Description Details Author(s) References Examples
This package contains functions used to determine the gene expression modules that represent the drivers of Kauffman's attractor landscape.
Package: | attract |
Type: | Package |
Version: | 1.33.2 |
Date: | 2018-06-29 |
License: | |
LazyLoad: | yes |
The method can be summarized in the following key steps:
(1) Determine core KEGG or reactome pathways that discriminate the most strongly between celltypes or experimental groups of interest (see findAttractors)
).
(2) Find the different synexpression groups that are present within a core attractor pathway (see findSynexprs
).
(3) Find sets of genes that show highly similar profiles to the synexpression groups within an attractor pathway module (see findCorrPartners
).
(4) Test for functional enrichment for each of the synexpression groups to detect any potentially shared biological themes (see calcFuncSynexprs
).
Jessica Mar <jess@jimmy.harvard.edu>
Kauffman S. 2004. A proposal for using the ensemble approach to understand genetic regulatory networks. J Theor Biol. 230:581. Mar JC, Wells CA, Quackenbush J. 2010. Identifying Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape. To Appear. M\"uller F et al. 2008. Regulatory networks define phenotypic classes of human stem cell lines. Nature. 455(7211): 401. Mar JC, Wells CA, Quackenbush J. 2010. Defining an Informativeness Metric for Clustering Gene Expression Data. To Appear.
1 2 3 4 5 6 7 8 9 | ## Not run:
data(subset.loring.eset)
attractor.states <- findAttractors(subset.loring.eset, "celltype", nperm=10, annotation="illuminaHumanv1.db")
remove.these.genes <- removeFlatGenes(subset.loring.eset, "celltype", contrasts=NULL, limma.cutoff=0.05)
mapk.syn <- findSynexprs("04010", attractor.states, remove.these.genes)
mapk.cor <- findCorrPartners(mapk.syn, subset.loring.eset, remove.these.genes)
mapk.func <- calcFuncSynexprs(mapk.syn, attractor.states, "CC", annotation="illuminaHumanv1.db")
## End(Not run)
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