TF-Cluster: A Pipeline for Identifying Functionally Coordinated Transcription Factors
Author Information
Author(s): Jeff Nie, Ron Stewart, Hang Zhang, James A. Thomson, Fang Ruan, Xiaoqi Cui, Hai Rong Wei
Primary Institution: Morgridge Institute for Research
Hypothesis
Can a new computational pipeline effectively identify functionally coordinated transcription factors involved in biological processes?
Conclusion
TF-Cluster can accurately identify transcription factors that control biological processes from gene expression data.
Supporting Evidence
- TF-Cluster was applied to microarray data from human stem cells and Arabidopsis roots.
- Many resulting TF clusters accurately represent biological processes based on existing literature.
- TF-Cluster identified key transcription factors involved in pluripotency and neural development.
Takeaway
The study created a tool that helps scientists find important proteins that control how genes work together in living things.
Methodology
The study developed a computational pipeline called TF-Cluster that constructs a shared coexpression connectivity matrix and decomposes it using a heuristic algorithm.
Limitations
TF-Cluster may not be useful for datasets with fewer than 30 samples or for biological processes involving few transcription factors.
Digital Object Identifier (DOI)
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