Jingchun Zhu

November 18, 2006

We developed
ExpressionNet, a program that uses Bayesian network learning algorithms to
explore relationships among random variables to generate network models that
best explains external data. The
software has been used to study the transcriptional response in budding yeast. A combination of gene expression
clusters, genetic information and experimental conditions was used to derive
network that models gene expression response to a seriese of environmental
triggers. Detail of the study has
been submitted for publication.

The program has
been applied to **model the
transcriptional response to nitric oxide in Saccharomyces
cerevisiae, **which will soon be published.
The following is the supplemental data for the manuscript.

** **

Jingchun Zhu, Ashwini Jambhekar, Aaron Sarver and Joseph
DeRisi

The supplemental data are organized by the three iterations
of modeling. Network models (files
with extension .bn or .matrix) can be viewed using ExpressionNet, a free
program developed for Bayesian network learning, which is available at http://expressionnet.sourceforge.net. Microarray clustering results (files
with extension .cdt or .gtr) can be viewed as a pair using JavaTreeView, a free
program available at http://jtreeview.sourceforge.net.

The datasets include the derived network models, the
learning datasets, gene cluster memberships, and complete microarray datasets
obtained in this study.

- Microarray
dataset:

1st_model_microarraydata.cdt;
1st_model_microarraydata.gtr

- Gene cluster
membership:

1st_model_gene_cluster_membership.xls

- Node
definitions and node state values:

1st_model_node_definitions.xls

- Learning
dataset:

1st_model_learning_dataset.txt

- Derived
model:

Model: 1st_model_average.matrix and 1st_model.bn

Networks
used to construct the model: in the “1st model
high-scoring network collection” folder

High-scoring
criteria: top 15 percentile using both priors

- Manually
pre-constructed network:

** **

** **

- Microarray
dataset:

2nd_model_microarraydata.cdt;
2nd_model_microarraydata.gtr

- Gene
cluster membership:

2nd_model_gene_cluster_membership.xls

- Node
definitions and node state values:

2nd_model_node_definitions.xls

- Learning
dataset:

2nd_model_learning_dataset.txt

- Derived
model:

Model: 2nd_model_average.matrix and 2nd_model.bn

Networks
used to construct the model: “2nd model
high-scoring network collection” folder

High-scoring
criteria: top 15 percentile using two both priors

- Microarray
dataset:

3rd_model_microarraydata.cdt;
3rd_model_microarraydata.gtr

- Gene
cluster membership:

3rd_model_gene_cluster_membership.xls

- Node
definitions and node state values:

3rd_model_node_definitions.xls

- Learning
dataset:

3rd_model_learning_dataset.txt

- Derived
model:

Model: 3rd_model_average.matrix and 3rd_model.bn

Networks
used to construct the model: “3rd model high-scoring
network collection” folder

High-scoring
criteria: the top 25 percentile using both priors

- Nitric
oxide perturbation (E1) experiment: E1.txt
- Glucose
to galactose I (E2) experiment: E2.txt
- Glucose
to galactose II (E3) experiment: E3.txt
- Raffinose
to galactose (E4) experiment: E4.txt
- Diauxic
shift experiment: DiaucxicShift.txt
(from SGD)
- H
_{2}0_{2}menadione treatment experiment: H_{2}0_{2}Menadione.txt (from SGD)