Order-preserving factor discovery from misaligned data

Abstract : We present a factor analysis method that accounts for possible temporal misalignment of the factor loadings across the population of samples. Our main hypothesis is that the data contains a subset of variables with similar but delayed profiles obeying a consistent precedence ordering relationship. Our model is motivated by the difficulty of gene expression analysis across subjects who have common patterns of immune response but show different onset times after a uniform innoculation time of a viral pathogen. The proposed method is based on a linear model with additional degrees of freedom that account for each subject's inherent delays. We present an algorithm to fit this model in a totally unsupervised manner and demonstrate its effectiveness on extracting gene expression factors affecting host response using a flu-virus human challenge study dataset.
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Submitted on : Monday, December 13, 2010 - 11:47:21 AM
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Arnau Tibau Puig, Ami Wiesel, Aimee Zaas, Geoffrey Ginsburg, Gilles Fleury, et al.. Order-preserving factor discovery from misaligned data. 6th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM'10), Oct 2010, Jerusalem, Israel. CD-ROM Proceedings (4 p.). ⟨hal-00545921⟩

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