Why Javascript?

Karl Broman

Biostatistics & Medical Informatics, University of Wisconsin – Madison

kbroman.org github.com/kbroman @kwbroman

slides: bit.ly/jsm_2019

It's fun!

http://mbostock.github.io/d3/talk/20111018/collision.html

I’m using D3, a javascript library for data visualization. (D3 stands for “data-driven documents.”)

This is one of the images that convinced me that I wanted to learn D3. There are lots of additional examples at the D3 gallery.

There’s a ton to learn, and it’s rather forbidding (and much of it is dull).

My most common mistake is to forget to include .enter(). No error is given, but nothing shows up in the plot.

high-dimensional data

code | data

exploratory data analysis

Outliers can be easily identified, with the right interactive plot. In R, you can use identify(), but who remembers how to do it? Usually, I’ll say, "Hmm...I want to figure out which point has weight 1 > 30 and weight 2 < 25."

people are busy

code | data

reports to collaborators

I write a lot of reports to collaborators, describing my analysis results and conclusions. It’s hard to anticipate all possible questions that a collaborator might have. (“What gene is that?”) And you can only show so many pictures.

Including interactive graphs in a report allows my collaborators to answer a lot of their own questions on their own and to more fully explore their own data. And a single interactive graph can take the place of thousands of static ones.

Here we consider a phenotype measured over time, and perform QTL analysis with each time point individually, to seek genetic loci that affect the response.

The top-left plot is a “heat map” of the LOD scores for each time point at each genomic position. LOD scores are also colored to indicate the sign of the QTL effect, with red indicating that BB lines have larger phenotype values and blue indicating that AA lines have larger phenotype values. We consider only those (position, time) pairs with LOD > 1.

When you hover over a point in the top-left plot, the LOD curves for the corresponding time are shown below, and the phenotype averages and estimated QTL effect (across time) for the corresponding genomic position are shown to the right.

Click on a point to show pointwise confidence bands on the QTL effect (± 2 SE). (We require a mouse click, as otherwise the graph was painfully slow to refresh.)

code | data

teaching

code | data

Summary

Javascript is fun!
High-dimensional data
Eases exploratory data analysis
Encourages exploration
Simplifies reports
Helps teach concepts

Slides: bit.ly/jsm_2019