introduce Bayesian inference and contrast it to frequentist inference.

Given a hypothesis and a model, the frequentist calculates the probability of different data generated by the model, P(data|model). When this probability to obtain the observed data from the model is small (e.g. alpha = 0.05), the frequentist rejects the hypothesis.

Nature Methods Points of Significance column: Bayesian Statistics. (read)

In contrast, the Bayesian makes direct probability statements about the model by calculating P(model|data). In other words, given the observed data, the probability that the model is correct. With this approach it is possible to relate the probability of different models to identify one that is most compatible with the data.

The Bayesian approach is actually more intuitive. From the frequentist point of view, the probability used to assess the veracity of a hypothesis, P(data|model), commonly referred to as the P value, does not help us determine the probability that the model is correct. In fact, the P value is commonly misinterpreted as the probability that the hypothesis is right. This is the so-called "prosecutor's fallacy", which confuses the two conditional probabilities P(data|model) for P(model|data). It is the latter quantity that is more directly useful and calculated by the Bayesian.

Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayes' Theorem Nature Methods 12:277-278.

Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayes' Theorem Nature Methods 12:277-278.

# Bayes' Theorem

Wed 22-04-2015

In our first column on Bayesian statistics, we introduce conditional probabilities and Bayes' theorem

P(B|A) = P(A|B) × P(B) / P(A)

This relationship between conditional probabilities P(B|A) and P(A|B) is central in Bayesian statistics. We illustrate how Bayes' theorem can be used to quickly calculate useful probabilities that are more difficult to conceptualize within a frequentist framework.

Nature Methods Points of Significance column: Bayes' Theorem. (read)

Using Bayes' theorem, we can incorporate our beliefs and prior experience about a system and update it when data are collected.

Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of Significance: Bayes' Theorem Nature Methods 12:277-278.