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Bioinformatics. 2009 Oct 1;25(19):2581-7. doi: 10.1093/bioinformatics/btp437. Epub 2009 Jul 16.

Reconstructing spatiotemporal gene expression data from partial observations.

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  • 1Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu



Developmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root; however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures.


Given these partial observations, we present a novel method for reconstructing integrated high-resolution spatiotemporal data. Our method is based on a new iterative algorithm for finding approximate roots to systems of bilinear equations.


Source code for solving bilinear equations is available at http://math.berkeley.edu/ approximately dustin/bilinear/. Visualizations of reconstructed patterns on a schematic Arabidopsis root are available at http://www.arexdb.org/.

[PubMed - indexed for MEDLINE]
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