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.
DOWNLOAD ALL PHOTOS.
Photos are for public use. Use, modification and distribution of these photos is unrestricted.
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.
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.
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.
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.
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).
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 (PCA) simplifies the complexity in high-dimensional data by reducing its number of dimensions.
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.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.