Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization
Proceedings of SPIE(2009)
摘要
Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in
neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ
hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional
200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression
based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven
structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative
matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF
approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data.
In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of
Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard
NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of
hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established
neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified
by using the mNMF algorithm.
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关键词
spatial correlation,genetics,geography,matrices,somatosensory cortex,nonnegative matrix factorization,three dimensional,gene expression,non negative matrix factorization,spatial data,brain mapping,colorimetry
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