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Without an after or a when.Papercut feat. Maiken Sundbycan you hear the rain?more quotes

genomics: complicated


EMBO Practical Course: Bioinformatics and Genome Analysis, 5–17 June 2017.


data visualization + art

Genes that make us sick

It is said that for money you can have everything, but you cannot. You can buy food, but not appetite; medicine, but not health; knowledge, but not wisdom; glitter, but not beauty; fun, but not joy; acquaintances, but not friends; servants, but not faithfulness; leisure, but not peace. You can have the husk of everything for money, but not the kernel.
— Arne Garborg

I have recently had the opportunity to contribute to The Objects that Power the Global Economy, a book by Quartz.

The book is about objects that have impact on our world and our lives. "Each chapter of this book examines an object that is driving radical change in the global economy: how we communicate, what we eat, the way we spend our money. The stories are told through global reporting, original photography and illustration by award-winning artists, contributions from business visionaries, data visualization, and interactive features." (Quartz).

where disease hides in the genome

My illustration is of the human genome with a focus on the genes that have been implicated in disease.

We have about 30,000 genes and about half of these play some role in disease.

You can peruse what we know about the connection between genetics and illness at the Online Mendelean Inheritance of Man database. For example, a cursory search for "cancer" results in over 3,500 entries.

It's important to realize that these aren't genes that cause disease—its misregulation and mutations in them that are associated with disease (causality is complicated).

the visualization

The illustration shows the genome as a single line, wound in an Archimedean spiral. Chromosomes 1–22 are shown binned into about 10,000 regions along the spiral. Regions that have genes associated with disease are marked with dots—the size of the dot shows how many such genes are found. Each region corresponds to about 286,000 bases.

We see that in about 73% of the 286 kb regions, there are no genes. In about 18% we see a single gene and in roughly 10% two genes or more.

  regions  genes
    7,321  0
    1,812  1
      556  2
      221  3
       85  4
       93  5+

Winding the genome up in a spiral creates a compact representation. Squishing a line onto a page can be tricky.

Luckily, space filling curves like the Hilbert curve are very efficient at doing this. I've previously shown the genome along a Hilbert curve for a Scientific American Graphic Science page.

the artwork

I show several versions of the illustrations below. In the book, the image is printed on a black background.


 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The human genome is shown as a spiral. Starting at the top with chromosome 1 and proceeding clockwise, each of the 10,087 dots corresponds to 286,000 bases, colored by chromosome. Within each dot, the number of genes in that region implicated in disease is shown by the size of the black circle. Chromosomes X and Y are not shown. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The human genome is shown as a spiral. Starting at the top with chromosome 1 and proceeding clockwise, each of the 10,087 dots corresponds to 286,000 bases, colored by chromosome. Within each dot, the number of genes in that region implicated in disease is shown by the size of the black circle. Chromosomes X and Y are not shown. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The human genome is shown as a spiral, starting at the top with chromosome 1 and proceeding clockwise. The spiral is formed by 10,087 segments that correspond to 286,000 bases each. Segments that contain genes implicated in disease are indicated by dots, sized by the number of genes. Chromosomes X and Y are not shown. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The human genome is shown as a spiral, starting at the top with chromosome 1 and proceeding clockwise. The spiral is formed by 10,087 segments that correspond to 286,000 bases each. Segments that contain genes implicated in disease are indicated by dots, sized by the number of genes. Chromosomes X and Y are not shown. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The human genome is shown as a spiral, starting at the top with chromosome 1 and proceeding clockwise. The spiral is formed by 10,087 segments that correspond to 286,000 bases each. Segments that contain genes implicated in disease are indicated by dots, sized by the number of genes. Chromosomes X and Y are not shown. (zoom)
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news + thoughts

Snowflake simulation

Tue 14-11-2017
Symmetric, beautiful and unique.

Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A few of the beautiful snowflakes generated by the Gravner-Griffeath model. (explore)

Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.

Genes that make us sick

Thu 02-11-2017
Where disease hides in the genome.

My illustration of the location of genes in the human genome that are implicated in disease appears in The Objects that Power the Global Economy, a book by Quartz.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The location of genes implicated in disease in the human genome, shown here as a spiral. (more...)

Ensemble methods: Bagging and random forests

Mon 16-10-2017
Many heads are better than one.

We introduce two common ensemble methods: bagging and random forests. Both of these methods repeat a statistical analysis on a bootstrap sample to improve the accuracy of the predictor. Our column shows these methods as applied to Classification and Regression Trees.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Ensemble methods: Bagging and random forests. (read)

For example, we can sample the space of values more finely when using bagging with regression trees because each sample has potentially different boundaries at which the tree splits.

Random forests generate a large number of trees by not only generating bootstrap samples but also randomly choosing which predictor variables are considered at each split in the tree.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Ensemble methods: bagging and random forests. Nature Methods 14:933–934.

Background reading

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

...more about the Points of Significance column

Classification and regression trees

Mon 16-10-2017
Decision trees are a powerful but simple prediction method.

Decision trees classify data by splitting it along the predictor axes into partitions with homogeneous values of the dependent variable. Unlike logistic or linear regression, CART does not develop a prediction equation. Instead, data are predicted by a series of binary decisions based on the boundaries of the splits. Decision trees are very effective and the resulting rules are readily interpreted.

Trees can be built using different metrics that measure how well the splits divide up the data classes: Gini index, entropy or misclassification error.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Classification and decision trees. (read)

When the predictor variable is quantitative and not categorical, regression trees are used. Here, the data are still split but now the predictor variable is estimated by the average within the split boundaries. Tree growth can be controlled using the complexity parameter, a measure of the relative improvement of each new split.

Individual trees can be very sensitive to minor changes in the data and even better prediction can be achieved by exploiting this variability. Using ensemble methods, we can grow multiple trees from the same data.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

Background reading

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.

Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.

...more about the Points of Significance column

Personal Oncogenomics Program 5 Year Anniversary Art

Wed 26-07-2017

The artwork was created in collaboration with my colleagues at the Genome Sciences Center to celebrate the 5 year anniversary of the Personalized Oncogenomics Program (POG).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
5 Years of Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. (left) Cases ordered chronologically by case number. (right) Cases grouped by diagnosis (tissue type) and then by similarity within group.

The Personal Oncogenomics Program (POG) is a collaborative research study including many BC Cancer Agency oncologists, pathologists and other clinicians along with Canada's Michael Smith Genome Sciences Centre with support from BC Cancer Foundation.

The aim of the program is to sequence, analyze and compare the genome of each patient's cancer—the entire DNA and RNA inside tumor cells— in order to understand what is enabling it to identify less toxic and more effective treatment options.

Principal component analysis

Thu 06-07-2017
PCA helps you interpret your data, but it will not always find the important patterns.

Principal component analysis (PCA) simplifies the complexity in high-dimensional data by reducing its number of dimensions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Principal component analysis. (read)

To retain trend and patterns in the reduced representation, PCA finds linear combinations of canonical dimensions that maximize the variance of the projection of the data.

PCA is helpful in visualizing high-dimensional data and scatter plots based on 2-dimensional PCA can reveal clusters.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Principal component analysis. Nature Methods 14:641–642.

Background reading

Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.

...more about the Points of Significance column