Order-preserving factor analysis-application to longitudinal gene expression

Arnau Tibau Puig*, Ami Wiesel, Aimee K. Zaas, Chris W. Woods, Geoffrey S. Ginsburg, Gilles Fleury, Alfred O. Hero

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in applications where variables are activated in a specific order, which is unknown. The proposed method is based on a linear model that accounts for each factor's inherent delays and relative order. We present an algorithm to fit the model in an unsupervised manner using techniques from convex and nonconvex optimization that enforce sparsity of the factor scores and consistent precedence-order of the factor loadings. We illustrate the order-preserving factor analysis (OPFA) method for the problem of extracting precedence-ordered factors from a longitudinal (time course) study of gene expression data.

Original languageAmerican English
Article number5771608
Pages (from-to)4447-4458
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number9
StatePublished - Sep 2011

Bibliographical note

Funding Information:
Manuscript received September 21, 2010; revised January 12, 2011, and April 25, 2011; accepted April 26, 2011. Date of publication May 19, 2011; date of current version August 10, 2011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Z. Jane Wang. This work was supported in part by DARPA under the PHD program. The work of G. Fleury and A. Tibau Puig was partially supported by the Digiteo DANSE project.


  • Dictionary learning
  • genomic signal processing
  • misaligned data processing
  • structured factor analysis


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