Basis-expansion factor models for uncovering transcription factor regulatory network

M. Sanchez-Castillo, Jia Meng, I. M. Tienda-Luna, Yufei Huang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Uncovering transcription factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the factor for modeling large networks, a novel, efficient basis-expansion factor (BE-FaM) model has been proposed, where the loading (regulatory) matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the factor loading matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.

Original languageEnglish
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Number of pages4
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: 5 Aug 20128 Aug 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012


Conference2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/TerritoryUnited States
CityAnn Arbor, MI


  • Bayesian factor model
  • Breast cancer subtyping
  • Microarray data
  • Sparse representation
  • Wavelet decomposition

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