Targeted retrieval of gene expression measurements using regulatory models
published: Oct. 23, 2012, recorded: September 2012, views: 2661
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
Motivation: Large public repositories of gene expression measurements offer the opportunity to
position a new experiment into the context of earlier studies. While previous methods rely on
experimental annotation or global similarity of expression profiles across genes or gene sets, we
compare experiments by measuring similarity based on an unsupervised, data-driven regulatory
model around pre-specified genes of interest. Our experiment retrieval approach is novel in two
conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory
relationships of genes that are relevant to the analyst or come from prior knowledge; (ii)
regulatory model-based similarity measure: related experiments are retrieved based on the
strength of inferred regulatory links between genes.
Results: We learn a model for the regulation of specific genes from a data repository, and exploit it
to construct a similarity metric for an information retrieval task. We use the Fisher kernel, a
rigorous similarity measure that typically has been applied to utilize generative models in
discriminative classifiers. Results on human and plant microarray collections indicate that our
method is able to substantially improve the retrieval of related experiments against standard
methods. Furthermore, it allows the user to interpret biological conditions in terms of changes in
link activity patterns. Our study of the osmotic stress network for A. thaliana shows that the
method successfully identifies relevant relationships around given key genes.
Availability: The code (R) will be available at http://research. ics.tkk.fi/mi/software.shtml at the
time of publication.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: