Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models

Puniyani, Kriti; Xing, Eric P.

doi:10.1007/978-3-642-33783-3_6

“…The ISH images were processed to extract 311 data points for each dataset, as described in Puniyani and Xing (2012). The microarray data was processed using standard microarray processing algorithms.…”

Section: Drosophila Embryonic Datamentioning

confidence: 99%

NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

Puniyani

¹

,

Xing

²

2013

Journal of Computational Biology

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0

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Inference of gene interaction networks from expression data usually focuses on either supervised or unsupervised edge prediction from a single data source. However, in many real world applications, multiple data sources, such as microarray and ISH (in situ hybridization) measurements of mRNA abundances, are available to offer multiview information about the same set of genes. We propose ISH to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same precision (i.e., inverse covariance) matrix, and we present an efficient algorithm to estimate such a model in the high-dimensional scenario. Results are reported on synthetic data, where NP-MuScL outperforms baseline algorithms significantly, even in the presence of noisy data sources. Experiments are also run on two real-world scenarios: two yeast microarray datasets and three Drosophila embryonic gene expression datasets, where NP-MuScL predicts a higher number of known gene interactions than existing techniques.

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“…This includes the popular BEST [8] and SPEX 2 methods [14]. These techniques can identify groups of similar patterns from which one can, e.g., infer prospective gene interaction networks [15]. However, they do not provide stage annotation, as we require.…”

Section: Introductionmentioning

confidence: 99%

Drosophila Embryo Stage Annotation Using Label Propagation

Kazmar¹,

Kvon

²

,

Stark

³

et al. 2013

2013 IEEE International Conference on Computer Vision

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In this work we propose a system for automatic classification of Drosophila embryos into developmental stages. While the system is designed to solve an actual problem in biological research, we believe that the principle underlying it is interesting not only for biologists, but also for researchers in computer vision. The main idea is to combine two orthogonal sources of information: one is a classifier trained on strongly invariant features, which makes it applicable to images of very different conditions, but also leads to rather noisy predictions. The other is a label propagation step based on a more powerful similarity measure that however is only consistent within specific subsets of the data at a time.In our biological setup, the information sources are the shape and the staining patterns of embryo images. We show experimentally that while neither of the methods can be used by itself to achieve satisfactory results, their combination achieves prediction quality comparable to human performance.

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NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

Puniyani

¹

,

Xing

²

2013

Lecture Notes in Computer Science

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1

0

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Inference of gene interaction networks from expression data usually focuses on either supervised or unsupervised edge prediction from a single data source. However, in many real world applications, multiple data sources, such as microarray and ISH (in situ hybridization) measurements of mRNA abundances, are available to offer multiview information about the same set of genes. We propose ISH to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same precision (i.e., inverse covariance) matrix, and we present an efficient algorithm to estimate such a model in the high-dimensional scenario. Results are reported on synthetic data, where NP-MuScL outperforms baseline algorithms significantly, even in the presence of noisy data sources. Experiments are also run on two real-world scenarios: two yeast microarray datasets and three Drosophila embryonic gene expression datasets, where NP-MuScL predicts a higher number of known gene interactions than existing techniques.

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Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models

Cited by 4 publications

References 29 publications

NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

Drosophila Embryo Stage Annotation Using Label Propagation

NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources

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