3. Genes show
clustered
responses
Expression
correlates
between
platforms
4. We want to extrapolate the
expression of regular genes
11000 Genes
2K Arrays
10K 1K
Regular Landmark
Genes Genes
5. We fit a linear
model to each
2K Arrays
1K
regular gene X Landmark
Genes
Predicted Expression = Xβ
Expression Gene 1 = X1β1 +X2β2 +…+X2Kβ2K
Expression Gene 2 = X1β1 +X2β2 +…+X2Kβ2K
…
Expression Gene 10K = X1β1 +X2β2 +…+X2Kβ2K
6. Elastic
Net
mean error
number of variables
glmnet: Lasso and elastic-
net regularized
generalized linear models
http://cran.r-project.org/web/
packages/glmnet/index.html
7. Neural
Networks
regular
genes
hidden
layer
nnet: Feed-forward
Neural Networks and
Multinomial Log-
Linear Models landmark
http://cran.r-project.org/ genes
web/packages/nnet/index.html
9. Building 10451 models
takes a long time…
runtime single
total
per CPU
runtime
model runtime
linear
120 x 3 h 2 min 360 h
regression
elastic
120 x 16 h 11 min 1920 h
net
neural 50 x 0.75 7800 h
45 min
network h ?