scholarly journals Reconciling differential gene expression data with molecular interaction networks

2013 ◽  
Vol 29 (5) ◽  
pp. 622-629 ◽  
Author(s):  
Christopher L. Poirel ◽  
Ahsanur Rahman ◽  
Richard R. Rodrigues ◽  
Arjun Krishnan ◽  
Jacqueline R. Addesa ◽  
...  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramin Hasibi ◽  
Tom Michoel

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.


2021 ◽  
Author(s):  
Magdalena Navarro ◽  
T Ian Simpson

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 870
Author(s):  
Leonid Bystrykh

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.


2006 ◽  
pp. 223-238 ◽  
Author(s):  
Michael A. Langston ◽  
Lan Lin ◽  
Xinxia Peng ◽  
Nicole E. Baldwin ◽  
Christopher T. Symons ◽  
...  

2006 ◽  
Vol 7 (Suppl 4) ◽  
pp. S7 ◽  
Author(s):  
Lily R Liang ◽  
Shiyong Lu ◽  
Xuena Wang ◽  
Yi Lu ◽  
Vinay Mandal ◽  
...  

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