Trance opera—Spente le Stellebe dramaticmore quotes

# art is science is art

DNA on 10th — street art, wayfinding and font

# data visualization + art

The BC Cancer Agency’s Personalized Oncogenomics Program (POG) is a clinical research initiative applying genomic sequencing to the diagnosis and treatment of patients with incurable cancers.

# Art of the Personalized Oncogenomics Program

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry.
— Richard Feynman

The design on the posters is being used for the Vancouver Ride to Conquer Cancer cycling jersey. (buy a jersey, tour info)
The POG art shows 545 cases studied over the course of 5 years and is freely available as posters for printing and images for your desktop and presentation slides in both bitmap and PDF formats.
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases grouped by diagnosis (tissue type) and then by similarity within group. (zoom)

## cancer is the difference of differences

As individuals, we all have slightly different genomes. If you compare the genomes of two people, you will find about 3 million base pair differences, which is about 0.1% of the genome.

This variation exists not only within the population but potentially also, to a lesser extent, among our cells, which number around 40 trillion. That's roughly 10,000 cells for each base in your 3 billion base genome. And each has a role to play.

POG cases, by tissue type
n %
Gastrointestinal 141 25

Breast 138 25

Thoracic 57 10

Gynecologic 45 8.3

Soft tissue 44 8.1

Skin 11 2.0

Urologic 8 1.5

Hematologic 7 1.3

Head and neck 6 1.1

Endocrine 5 0.9

Central nervous system 5 0.9

Other 78 14

ALL 545

One consequence of this complexity and variation is that changes in the genome (through mutation or other processes) can have very different effects, depending on both the change and the genome. Cancer is a phenomena in which cells' ability to organize themselves as they divide is altered due to changes in the genome. It is an incredibly complex biological phenomenon—considering all the genomes in the population and all the possible changes that may arise, there is truly an inexhaustible number of ways in which the genome can break.

## classifying cancer

Cancers are classified according to their site of origin, such as lung, breast, liver, or colon. This is a coarse grouping—within each group there are many subtypes with differences in response to treatment and overall behaviour.

## diversities among clinical cases

The design of the POG art highlights the diversity and similarity among cases. The diversity is what makes the study of cancer difficult and the similarities are what makes inference possible.

Each case is represented by three concentric rings. The width of each ring represents the extent to which the case is similar (as measured by correlation) to cancers of the type encoded by the color of the ring (see Methods).

## remixes

In additional to the posters, I've created remixes for your desktop at 4k resolution.

5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)

## Ride to Conquer Cancer — Data-powered, Human-driven

This year, the cyclists in the Ride to Conquer Cancer will not only have the chance to raise money for research (as they've always done) but also do so while wearing data (as they've never done before).

VIEW ALL

# Hola Mundo Cover

Sat 21-09-2019

My cover design for Hola Mundo by Hannah Fry. Published by Blackie Books.

Hola Mundo by Hannah Fry. Cover design is based on my 2013 $\pi$ day art. (read)

Curious how the design was created? Read the full details.

# Markov Chains

Tue 30-07-2019

You can look back there to explain things,
but the explanation disappears.
You'll never find it there.
Things are not explained by the past.
They're explained by what happens now.
—Alan Watts

A Markov chain is a probabilistic model that is used to model how a system changes over time as a series of transitions between states. Each transition is assigned a probability that defines the chance of the system changing from one state to another.

Nature Methods Points of Significance column: Markov Chains. (read)

Together with the states, these transitions probabilities define a stochastic model with the Markov property: transition probabilities only depend on the current state—the future is independent of the past if the present is known.

Once the transition probabilities are defined in matrix form, it is easy to predict the distribution of future states of the system. We cover concepts of aperiodicity, irreducibility, limiting and stationary distributions and absorption.

This column is the first part of a series and pairs particularly well with Alan Watts and Blond:ish.

Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.

# 1-bit zoomable gigapixel maps of Moon, Solar System and Sky

Mon 22-07-2019

Places to go and nobody to see.

Exquisitely detailed maps of places on the Moon, comets and asteroids in the Solar System and stars, deep-sky objects and exoplanets in the northern and southern sky. All maps are zoomable.

3.6 gigapixel map of the near side of the Moon, annotated with 6,733. (details)
100 megapixel and 10 gigapixel map of the Solar System on 20 July 2019, annotated with 758k asteroids, 1.3k comets and all planets and satellites. (details)
100 megapixle and 10 gigapixel map of the Northern Celestial Hemisphere, annotated with 44 million stars, 74,000 deep-sky objects and 3,000 exoplanets. (details)
100 megapixle and 10 gigapixel map of the Southern Celestial Hemisphere, annotated with 69 million stars, 88,000 deep-sky objects and 1000 exoplanets. (details)

# Quantile regression

Sat 01-06-2019
Quantile regression robustly estimates the typical and extreme values of a response.

Quantile regression explores the effect of one or more predictors on quantiles of the response. It can answer questions such as "What is the weight of 90% of individuals of a given height?"

Nature Methods Points of Significance column: Quantile regression. (read)

Unlike in traditional mean regression methods, no assumptions about the distribution of the response are required, which makes it practical, robust and amenable to skewed distributions.

Quantile regression is also very useful when extremes are interesting or when the response variance varies with the predictors.

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nature Methods 16:451–452.

### Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nature Methods 12:999–1000.

# Analyzing outliers: Robust methods to the rescue

Sat 30-03-2019
Robust regression generates more reliable estimates by detecting and downweighting outliers.

Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.

Nature Methods Points of Significance column: Analyzing outliers: Robust methods to the rescue. (read)

We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.

Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.

### Background reading

Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.

# Two-level factorial experiments

Fri 22-03-2019
To find which experimental factors have an effect, simultaneously examine the difference between the high and low levels of each.

Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactions—desirable statistical qualities that can provide deep insight into a system.

They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.

Nature Methods Points of Significance column: Two-level factorial experiments. (read)

Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).

Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.

### Background reading

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.