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Automatic annotation of spatial expression patterns via sparse Bayesian factor models.

Publication ,  Journal Article
Pruteanu-Malinici, I; Mace, DL; Ohler, U
Published in: PLoS Comput Biol
July 2011

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.

Duke Scholars

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

July 2011

Volume

7

Issue

7

Start / End Page

e1002098

Location

United States

Related Subject Headings

  • Pattern Recognition, Automated
  • Oligonucleotide Array Sequence Analysis
  • Models, Biological
  • Image Processing, Computer-Assisted
  • Humans
  • Gene Expression Regulation, Developmental
  • Gene Expression Profiling
  • Drosophila melanogaster
  • Computational Biology
  • Cluster Analysis
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Pruteanu-Malinici, I., Mace, D. L., & Ohler, U. (2011). Automatic annotation of spatial expression patterns via sparse Bayesian factor models. PLoS Comput Biol, 7(7), e1002098. https://doi.org/10.1371/journal.pcbi.1002098
Pruteanu-Malinici, Iulian, Daniel L. Mace, and Uwe Ohler. “Automatic annotation of spatial expression patterns via sparse Bayesian factor models.PLoS Comput Biol 7, no. 7 (July 2011): e1002098. https://doi.org/10.1371/journal.pcbi.1002098.
Pruteanu-Malinici I, Mace DL, Ohler U. Automatic annotation of spatial expression patterns via sparse Bayesian factor models. PLoS Comput Biol. 2011 Jul;7(7):e1002098.
Pruteanu-Malinici, Iulian, et al. “Automatic annotation of spatial expression patterns via sparse Bayesian factor models.PLoS Comput Biol, vol. 7, no. 7, July 2011, p. e1002098. Pubmed, doi:10.1371/journal.pcbi.1002098.
Pruteanu-Malinici I, Mace DL, Ohler U. Automatic annotation of spatial expression patterns via sparse Bayesian factor models. PLoS Comput Biol. 2011 Jul;7(7):e1002098.

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

July 2011

Volume

7

Issue

7

Start / End Page

e1002098

Location

United States

Related Subject Headings

  • Pattern Recognition, Automated
  • Oligonucleotide Array Sequence Analysis
  • Models, Biological
  • Image Processing, Computer-Assisted
  • Humans
  • Gene Expression Regulation, Developmental
  • Gene Expression Profiling
  • Drosophila melanogaster
  • Computational Biology
  • Cluster Analysis