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Automatic annotation of spatial expression patterns via sparse Bayesian factor models.
Abstract
Advances in reporters for gene expression have made it possible to document and quantify
expression patterns in 2D-4D. In contrast to microarrays, which provide data for many
genes but averaged and/or at low resolution, images reveal the high spatial dynamics
of gene expression. Developing computational methods to compare, annotate, and model
gene expression based on images is imperative, considering that available data are
rapidly increasing. We have developed a sparse Bayesian factor analysis model in which
the observed expression diversity of among a large set of high-dimensional images
is modeled by a small number of hidden common factors. We apply this approach on embryonic
expression patterns from a Drosophila RNA in situ image database, and show that the
automatically inferred factors provide for a meaningful decomposition and represent
common co-regulation or biological functions. The low-dimensional set of factor mixing
weights is further used as features by a classifier to annotate expression patterns
with functional categories. On human-curated annotations, our sparse approach reaches
similar or better classification of expression patterns at different developmental
stages, when compared to other automatic image annotation methods using thousands
of hard-to-interpret features. Our study therefore outlines a general framework for
large microscopy data sets, in which both the generative model itself, as well as
its application for analysis tasks such as automated annotation, can provide insight
into biological questions.
Type
Journal articleSubject
AlgorithmsAnimals
Area Under Curve
Artificial Intelligence
Bayes Theorem
Cluster Analysis
Computational Biology
Drosophila melanogaster
Gene Expression Profiling
Gene Expression Regulation, Developmental
Humans
Image Processing, Computer-Assisted
Models, Biological
Oligonucleotide Array Sequence Analysis
Pattern Recognition, Automated
Permalink
https://hdl.handle.net/10161/15356Published Version (Please cite this version)
10.1371/journal.pcbi.1002098Publication Info
Pruteanu-Malinici, Iulian; Mace, Daniel L; & Ohler, Uwe (2011). Automatic annotation of spatial expression patterns via sparse Bayesian factor models.
PLoS Comput Biol, 7(7). pp. e1002098. 10.1371/journal.pcbi.1002098. Retrieved from https://hdl.handle.net/10161/15356.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Uwe Ohler
Adjunct Associate Professor in the Department of Biostatistics & Bioinformatics
Computational Biology of Gene Regulation Sequence Analysis Image Expression Analysis
Applied Machine Learning

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