MONSTER-package: Modeling Network State Transitions from Expression and...

Description Details Author(s) See Also

Description

MONSTER indentifies transcription factor drivers of state change from expression data.

Details

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MONSTER takes in sequence motif data linking transcription factors (TFs) to genes and gene expression from two conditions. The goal is generate bipartite networks from the gene expression data which quantify evidence of the regulatory roles of each of the TFs to each of the genes. Next, critical TFs are identified by computing a transition matrix, which maps the gene regulatory network in the first state to the gene regulatory network in the second state.

Author(s)

"Dan Schlauch <dschlauch@fas.harvard.edu>"

Maintainer: Dan Schlauch <dschlauch@fas.harvard.edu>

See Also

monster


QuackenbushLab/MONSTER documentation built on Oct. 22, 2020, 8:07 a.m.