Analysis & Visualization of Large Scale Genomic Data Sets 


Mondays 1:00-3:30pm

Rm. 200 in CIL (genomics building)


Course Info

The goal of this course is to introduce students to computational issues involved in analysis and display of large-scale biological data sets.  Techniques covered will include clustering and machine learning techniques for gene expression microarrays and proteomics data analysis, biological networks and pathways modeling, data integration in genomics, and visualization issues for large-scale data sets.


A short introduction to the field of bioinformatics and the nature of biological data will be provided, no prior knowledge of biology is required. In depth knowledge of computer science is not required, but students must have some understanding of computation (though no need to know programming).


The course will be taught in a mixed lectures and seminar format, and will involve completing a project and a final exam.  The course is open to graduate and advanced undergraduate students from all departments.


Administrative info:

Level:               Graduate and upper level undergraduate

Background:     Some understanding of computation

Format:            Mixed lectures and seminar-style

Grading:           30% presentations

15% quizzes

20% participation (including attendance and participation in discussions)

35% final project (10% project proposal, 25% final project report)

Auditors:          Auditors are welcome, must participate in presentations and discussions (but do not need to do the final project).   


For more admin info, see syllabus):



There is no required book for this class.  Material will be presented in lectures, and readings will be based on current literature.  However, here are a few recommendations for the curious.


If you need to catch up on molecular biology and genetics: 

DOE primer on human genetics

R. Brent. Genomic Biology. Cell 100:169-183, 2000.

L. Hunter. Molecular Biology for Computer Scientists. In Artificial Intelligence and Molecular Biology, L. Hunter editor, 1993, AAAI Press.


Introduction to bioinformatics:

NCBI bioinformatics primer

NCBI primer on microarray analysis



Each presentation should be 20mins, with discussion afterwards.  Presentations should be in power point (or another slides format), and you must e-mail me the power point after your presentation before I can grade it.


A good presentation would include:

-a brief overview of the paper

-outline of major methods and findings, with background of important concepts (e.g. if the paper uses Dynamic Bayesian Networks, give an intro of what they are)

-suggest discussion points for the class: what the paper did well,  what are problems/issues with the approach, what puzzled you

-what should be the future of this method (don’t just retype the “future work” section, we’re looking for your analysis here)


Course Announcements (check here often):


PLEASE sign up for the course on blackboard, or you won’t get any of the course-related emails, which are important.

If you are auditing, sing up for audit.  If you are a postdoc and can’t officially sign up, let me know, and I’ll make sure

to copy you on e-mails.


CHECK THE ASSIGNMENTS BELOW – if you don’t have an assignment and you are AUDITING OR

taking the class for credit, let me know ASAP.


Course schedule:








1 (2/5)


Introduction to the course and bio


Intro to the course and introduction to biology and bioinformatics

Reading: “Systems biology 101-what you need to know




2 (2/12)




Microarray analysis introduction and overview

Required reading:

Hand and Heard “Finding groups in gene expression data” (a very nice review of clustering microarray data, present general concepts and choose 2-3 methods (not hierarchical or kmeans or SOM) to describe in a bit more detail)

Suggested readings:

Lockhart et al  "Genomics, gene expression, and DNA microarrays" (general microarray)

Kaminski N et al "Practical approaches to analyzing results of microarray experiments" (review)

Ehrenreich A.  “DNA microarray technology for the microbiologist: an overview.” (a nice intro to types of microarrays and how microarray experiments work)






Patrick B.


3 (2/19)




Normalization of Microarray Data: Single-Labeled and Dual-Labeled Arrays

Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms

Global survey of organ and organelle protein expression in mouse: Combined proteomic and transcriptomic profiling (bio)


Tony Ambrosini


Andrew F




Chris Bristow

4 (2/26)


Regulation (modules and pathways)


ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.

Inferring transcriptional modules from ChIP-chip, motif and microarray data.

.A Systems Approach to Mapping DNA Damage Response Pathways (bio+method)


Ari S.




Julie Wu

5  (3/5)


Bayesian methods in biology and medicine


Guest Lecture

Required reading:

Inference in Bayesian Networks





6 (3/12)


Data integration


Introduction and overview of data integration and networks prediction

Inferring gene networks from time series microarray data using dynamic Bayesian networks.




Siddhartha B.

7 (3/26)


NSF workshop trip


8  (4/2)


Interactions and Networks


The synthetic genetic interaction spectrum of essential genes  (bio)

Emergent behavior of growing knowledge about molecular interactions

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets (bio + analysis).




Yulia M.


Jeffrey Breunig

9 (4/9)



Probabilistic model of the human protein-protein interaction network

Cluster stability and the use of noise in interpretation of clustering.

Discovery of biological networks from diverse functional genomic data


Yue Niu


Namita Bisaria


Alex O.

10 (4/16)


Interactions, networks, and pathways


Global mapping of pharmacological space

Refinement and expansion of signaling pathways: The osmotic response network in yeast

Herpesviral protein networks and their interaction with the human proteome (bio + analysis)


Maria C.


Adam Stoler


Emily Capra

11 (4/23)


Comparative Genomics & Visualization


Modeling cellular machinery through biological network comparison

Detection of parallel functional modules by comparative analysis of genome sequences

Click and Expander: a system for clustering and visualizing gene expression data


Daniel Barrett


Brendan Collins


Bill Zeller


12 (4/30)


Project proposal presentations – Part II


Final project proposals


All students taking the course for credit



Additional articles of interest


Inferring pathways and networks with a Bayesian framework

Click and Expander: a system for clustering and visualizing gene expression data

Cluster stability and the use of noise in interpretation of clustering. (Interesting clustering algorithm + visualization)

Factorgrams: A tool for visualizing multi-way associations in biological data V Cheung, I Givoni, D Dueck, BJ Frey
University of Toronto Technical Report PSI-2006-44, May 15, 2006.