Pathway Analysis using Logistic Regression
Overview
LRpath performs gene set enrichment
testing, an approach used to test for predefined
biologically-relevant gene sets that contain more significant
genes from an experimental dataset than expected by chance. Given
a high-throughput dataset with continuous significance values
(i.e. p-values), LRpath tests for gene sets (termed concepts) that
have significantly higher significance values (e.g. for
differential expression) than expected at random. LRpath can
identify both concepts that have a few genes with very significant
differential expression and concepts containing many genes with
only moderate differential expression. This user interface
provides a user-friendly implementation of LRpath, and greatly
expands the set of concepts available to test from the original
publication1. Genes are mapped to concepts using their
Entrez Gene IDs.
The pre-defined gene sets (concept
databases) available to test depend on the species, but for
human, mouse, and rat include all those used in ConceptGen.
The use of logistic
regression allows the data to remain on a continuous scale while
maintaining the interpretation of
results in terms of an odds ratio , as
is used with the standard Fisher's
Exact test. Detailed methods are provided here.
When LRpath is run for multiple comparisons in an experiment, it
can be useful to visualize the results in a clustering heatmap
(see example). To cluster your own LRpath results, scroll down to
the bottom of the page to the Clustering section.
Input
LRpath Clustering Analysis
LRpath Cluster Analysis allows you to
integrate your LRpath results from multiple experiments in order
to interactively view and explore the enrichment profiles of a set
of concepts across experiments. It provides a user-friendly method
for filtering, merging, and clustering LRpath results using
several options. The output of this section is a set of files
required to view the hierarchical clustering with each row
corresponding to a concept, and each column corresponding to an
experiment. In order to view and interact with the results of the
cluster analysis you can use the freely available TreeView software.
Simply save the output files from the cluster analysis in one
folder, and then once TreeView is installed, start the program,
and open the saved .cdt file. For more help, see the Java
TreeView Documentation. An example of the resulting clustering is
provided here.
Analysis Form
Reference
Please reference the following
publication when citing LRpath:
1 Sartor MA, Leikauf GD, Medvedovic M. LRpath: A logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics. 2009; 25(2): 211-7.
1 Sartor MA, Leikauf GD, Medvedovic M. LRpath: A logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics. 2009; 25(2): 211-7.
Copyright 2010 The University of Michigan
Developed under the support of the NIH/National Library of Medicine
Grant # R01 LM008106 ("Representing and Acquiring Knowledge of Genome
Regulation") and the National Center for Integrative Biomedical
Informatics (NCIBI), NIH Grant # U54 DA021519 01A1
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