Module Guide: Transcription Factor Networks
This module is intended to teach some basic methods of inferring which
transcription factors (TFs) regulate each gene. Because because TF's
regulate other TFs, these regulatory relationships can be viewed as a
network with feed forward and feed back loops. In addition to the
qualitative networks, an important goal in this field is the
development of quantative regulatory models that make it possible to
predict the effects of perturbing TF activity.
The abilities you should come away from this module with are:
To explain the types of data used in TF network mapping and the basics
of how they are generated.
To explain the basic logic behind regression/coexpression analysis and
differential expression analysis.
To describe the strengths and weaknesses of regression and
differential expression analysis.
To read and understand published papers on computational methods for
mapping TF networks.
We do not have lecture notes for this section. We will rely instead on
the primary literature. There are papers to read before class and we
will have short quizzes to ensure that you read the papers.
Day 0
In Class

This is the last day of the previous module. See the module guide for
this day’s inclass plan.
Before the next class
Day 1
In Class
 Quick quiz on assigned reading
 Applications of TF network mapping
 Introduction to gene expression profiling
 Introduction to regression: least squares, LASSO, etc.
 Discussion of the Inferelator paper
Before the next class

Read and think about this paper,
including the appendix. I was not able to find a paper that was an
ideal introduction. I choose this paper because the math less complex
than most of the others and this method is the basis for later methods
called Limma (for microarray data) and voom (for RNAseq data) that
are very useful in practice. That being said, some of it will be rough
going if you haven't had a statstics class, so just do the best you
can.
Day 2
In Class
 Finish discussion of the Inferelator paper, including regression and shrinkage.
 Discussion of network inference evaluation
 Exercise: Manual network inference from simulated data
 Quick quiz on assigned reading
 Overview of differential expression analysis
 Begin discussion of paper on differential expression analysis
Before the next class
Day 3
In Class

Quick quiz on assigned reading

Discussion of the NetProphet paper
Before the next class
Day 4
In Class

Quick quiz on assigned reading

Discussion of the assigned reading
Before the next class
Day 5
In Class

Quick quiz on assigned reading

Discussion of the assigned reading
Before the next class

See Day 0 of the guide for the next module.