Cluster Analysis of Gene Expression Data

Important insights into gene function can be gained by gene expression analysis. For example, some genes are turned on (expressed) or turned off (repressed) when there is a change in external conditions or stimuli. The expression of one gene is often regulated by the expression of other genes. A detail analysis of gene expression information will provide an understanding about the inter-networking of different genes and their functional roles. DNA microarray technology allows massively parallel, high throughput genome-wide profiling of gene expression in a single hybridization experiment [Lockhart & Winzeler, 2000]. It has been widely used in numerous studies over a broad range of biological disciplines, such as cancer classification (Armstrong et al., 2002), identification of genes relevant to a certain diagnosis or therapy (Muro et al., 2003), investigation of the mechanism of drug action and cancer prognosis (Kim et al., 2000; Duggan et al., 1999). Due to the large number of genes involved in microarray experiment study and the complexity of biological networks, clustering is an important exploratory technique for gene expression data analysis. In this article, we present a succinct review of some of our work in cluster analysis of gene expression data.

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State-of-the-art of Cluster Analysis of Gene Expression Data

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2008.00113 ◽

2009 ◽

Vol 34 (2) ◽

pp. 113-120 ◽

Cited By ~ 3

Author(s):

Feng YUE

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Gene Expression Data ◽

State Of The Art ◽

Expression Data

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A mixture model-based cluster analysis of DNA microarray gene expression data on Brahman and Brahman composite steers fed high-, medium-, and low-quality diets1

Journal of Animal Science ◽

10.2527/2003.8181900x ◽

2003 ◽

Vol 81 (8) ◽

pp. 1900-1910 ◽

Cited By ~ 40

Author(s):

A. Reverter ◽

K. A. Byrne ◽

H. L. Bruce ◽

Y. H. Wang ◽

B. P. Dalrymple ◽

...

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Mixture Model ◽

Dna Microarray ◽

Gene Expression Data ◽

Microarray Gene Expression Data ◽

Expression Data ◽

Microarray Gene Expression ◽

Model Based ◽

Microarray Gene

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A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

BMC Bioinformatics ◽

10.1186/s12859-021-04447-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ramin Hasibi ◽

Tom Michoel

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Gene Expression Data ◽

Biological Networks ◽

Molecular Interaction ◽

Interaction Networks ◽

Omics Data ◽

Expression Data ◽

Molecular Interaction Networks ◽

Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

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