Inference of Transcription Factor Concentrations and
This page describes examples of how to use the Dynamical Model for
Variational Inference of Protein Concentration and Regulatory
Intensities detailed in . The code is available for download
Current release is 0.11.
Release 0.11 contains a bug fix for file chipVarEstepCMu.m
Release 0.1 is the first release associated with the paper,
containing scripts for recreating the results given.
The toolboxes required to run the code are listed below.
Finally you will also need the NETLAB toolbox in your
Results in the Paper
The main scripts are
which run the code on the metabolic and cell cycle data sets. Also of
interest may be the script
runs the code on artificial data.
invoke the functions
which compute the posterior estimates of the latent variables by
variational lower bound on the likelihood. The functions used are
different as there
is no need to estimate the baseline expression level for cDNA array
data such as the
cell cycle data set. The scripts output a variable model, containing
values which maximise the likelihood, and expectationsB (posterior
the regulatory intensities) and expectationsC, posterior expectations
of the protein
concentration profiles. The function
outputs a variable
expectationsMu containing posterior statistics for the distribution of
The variable expectationsC has four fields: .entropy, a number
contatining the posterior
entropy of the approximating distribution, .c, a matrix with as many
rows as transcription factors and as many columns as time points, .ccT,
a 3D array containing
the correlations among transcription factors at each time point, and
.cAltc, a 3D array
containing correlations among transcription factors at consecutive time
points. By specialising the transcription factor index one can obtain
plots of the transcription
factor protein concentrations with errorbars. An example is show below
for the transcription factor ACE2 during the cell cycle.
concentration profile for the transcription factor ACE2 during the
yeast cell cycle.
Similarly, expectationsB has several fields, but only two of them are
of interest for data analysis, .b and .errorbars (the others are
necessary for the estimation of the variational lower bound). The
expectationsB.b variable is a matrix containing in each
row the regulatory intensity with which a gene is affected by each
transcription factor. Obviously most of the entries will be zero due to
the sparsity of the network structure. The matrix
contains the associated error bars. By specifying to a transcription
factor, we can
obtain a distribution of the significance levels of its regulatory
intensities. An example of this is shown below for the transcription
factor ACE2 during the cell cycle.
Significance of the
regulatory intensities for the transcription factor ACE2 during the
yeast cell cycle. The plot shows the ratio between gene-specific
and the associated noise